One of the most active research areas in field of robotics is control of robot manipulator because this system has highly nonlinear dynamic parameters and most of dynamic parameters are unknown so design an acceptable controller is the main goal in this work. To solve this challenge position new estimation sliding mode fuzzy controller is introduced and applied to robot manipulator. This controller can solve to most important challenge in classical sliding mode controller in presence of highly uncertainty, namely; chattering phenomenon based on fuzzy estimator and online tuning and equivalent nonlinear dynamic based on estimation. Proposed method has acceptable performance in presence of uncertainty (e.g., overshoot=0%, rise time=0.8 s, steady state error = 1e-9 and RMS error=0.0001632).
Design Auto Adjust Sliding Surface Slope: Applied to Robot ManipulatorWaqas Tariq
The main target in this paper is to present the nonlinear methods in order to control the robot manipulators and also the related results. Also the important role of sliding surface slope in sliding mode fuzzy control of robot manipulator should be considered. Sliding mode controller (SMC) is a significant nonlinear controller in certain and uncertain dynamic parameters systems. To solve the chattering phenomenon, this paper complicated two methods to each other; boundary layer method and applied fuzzy logic in sliding mode methodology. To remove the chattering sliding surface slope also played important role so this paper focused on the auto tuning this important coefficient to have the best results by applied mathematical model free methodology. Auto tuning methodology has acceptable performance in presence of uncertainty (e.g., overshoot=0%, rise time=0.8 s, steady state error = 1e-9 and RMS error=0.0001632).
Design Novel Nonlinear Controller Applied to Robot Manipulator: Design New Fe...Waqas Tariq
This document describes a novel adaptive feedback linearization fuzzy controller for robot manipulators. It begins by discussing limitations of traditional feedback linearization controllers, such as sensitivity to parameter uncertainty. It then proposes designing a feedback linearization fuzzy controller to address this issue. The key steps are: 1) designing the fuzzy controller, including fuzzifying inputs/outputs and developing a rule base, 2) developing an adaptive feedback linearization fuzzy controller by adding an adaptive law to tune fuzzy rule parameters online and improve disturbance rejection. The goal is to develop a robust position controller for robot manipulators that maintains acceptable performance despite nonlinearities and uncertainty.
In this research, a model free sliding mode fuzzy adaptive inverse dynamic fuzzy controller (SMFIDFC) is designed for a robot manipulator to rich the best performance. Inverse dynamic controller is considered because of its high performance in certain system. Fuzzy methodology has been included in inverse dynamic to keep away from design nonlinear controller based on dynamic model. Sliding mode fuzzy adaptive methodology is applied to model free controller to have better result in presence of structure and unstructured uncertainties. Besides, this control method can be applied to non-linear systems easily. Today, strong mathematical tools are used in new control methodologies to design adaptive nonlinear controller with satisfactory output results (e.g., minimum error, good trajectory, disturbance rejection).
Methodology of Mathematical error-Based Tuning Sliding Mode ControllerCSCJournals
Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of the most important challenging works. This paper focuses on the design of a chattering free mathematical error-based tuning sliding mode controller (MTSMC) for highly nonlinear dynamic robot manipulator, in presence of uncertainties. In order to provide high performance nonlinear methodology, sliding mode controller is selected. Pure sliding mode controller can be used to control of partly known nonlinear dynamic parameters of robot manipulator. Conversely, pure sliding mode controller is used in many applications; it has an important drawback namely; chattering phenomenon which it can causes some problems such as saturation and heat the mechanical parts of robot manipulators or drivers. In order to reduce the chattering this research is used the switching function in presence of mathematical error-based method instead of switching function method in pure sliding mode controller. The results demonstrate that the sliding mode controller with switching function is a model-based controllers which works well in certain and partly uncertain system. Pure sliding mode controller has difficulty in handling unstructured model uncertainties. To solve this problem applied mathematical model-free tuning method to sliding mode controller for adjusting the sliding surface gain (ë ). Since the sliding surface gain (ë) is adjusted by mathematical model free-based tuning method, it is nonlinear and continuous. In this research new ë is obtained by the previous ë multiple sliding surface slopes updating factor (á). Chattering free mathematical error-based tuning sliding mode controller is stable controller which eliminates the chattering phenomenon without to use the boundary layer saturation function. Lyapunov stability is proved in mathematical error-based tuning sliding mode controller with switching (sign) function. This controller has acceptable performance in presence of uncertainty (e.g., overshoot=0%, rise time=0.8 second, steady state error = 1e-9 and RMS error=1.8e-12).
Design Novel Lookup Table Changed Auto Tuning FSMC: Applied to Robot ManipulatorCSCJournals
Refer to this paper, design lookup table changed adaptive fuzzy sliding mode controller with minimum rule base and good response in presence of structure and unstructured uncertainty is presented. However sliding mode controller is one of the robust nonlinear controllers but when this controller is applied to robot manipulator with highly nonlinear and uncertain dynamic function; caused to be challenged in control. Sliding mode controller in presence of uncertainty has two most important drawbacks; chattering and nonlinear equivalent part which proposed method is solved these challenges with look up table change methodology. This method is based on self tuning methodology therefore artificial intelligence (e.g., fuzzy logic method) is played important role to design proposed method. This controller has acceptable performance in presence of uncertainty (e.g., overshoot=0%, rise time=0.8 s, steady state error = 1e-9 and RMS error=0.00017).
Refer to the research, design a novel SISO adaptive fuzzy sliding algorithm inverse dynamic like method (NAIDLC) and application to robot manipulator has proposed in order to design high performance nonlinear controller in the presence of uncertainties. Regarding to the positive points in inverse dynamic controller, fuzzy logic controller and self tuning fuzzy sliding method, the output has improved. The main objective in this research is analyses and design of the adaptive robust controller based on artificial intelligence and nonlinear control. Robot manipulator is nonlinear, time variant and a number of parameters are uncertain, so design the best controller for this plant is the main target. Although inverse dynamic controller have acceptable performance with known dynamic parameters but regarding to uncertainty, this controller\'s output has fairly fluctuations. In order to solve this problem this research is focoused on two methodology the first one is design a fuzzy inference system as a estimate nonlinear part of main controller but this method caused to high computation load in fuzzy rule base and the second method is focused on design novel adaptive method to reduce the computation in fuzzy algorithm.
Evaluation Performance of 2nd Order Nonlinear System: Baseline Control Tunabl...Waqas Tariq
Design a nonlinear controller for second order nonlinear uncertain dynamical systems (e.g., internal combustion engine) is one of the most important challenging works. This paper focuses on the comparative study between two important nonlinear controllers namely; computed torque controller (CTC) and sliding mode controller (SMC) and applied to internal combustion (IC) engine in presence of uncertainties. In order to provide high performance nonlinear methodology, sliding mode controller and computed torque controller are selected. Pure SMC and CTC can be used to control of partly known nonlinear dynamic parameters of IC engine. Pure sliding mode controller and computed torque controller have difficulty in handling unstructured model uncertainties. To solve this problem applied linear error-based tuning method to sliding mode controller and computed torque controller for adjusting the sliding surface gain (ë ) and linear inner loop gain (K). Since the sliding surface gain (ë) and linear inner loop gain (K) are adjusted by linear error-based tuning method. In this research new ë and new K are obtained by the previous ë and K multiple gains updating factor(á). The results demonstrate that the error-based linear SMC and CTC are model-based controllers which works well in certain and uncertain system. These controllers have acceptable performance in presence of uncertainty.
Evolutionary Design of Mathematical tunable FPGA Based MIMO Fuzzy Estimator S...Waqas Tariq
In this research, a Multi Input Multi Output (MIMO) position Field Programmable Gate Array (FPGA)-based fuzzy estimator sliding mode control (SMC) design with the estimation laws derived in Lyapunov sense and application to robotic manipulator has proposed in order to design high performance nonlinear controller in the presence of uncertainties. Regarding to the positive points in sliding mode controller, fuzzy inference methodology and Lyapunov based method, the controllers output has improved. The main target in this research is analyses and design of the position MIMO artificial Lyapunov FPGA-based controller for robot manipulator in order to solve uncertainty, external disturbance, nonlinear equivalent part, chattering phenomenon, time to market and controller size using FPGA. Robot manipulators are nonlinear, time variant and a number of parameters are uncertain therefore design robust and stable controller based on Lyapunov based is discussed in this research. Studies about classical sliding mode controller (SMC) show that: although this controller has acceptable performance with known dynamic parameters such as stability and robustness but there are two important disadvantages as below: chattering phenomenon and mathematical nonlinear dynamic equivalent controller part. The first challenge; nonlinear dynamic part; is applied by inference estimator method in sliding mode controller in order to solve the nonlinear problems in classical sliding mode controller. And the second challenge; chattering phenomenon; is removed by linear method. Asymptotic stability of the closed loop system is also proved in the sense of Lyapunov. In the last part it can find the implementation of MIMO fuzzy estimator sliding mode controller on FPGA; FPGA-based fuzzy estimator sliding mode controller has many advantages such as high speed, low cost, short time to market and small device size. One of the most important drawbacks is limited capacity of available cells which this research focuses to solve this challenge. FPGA can be used to design a controller in a single chip Integrated Circuit (IC). In this research the SMC is designed using Very High Description Language (VHDL) for implementation on FPGA device (XA3S1600E-Spartan-3E), with minimum chattering.
Design Auto Adjust Sliding Surface Slope: Applied to Robot ManipulatorWaqas Tariq
The main target in this paper is to present the nonlinear methods in order to control the robot manipulators and also the related results. Also the important role of sliding surface slope in sliding mode fuzzy control of robot manipulator should be considered. Sliding mode controller (SMC) is a significant nonlinear controller in certain and uncertain dynamic parameters systems. To solve the chattering phenomenon, this paper complicated two methods to each other; boundary layer method and applied fuzzy logic in sliding mode methodology. To remove the chattering sliding surface slope also played important role so this paper focused on the auto tuning this important coefficient to have the best results by applied mathematical model free methodology. Auto tuning methodology has acceptable performance in presence of uncertainty (e.g., overshoot=0%, rise time=0.8 s, steady state error = 1e-9 and RMS error=0.0001632).
Design Novel Nonlinear Controller Applied to Robot Manipulator: Design New Fe...Waqas Tariq
This document describes a novel adaptive feedback linearization fuzzy controller for robot manipulators. It begins by discussing limitations of traditional feedback linearization controllers, such as sensitivity to parameter uncertainty. It then proposes designing a feedback linearization fuzzy controller to address this issue. The key steps are: 1) designing the fuzzy controller, including fuzzifying inputs/outputs and developing a rule base, 2) developing an adaptive feedback linearization fuzzy controller by adding an adaptive law to tune fuzzy rule parameters online and improve disturbance rejection. The goal is to develop a robust position controller for robot manipulators that maintains acceptable performance despite nonlinearities and uncertainty.
In this research, a model free sliding mode fuzzy adaptive inverse dynamic fuzzy controller (SMFIDFC) is designed for a robot manipulator to rich the best performance. Inverse dynamic controller is considered because of its high performance in certain system. Fuzzy methodology has been included in inverse dynamic to keep away from design nonlinear controller based on dynamic model. Sliding mode fuzzy adaptive methodology is applied to model free controller to have better result in presence of structure and unstructured uncertainties. Besides, this control method can be applied to non-linear systems easily. Today, strong mathematical tools are used in new control methodologies to design adaptive nonlinear controller with satisfactory output results (e.g., minimum error, good trajectory, disturbance rejection).
Methodology of Mathematical error-Based Tuning Sliding Mode ControllerCSCJournals
Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of the most important challenging works. This paper focuses on the design of a chattering free mathematical error-based tuning sliding mode controller (MTSMC) for highly nonlinear dynamic robot manipulator, in presence of uncertainties. In order to provide high performance nonlinear methodology, sliding mode controller is selected. Pure sliding mode controller can be used to control of partly known nonlinear dynamic parameters of robot manipulator. Conversely, pure sliding mode controller is used in many applications; it has an important drawback namely; chattering phenomenon which it can causes some problems such as saturation and heat the mechanical parts of robot manipulators or drivers. In order to reduce the chattering this research is used the switching function in presence of mathematical error-based method instead of switching function method in pure sliding mode controller. The results demonstrate that the sliding mode controller with switching function is a model-based controllers which works well in certain and partly uncertain system. Pure sliding mode controller has difficulty in handling unstructured model uncertainties. To solve this problem applied mathematical model-free tuning method to sliding mode controller for adjusting the sliding surface gain (ë ). Since the sliding surface gain (ë) is adjusted by mathematical model free-based tuning method, it is nonlinear and continuous. In this research new ë is obtained by the previous ë multiple sliding surface slopes updating factor (á). Chattering free mathematical error-based tuning sliding mode controller is stable controller which eliminates the chattering phenomenon without to use the boundary layer saturation function. Lyapunov stability is proved in mathematical error-based tuning sliding mode controller with switching (sign) function. This controller has acceptable performance in presence of uncertainty (e.g., overshoot=0%, rise time=0.8 second, steady state error = 1e-9 and RMS error=1.8e-12).
Design Novel Lookup Table Changed Auto Tuning FSMC: Applied to Robot ManipulatorCSCJournals
Refer to this paper, design lookup table changed adaptive fuzzy sliding mode controller with minimum rule base and good response in presence of structure and unstructured uncertainty is presented. However sliding mode controller is one of the robust nonlinear controllers but when this controller is applied to robot manipulator with highly nonlinear and uncertain dynamic function; caused to be challenged in control. Sliding mode controller in presence of uncertainty has two most important drawbacks; chattering and nonlinear equivalent part which proposed method is solved these challenges with look up table change methodology. This method is based on self tuning methodology therefore artificial intelligence (e.g., fuzzy logic method) is played important role to design proposed method. This controller has acceptable performance in presence of uncertainty (e.g., overshoot=0%, rise time=0.8 s, steady state error = 1e-9 and RMS error=0.00017).
Refer to the research, design a novel SISO adaptive fuzzy sliding algorithm inverse dynamic like method (NAIDLC) and application to robot manipulator has proposed in order to design high performance nonlinear controller in the presence of uncertainties. Regarding to the positive points in inverse dynamic controller, fuzzy logic controller and self tuning fuzzy sliding method, the output has improved. The main objective in this research is analyses and design of the adaptive robust controller based on artificial intelligence and nonlinear control. Robot manipulator is nonlinear, time variant and a number of parameters are uncertain, so design the best controller for this plant is the main target. Although inverse dynamic controller have acceptable performance with known dynamic parameters but regarding to uncertainty, this controller\'s output has fairly fluctuations. In order to solve this problem this research is focoused on two methodology the first one is design a fuzzy inference system as a estimate nonlinear part of main controller but this method caused to high computation load in fuzzy rule base and the second method is focused on design novel adaptive method to reduce the computation in fuzzy algorithm.
Evaluation Performance of 2nd Order Nonlinear System: Baseline Control Tunabl...Waqas Tariq
Design a nonlinear controller for second order nonlinear uncertain dynamical systems (e.g., internal combustion engine) is one of the most important challenging works. This paper focuses on the comparative study between two important nonlinear controllers namely; computed torque controller (CTC) and sliding mode controller (SMC) and applied to internal combustion (IC) engine in presence of uncertainties. In order to provide high performance nonlinear methodology, sliding mode controller and computed torque controller are selected. Pure SMC and CTC can be used to control of partly known nonlinear dynamic parameters of IC engine. Pure sliding mode controller and computed torque controller have difficulty in handling unstructured model uncertainties. To solve this problem applied linear error-based tuning method to sliding mode controller and computed torque controller for adjusting the sliding surface gain (ë ) and linear inner loop gain (K). Since the sliding surface gain (ë) and linear inner loop gain (K) are adjusted by linear error-based tuning method. In this research new ë and new K are obtained by the previous ë and K multiple gains updating factor(á). The results demonstrate that the error-based linear SMC and CTC are model-based controllers which works well in certain and uncertain system. These controllers have acceptable performance in presence of uncertainty.
Evolutionary Design of Mathematical tunable FPGA Based MIMO Fuzzy Estimator S...Waqas Tariq
In this research, a Multi Input Multi Output (MIMO) position Field Programmable Gate Array (FPGA)-based fuzzy estimator sliding mode control (SMC) design with the estimation laws derived in Lyapunov sense and application to robotic manipulator has proposed in order to design high performance nonlinear controller in the presence of uncertainties. Regarding to the positive points in sliding mode controller, fuzzy inference methodology and Lyapunov based method, the controllers output has improved. The main target in this research is analyses and design of the position MIMO artificial Lyapunov FPGA-based controller for robot manipulator in order to solve uncertainty, external disturbance, nonlinear equivalent part, chattering phenomenon, time to market and controller size using FPGA. Robot manipulators are nonlinear, time variant and a number of parameters are uncertain therefore design robust and stable controller based on Lyapunov based is discussed in this research. Studies about classical sliding mode controller (SMC) show that: although this controller has acceptable performance with known dynamic parameters such as stability and robustness but there are two important disadvantages as below: chattering phenomenon and mathematical nonlinear dynamic equivalent controller part. The first challenge; nonlinear dynamic part; is applied by inference estimator method in sliding mode controller in order to solve the nonlinear problems in classical sliding mode controller. And the second challenge; chattering phenomenon; is removed by linear method. Asymptotic stability of the closed loop system is also proved in the sense of Lyapunov. In the last part it can find the implementation of MIMO fuzzy estimator sliding mode controller on FPGA; FPGA-based fuzzy estimator sliding mode controller has many advantages such as high speed, low cost, short time to market and small device size. One of the most important drawbacks is limited capacity of available cells which this research focuses to solve this challenge. FPGA can be used to design a controller in a single chip Integrated Circuit (IC). In this research the SMC is designed using Very High Description Language (VHDL) for implementation on FPGA device (XA3S1600E-Spartan-3E), with minimum chattering.
Artificial Chattering Free on-line Fuzzy Sliding Mode Algorithm for Uncertain...CSCJournals
This document proposes an artificial chattering free adaptive fuzzy sliding mode control algorithm for uncertain systems, specifically applied to robot manipulators. The algorithm combines sliding mode control, fuzzy logic control, and adaptive methods to address some limitations of each approach. Specifically, fuzzy logic is used instead of saturation functions and dead zones to eliminate chattering. Fuzzy rules are also used to estimate the nonlinear dynamic equivalent control term, reducing complexity. Adaptive methods are used to continuously adjust the sliding function in real-time for improved performance with uncertain or time-varying parameters. The algorithm is tested in simulations of a PUMA robot manipulator.
Design Error-based Linear Model-free Evaluation Performance Computed Torque C...Waqas Tariq
Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of the most important challenging works. This research focuses on the design, implementation and analysis of a model-free linear error-based tuning computed torque controller for highly nonlinear dynamic second order system, in presence of uncertainties. In order to provide high performance nonlinear methodology, computed torque controller is selected. Pure computed torque controller can be used to control of partly known nonlinear dynamic parameters of nonlinear systems. Conversely, pure computed torque controller is used in many applications; it has an important drawback namely; nonlinear equivalent dynamic formulation in uncertain dynamic parameter. In order to solve the uncertain nonlinear dynamic parameters, implement easily and avoid mathematical model base controller, model-free performance/error-based linear methodology with three inputs and one output is applied to pure computed torque controller. The results demonstrate that the error-based linear tuning computed torque controller is a model-based controllers which works well in certain and uncertain system. Pure computed torque controller has difficulty in handling unstructured model uncertainties. To solve this problem applied linear model-free error -based tuning method to computed torque controller for adjusting the linear inner loop gain (K ). Since the linear inner loop gain (K) is adjusted by linear error-based tuning method, it is linear and continuous. In this research new K is obtained by the previous K multiple gain updating factor (á) which is a coefficient varies between half to two.
Digital Implementation of Fuzzy Logic Controller for Real Time Position Contr...IOSR Journals
Fuzzy Logic Controller (FLC) systems have emerged as one of the most promising areas for
Industrial Applications. The highly growth of fuzzy logic applications led to the need of finding efficient way to
hardware implementation. Field Programmable Gate Array (FPGA) is the most important tool for hardware
implementation due to low consumption of energy, high speed of operation and large capacity of data storage.
In this paper, instead of an introduction to fuzzy logic control methodology, we have demonstrated the
implementation of a FLC through the use of the Very high speed integrated circuits Hardware Description
Language (VHDL) code. FLC is designed for position control of BLDC Motor. VHDL has been used to develop
FLC on FPGA. A Mamdani type FLC structure has been used to obtain the controller output. The controller
algorithm developed synthesized, simulated and implemented on FPGA Spartan 3E board.
Adaptive MIMO Fuzzy Compensate Fuzzy Sliding Mode Algorithm: Applied to Secon...CSCJournals
This research is focused on proposed adaptive fuzzy sliding mode algorithms with the adaptation laws derived in the Lyapunov sense. The stability of the closed-loop system is proved mathematically based on the Lyapunov method. Adaptive MIMO fuzzy compensate fuzzy sliding mode method design a MIMO fuzzy system to compensate for the model uncertainties of the system, and chattering also solved by linear saturation method. Since there is no tuning method to adjust the premise part of fuzzy rules so we presented a scheme to online tune consequence part of fuzzy rules. Classical sliding mode control is robust to control model uncertainties and external disturbances. A sliding mode method with a switching control low guarantees the stability of the certain and/or uncertain system, but the addition of the switching control low introduces chattering into the system. One way to reduce or eliminate chattering is to insert a boundary layer method inside of a boundary layer around the sliding surface. Classical sliding mode control method has difficulty in handling unstructured model uncertainties. One can overcome this problem by combining a sliding mode controller and artificial intelligence (e.g. fuzzy logic). To approximate a time-varying nonlinear dynamic system, a fuzzy system requires a large amount of fuzzy rule base. This large number of fuzzy rules will cause a high computation load. The addition of an adaptive law to a fuzzy sliding mode controller to online tune the parameters of the fuzzy rules in use will ensure a moderate computational load. The adaptive laws in this algorithm are designed based on the Lyapunov stability theorem. Asymptotic stability of the closed loop system is also proved in the sense of Lyapunov.
Position Control of Robot Manipulator: Design a Novel SISO Adaptive Sliding M...Waqas Tariq
The document describes a novel adaptive sliding mode fuzzy PD fuzzy sliding mode control algorithm for position control of robot manipulators. The algorithm uses a single-input single-output fuzzy system to compensate for model uncertainties and eliminate chattering using a linear boundary layer method. It also online tunes the sliding function parameter using adaptation laws. The stability of the closed-loop system is proved mathematically using Lyapunov stability theory. The algorithm is analyzed and evaluated on a 2 degree of freedom robotic manipulator to achieve improved tracking performance compared to conventional sliding mode control approaches.
The developed control methodology can be used to build more efficient intelligent and precision mechatronic systems. Three degrees of freedom robot arm is controlled by adaptive sliding mode fuzzy algorithm fuzzy sliding mode controller (SMFAFSMC). This plant has 3 revolute joints allowing the corresponding links to move horizontally. Control of robotic manipulator is very important in field of robotic, because robotic manipulators are Multi-Input Multi-Output (MIMO), nonlinear and most of dynamic parameters are uncertainty. Design strong mathematical tools used in new control methodologies to design adaptive nonlinear robust controller with acceptable performance in this controller is the main challenge. Sliding mode methodology is a nonlinear robust controller which can be used in uncertainty nonlinear systems, but pure sliding mode controller has chattering phenomenon and nonlinear equivalent part in uncertain system therefore the first step is focused on eliminate the chattering and in second step controller is improved with regard to uncertainties. Sliding function is one of the most important challenging in artificial sliding mode algorithm which this problem in order to solved by on-line tuning method. This paper focuses on adjusting the sliding surface slope in fuzzy sliding mode controller by sliding mode fuzzy algorithm.
Design Artificial Nonlinear Robust Controller Based on CTLC and FSMC with Tun...Waqas Tariq
The document describes research on designing artificial nonlinear robust controllers for robot manipulators. It discusses two classical nonlinear robust controllers - sliding mode controller (SMC) and computed torque controller (CTC) - and their limitations when applied to systems with uncertainties. It then proposes applying fuzzy logic methodology to these classical controllers to reduce their limitations. Specifically, it develops a fuzzy sliding mode controller with tunable gain (GTFSMC) and a computed torque-like controller with tunable gain (GTCTLC) that use fuzzy logic rules to eliminate the mathematical nonlinear dynamics and reduce chattering through optimization of the tunable gain parameter. The controllers aim to achieve satisfactory performance for robot manipulators operating in unknown environments with uncertainties and disturbances.
Design of Model Free Adaptive Fuzzy Computed Torque Controller for a Nonlinea...Waqas Tariq
In this study, a model free adaptive fuzzy computed torque controller (AFCTC) is designed for a two-degree-of freedom robot manipulator to rich the best performance. Computed torque controller is studied because of its high performance. AFCTC has been also included in this study because of its robust character and high performance. Besides, this control method can be applied to non-linear systems easily. Today, robot manipulators are used in unknown and unstructured environment and caused to provide sophisticated systems, therefore strong mathematical tools are used in new control methodologies to design adaptive nonlinear robust controller with acceptable performance (e.g., minimum error, good trajectory, disturbance rejection). The strategies of control robot manipulator are classified into two main groups: classical and non-classical methods, however both classical and non-classical theories have been applied successfully in many applications, but they also have some limitation. One of the most important nonlinear robust controller that can used in uncertainty nonlinear systems, are computed torque controller. This paper is focuses on applied non-classical method in robust classical method to reduce the limitations. Therefore adaptive fuzzy computed torque controller will be presented in this paper.
Novel Artificial Control of Nonlinear Uncertain System: Design a Novel Modifi...Waqas Tariq
This document presents a novel particle swarm optimization sliding mode control algorithm using fuzzy logic to estimate uncertainties for nonlinear systems like robotic manipulators. The algorithm combines PSO, sliding mode control, and fuzzy logic to address issues like chattering and not requiring an accurate dynamic model. It estimates the equivalent dynamic term using fuzzy logic to compensate for uncertainties. PSO is used to tune parameters offline for improved performance. Stability of the closed-loop system is proved using the Lyapunov method. The algorithm aims to provide robust control of robotic manipulators without an accurate dynamic model.
Evolutionary Design of Backstepping Artificial Sliding Mode Based Position Al...CSCJournals
This paper expands a fuzzy sliding mode based position controller whose sliding function is on-line tuned by backstepping methodology. The main goal is to guarantee acceptable position trajectories tracking between the robot manipulator end-effector and the input desired position. The fuzzy controller in proposed fuzzy sliding mode controller is based on Mamdani’s fuzzy inference system (FIS) and it has one input and one output. The input represents the function between sliding function, error and the rate of error. The second input is the angle formed by the straight line defined with the orientation of the robot, and the straight line that connects the robot with the reference cart. The outputs represent angular position, velocity and acceleration commands, respectively. The backstepping methodology is on-line tune the sliding function based on self tuning methodology. The performance of the backstepping on-line tune fuzzy sliding mode controller (TBsFSMC) is validated through comparison with previously developed robot manipulator position controller based on adaptive fuzzy sliding mode control theory (AFSMC). Simulation results signify good performance of position tracking in presence of uncertainty and external disturbance.
TORQUE CONTROL OF AC MOTOR WITH FOPID CONTROLLER BASED ON FUZZY NEURAL ALGORITHMijics
Nowadays in the complicated systems, design of proper and implementable controller has a most importance. With respect to ability of fractional order systems in complicated systems identification as a first order fractional system with time delay, usage of fractional order PID has a proper result. From one side flexibility of fractional calculus than integer order has been topics of interest to the researchers. From another side, PMSM motors which are one the AC motor types, has been allocated largely accounted position in industry and used in variety applications. Therefore in this paper torque direct control of PMSM motors with FOPID based on model is proposed. Also fuzzy neural controllers are widely considered. Reason of this is success of fuzzy neural controller in control and identification of uncertain and complicated systems. The proposed method in this paper is combination of FOPID controller with fuzzy neural supervision system which with coefficients setting of this controller, control operation of PMSM will improve. Results of proposed method show the ability of proposed technique in reference signal tracking, elimination of disturbances effects and functional robustness in presence of noise and uncertainty. The results show the error averagely in three condition, nominal form, step disturbance and noise and uncertainly will decrease 11.66% in proposed method (FNFOPID) with Integral Square Error criterion and 7.69% with Integral Absolute Error criterion in comparison to FOPID.
Design a nonlinear controller for second order nonlinear uncertain dynamical systems (e.g., Internal Combustion Engine) is one of the most important challenging works. This paper focuses on the design of a robust backstepping adaptive feedback linearization controller (FLC) for internal combustion (IC) engine in presence of uncertainties. In order to provide high performance nonlinear methodology, feedback linearization controller is selected. Pure feedback linearization controller can be used to control of partly unknown nonlinear dynamic parameters of IC engine. In order to solve the uncertain nonlinear dynamic parameters, implement easily and avoid mathematical model base controller, Mamdani’s performance/error-based fuzzy logic methodology with two inputs and one output and 49 rules is applied to pure feedback linearization controller. The results demonstrate that the error-based fuzzy feedback linearization controller is a model-free controllers which works well in certain and partly uncertain system. Pure feedback linearization controller and error-based feedback linearization like controller with have difficulty in handling unstructured model uncertainties. To solve this problem applied backstepping-based tuning method to error-based fuzzy feedback linearization controller for adjusting the feedback linearization controller gain (K_p,K_v ). This controller has acceptable performance in presence of uncertainty (e.g., overshoot=1%, rise time=0.48 second, steady state error = 1.3e-9 and RMS error=1.8e-11).
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Design Mathematical Tunable Gain PID-Like Sliding Mode Fuzzy Controller With ...Waqas Tariq
In this study, a mathematical tunable gain model free PID-like sliding mode fuzzy controller (GTSMFC) is designed to rich the best performance. Sliding mode fuzzy controller is studied because of its model free, stable and high performance. Today, most of systems (e.g., robot manipulators) are used in unknown and unstructured environment and caused to provide sophisticated systems, therefore strong mathematical tools (e.g., nonlinear sliding mode controller) are used in artificial intelligent control methodologies to design model free nonlinear robust controller with high performance (e.g., minimum error, good trajectory, disturbance rejection). Non linear classical theories have been applied successfully in many applications, but they also have some limitation. One of the best nonlinear robust controller which can be used in uncertainty nonlinear systems, are sliding mode controller but pure sliding mode controller has some disadvantages therefore this research focuses on applied sliding mode controller in fuzzy logic theory to solve the limitation in fuzzy logic controller and sliding mode controller. One of the most important challenging in pure sliding mode controller and sliding mode fuzzy controller is sliding surface slope. This paper focuses on adjusting the gain updating factor and sliding surface slope in PID like sliding mode fuzzy controller to have the best performance and reduce the limitation.
Control of IC Engine: Design a Novel MIMO Fuzzy Backstepping Adaptive Based F...Waqas Tariq
This paper expands a Multi Input Multi Output (MIMO) fuzzy estimator variable structure control (VSC) which controller coefficient is on-line tuned by fuzzy backstepping algorithm. The main goal is to guarantee acceptable trajectories tracking between the internal combustion engine (IC engine) air to fuel ratio and the desired input. The fuzzy controller in proposed fuzzy estimator variable structure controller is based on Lyapunov fuzzy inference system (FIS) with minimum model based rule base. The input represents the function between variable structure function, error and the rate of error. The outputs represent fuel ratio, respectively. The fuzzy backstepping methodology is on-line tune the variable structure function based on adaptive methodology. The performance of the MIMO fuzzy estimator VSC which controller coefficient is on-line tuned by fuzzy backstepping algorithm (FBAFVSC) is validated through comparison with VSC and proposed method. Simulation results signify good performance of fuel ratio in presence of uncertainty and external disturbance.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Speed Sensorless Vector Control of Unbalanced Three-Phase Induction Motor wit...IAES-IJPEDS
This paper presents a technique for speed sensorless Rotor Flux Oriented Control (RFOC) of 3-phase Induction Motor (IM) under open-phase fault (unbalanced or faulty IM). The presented RFOC strategy is based on rotational transformation. An adaptive sliding mode control system with an adaptive switching gain is proposed instead of the speed PI controller. Using an adaptive sliding mode control causes the proposed speed sensorless RFOC drive system to become insensitive to uncertainties such as load disturbances and parameter variations. Moreover, with adaptation of the sliding switching gain, calculation of the system uncertainties upper bound is not needed. Finally, simulation results have been presented to confirm the good performance of the proposed method.
Novel Method of FOC to Speed Control in Three-Phase IM under Normal and Fault...IJPEDS-IAES
This paper proposes a novel method for speed control of three-phase
induction motor (IM) which can be used for both healthy three-phase IM and
three-phase IM under open-phase fault. The proposed fault-tolerant control
system is derived from conventional field-oriented control (FOC) algorithm
with minor changes on it. The presented drive system is based on using an
appropriate transformation matrix for the stator current variables. The
presented method in this paper can be also used for speed control of singlephase
IMs with two windings. The feasibility of the proposed strategy is
verified by simulation results.
This document presents a seminar on field oriented control of induction motors. It discusses direct and indirect field oriented control, with direct being better at high speeds but indirect not requiring additional sensors. It also discusses applications in industries like pumps, fans, conveyors and elevators. The document presents Matlab simulations comparing control methods, showing fuzzy logic control has better speed and torque response than PI control, with improved performance at low speeds. It concludes the hybrid model using current estimation could improve indirect control at low speeds.
Automation is a preferred for faster and precise operations as compare to manual operation. This paper provides a mean of simple yet effective fixed type of automation for sorting the products. Two products identical in shape and size are sorted out automatically on the basis of drilled or undrilled product. LED and photo transistor arrangement is used for hole detection. Vertical zigzag conveyor is employed instead of usual flat belt conveyor in order to utilise gravitational force as a driving force for feeding the products. 8051 Microcontroller is used for controlling the sorting mechanism by using program or coding. The cost of project is nearly negligible as it is made from the waste except electrical components used for the project. Sincere efforts are taken to set an example of a inexpensive, reliable and easy to manufacture automated machine. The Paper focuses on the aspect of sorting mechanism with microcontroller programming and the automation tasks and solutions is obtained.
Sliding Mode Methodology Vs. Computed Torque Methodology Using MATLAB/SIMULIN...CSCJournals
Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of the most important challenging works. This paper focuses on the design, implementation and analysis of a chattering free sliding mode controller for highly nonlinear dynamic PUMA robot manipulator and compare to computed torque controller, in presence of uncertainties. In order to provide high performance nonlinear methodology, sliding mode controller and computed torque controller are selected. Pure sliding mode controller and computed torque controller can be used to control of partly known nonlinear dynamic parameters of robot manipulator. Conversely, pure sliding mode controller is used in many applications; it has an important drawback namely; chattering phenomenon which it can causes some problems such as saturation and heat the mechanical parts of robot manipulators or drivers. In order to reduce the chattering this research is used the linear saturation function boundary layer method instead of switching function method in pure sliding mode controller. These simulation models are developed as a part of a software laboratory to support and enhance graduate/undergraduate robotics courses, nonlinear control courses and MATLAB/SIMULINK courses at research and development company (SSP Co.) research center, Shiraz, Iran.
This chapter introduces sliding control methodology for nonlinear systems with structured or unstructured uncertainties. A sliding surface is defined in the state space based on a tracking error variable. The sliding condition requires the system trajectories to point towards the sliding surface. Satisfying the sliding condition guarantees the system states will reach and remain on the sliding surface in finite time. The equivalent control law that would maintain the system on the surface can be constructed. Practically, a discontinuous control is added to the estimated equivalent control to satisfy the sliding condition despite model uncertainties. The control discontinuity must increase with greater parametric uncertainty or uncertainty in the control gain. An example of controlling an underwater vehicle's position is provided to illustrate the methodology.
Artificial Chattering Free on-line Fuzzy Sliding Mode Algorithm for Uncertain...CSCJournals
This document proposes an artificial chattering free adaptive fuzzy sliding mode control algorithm for uncertain systems, specifically applied to robot manipulators. The algorithm combines sliding mode control, fuzzy logic control, and adaptive methods to address some limitations of each approach. Specifically, fuzzy logic is used instead of saturation functions and dead zones to eliminate chattering. Fuzzy rules are also used to estimate the nonlinear dynamic equivalent control term, reducing complexity. Adaptive methods are used to continuously adjust the sliding function in real-time for improved performance with uncertain or time-varying parameters. The algorithm is tested in simulations of a PUMA robot manipulator.
Design Error-based Linear Model-free Evaluation Performance Computed Torque C...Waqas Tariq
Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of the most important challenging works. This research focuses on the design, implementation and analysis of a model-free linear error-based tuning computed torque controller for highly nonlinear dynamic second order system, in presence of uncertainties. In order to provide high performance nonlinear methodology, computed torque controller is selected. Pure computed torque controller can be used to control of partly known nonlinear dynamic parameters of nonlinear systems. Conversely, pure computed torque controller is used in many applications; it has an important drawback namely; nonlinear equivalent dynamic formulation in uncertain dynamic parameter. In order to solve the uncertain nonlinear dynamic parameters, implement easily and avoid mathematical model base controller, model-free performance/error-based linear methodology with three inputs and one output is applied to pure computed torque controller. The results demonstrate that the error-based linear tuning computed torque controller is a model-based controllers which works well in certain and uncertain system. Pure computed torque controller has difficulty in handling unstructured model uncertainties. To solve this problem applied linear model-free error -based tuning method to computed torque controller for adjusting the linear inner loop gain (K ). Since the linear inner loop gain (K) is adjusted by linear error-based tuning method, it is linear and continuous. In this research new K is obtained by the previous K multiple gain updating factor (á) which is a coefficient varies between half to two.
Digital Implementation of Fuzzy Logic Controller for Real Time Position Contr...IOSR Journals
Fuzzy Logic Controller (FLC) systems have emerged as one of the most promising areas for
Industrial Applications. The highly growth of fuzzy logic applications led to the need of finding efficient way to
hardware implementation. Field Programmable Gate Array (FPGA) is the most important tool for hardware
implementation due to low consumption of energy, high speed of operation and large capacity of data storage.
In this paper, instead of an introduction to fuzzy logic control methodology, we have demonstrated the
implementation of a FLC through the use of the Very high speed integrated circuits Hardware Description
Language (VHDL) code. FLC is designed for position control of BLDC Motor. VHDL has been used to develop
FLC on FPGA. A Mamdani type FLC structure has been used to obtain the controller output. The controller
algorithm developed synthesized, simulated and implemented on FPGA Spartan 3E board.
Adaptive MIMO Fuzzy Compensate Fuzzy Sliding Mode Algorithm: Applied to Secon...CSCJournals
This research is focused on proposed adaptive fuzzy sliding mode algorithms with the adaptation laws derived in the Lyapunov sense. The stability of the closed-loop system is proved mathematically based on the Lyapunov method. Adaptive MIMO fuzzy compensate fuzzy sliding mode method design a MIMO fuzzy system to compensate for the model uncertainties of the system, and chattering also solved by linear saturation method. Since there is no tuning method to adjust the premise part of fuzzy rules so we presented a scheme to online tune consequence part of fuzzy rules. Classical sliding mode control is robust to control model uncertainties and external disturbances. A sliding mode method with a switching control low guarantees the stability of the certain and/or uncertain system, but the addition of the switching control low introduces chattering into the system. One way to reduce or eliminate chattering is to insert a boundary layer method inside of a boundary layer around the sliding surface. Classical sliding mode control method has difficulty in handling unstructured model uncertainties. One can overcome this problem by combining a sliding mode controller and artificial intelligence (e.g. fuzzy logic). To approximate a time-varying nonlinear dynamic system, a fuzzy system requires a large amount of fuzzy rule base. This large number of fuzzy rules will cause a high computation load. The addition of an adaptive law to a fuzzy sliding mode controller to online tune the parameters of the fuzzy rules in use will ensure a moderate computational load. The adaptive laws in this algorithm are designed based on the Lyapunov stability theorem. Asymptotic stability of the closed loop system is also proved in the sense of Lyapunov.
Position Control of Robot Manipulator: Design a Novel SISO Adaptive Sliding M...Waqas Tariq
The document describes a novel adaptive sliding mode fuzzy PD fuzzy sliding mode control algorithm for position control of robot manipulators. The algorithm uses a single-input single-output fuzzy system to compensate for model uncertainties and eliminate chattering using a linear boundary layer method. It also online tunes the sliding function parameter using adaptation laws. The stability of the closed-loop system is proved mathematically using Lyapunov stability theory. The algorithm is analyzed and evaluated on a 2 degree of freedom robotic manipulator to achieve improved tracking performance compared to conventional sliding mode control approaches.
The developed control methodology can be used to build more efficient intelligent and precision mechatronic systems. Three degrees of freedom robot arm is controlled by adaptive sliding mode fuzzy algorithm fuzzy sliding mode controller (SMFAFSMC). This plant has 3 revolute joints allowing the corresponding links to move horizontally. Control of robotic manipulator is very important in field of robotic, because robotic manipulators are Multi-Input Multi-Output (MIMO), nonlinear and most of dynamic parameters are uncertainty. Design strong mathematical tools used in new control methodologies to design adaptive nonlinear robust controller with acceptable performance in this controller is the main challenge. Sliding mode methodology is a nonlinear robust controller which can be used in uncertainty nonlinear systems, but pure sliding mode controller has chattering phenomenon and nonlinear equivalent part in uncertain system therefore the first step is focused on eliminate the chattering and in second step controller is improved with regard to uncertainties. Sliding function is one of the most important challenging in artificial sliding mode algorithm which this problem in order to solved by on-line tuning method. This paper focuses on adjusting the sliding surface slope in fuzzy sliding mode controller by sliding mode fuzzy algorithm.
Design Artificial Nonlinear Robust Controller Based on CTLC and FSMC with Tun...Waqas Tariq
The document describes research on designing artificial nonlinear robust controllers for robot manipulators. It discusses two classical nonlinear robust controllers - sliding mode controller (SMC) and computed torque controller (CTC) - and their limitations when applied to systems with uncertainties. It then proposes applying fuzzy logic methodology to these classical controllers to reduce their limitations. Specifically, it develops a fuzzy sliding mode controller with tunable gain (GTFSMC) and a computed torque-like controller with tunable gain (GTCTLC) that use fuzzy logic rules to eliminate the mathematical nonlinear dynamics and reduce chattering through optimization of the tunable gain parameter. The controllers aim to achieve satisfactory performance for robot manipulators operating in unknown environments with uncertainties and disturbances.
Design of Model Free Adaptive Fuzzy Computed Torque Controller for a Nonlinea...Waqas Tariq
In this study, a model free adaptive fuzzy computed torque controller (AFCTC) is designed for a two-degree-of freedom robot manipulator to rich the best performance. Computed torque controller is studied because of its high performance. AFCTC has been also included in this study because of its robust character and high performance. Besides, this control method can be applied to non-linear systems easily. Today, robot manipulators are used in unknown and unstructured environment and caused to provide sophisticated systems, therefore strong mathematical tools are used in new control methodologies to design adaptive nonlinear robust controller with acceptable performance (e.g., minimum error, good trajectory, disturbance rejection). The strategies of control robot manipulator are classified into two main groups: classical and non-classical methods, however both classical and non-classical theories have been applied successfully in many applications, but they also have some limitation. One of the most important nonlinear robust controller that can used in uncertainty nonlinear systems, are computed torque controller. This paper is focuses on applied non-classical method in robust classical method to reduce the limitations. Therefore adaptive fuzzy computed torque controller will be presented in this paper.
Novel Artificial Control of Nonlinear Uncertain System: Design a Novel Modifi...Waqas Tariq
This document presents a novel particle swarm optimization sliding mode control algorithm using fuzzy logic to estimate uncertainties for nonlinear systems like robotic manipulators. The algorithm combines PSO, sliding mode control, and fuzzy logic to address issues like chattering and not requiring an accurate dynamic model. It estimates the equivalent dynamic term using fuzzy logic to compensate for uncertainties. PSO is used to tune parameters offline for improved performance. Stability of the closed-loop system is proved using the Lyapunov method. The algorithm aims to provide robust control of robotic manipulators without an accurate dynamic model.
Evolutionary Design of Backstepping Artificial Sliding Mode Based Position Al...CSCJournals
This paper expands a fuzzy sliding mode based position controller whose sliding function is on-line tuned by backstepping methodology. The main goal is to guarantee acceptable position trajectories tracking between the robot manipulator end-effector and the input desired position. The fuzzy controller in proposed fuzzy sliding mode controller is based on Mamdani’s fuzzy inference system (FIS) and it has one input and one output. The input represents the function between sliding function, error and the rate of error. The second input is the angle formed by the straight line defined with the orientation of the robot, and the straight line that connects the robot with the reference cart. The outputs represent angular position, velocity and acceleration commands, respectively. The backstepping methodology is on-line tune the sliding function based on self tuning methodology. The performance of the backstepping on-line tune fuzzy sliding mode controller (TBsFSMC) is validated through comparison with previously developed robot manipulator position controller based on adaptive fuzzy sliding mode control theory (AFSMC). Simulation results signify good performance of position tracking in presence of uncertainty and external disturbance.
TORQUE CONTROL OF AC MOTOR WITH FOPID CONTROLLER BASED ON FUZZY NEURAL ALGORITHMijics
Nowadays in the complicated systems, design of proper and implementable controller has a most importance. With respect to ability of fractional order systems in complicated systems identification as a first order fractional system with time delay, usage of fractional order PID has a proper result. From one side flexibility of fractional calculus than integer order has been topics of interest to the researchers. From another side, PMSM motors which are one the AC motor types, has been allocated largely accounted position in industry and used in variety applications. Therefore in this paper torque direct control of PMSM motors with FOPID based on model is proposed. Also fuzzy neural controllers are widely considered. Reason of this is success of fuzzy neural controller in control and identification of uncertain and complicated systems. The proposed method in this paper is combination of FOPID controller with fuzzy neural supervision system which with coefficients setting of this controller, control operation of PMSM will improve. Results of proposed method show the ability of proposed technique in reference signal tracking, elimination of disturbances effects and functional robustness in presence of noise and uncertainty. The results show the error averagely in three condition, nominal form, step disturbance and noise and uncertainly will decrease 11.66% in proposed method (FNFOPID) with Integral Square Error criterion and 7.69% with Integral Absolute Error criterion in comparison to FOPID.
Design a nonlinear controller for second order nonlinear uncertain dynamical systems (e.g., Internal Combustion Engine) is one of the most important challenging works. This paper focuses on the design of a robust backstepping adaptive feedback linearization controller (FLC) for internal combustion (IC) engine in presence of uncertainties. In order to provide high performance nonlinear methodology, feedback linearization controller is selected. Pure feedback linearization controller can be used to control of partly unknown nonlinear dynamic parameters of IC engine. In order to solve the uncertain nonlinear dynamic parameters, implement easily and avoid mathematical model base controller, Mamdani’s performance/error-based fuzzy logic methodology with two inputs and one output and 49 rules is applied to pure feedback linearization controller. The results demonstrate that the error-based fuzzy feedback linearization controller is a model-free controllers which works well in certain and partly uncertain system. Pure feedback linearization controller and error-based feedback linearization like controller with have difficulty in handling unstructured model uncertainties. To solve this problem applied backstepping-based tuning method to error-based fuzzy feedback linearization controller for adjusting the feedback linearization controller gain (K_p,K_v ). This controller has acceptable performance in presence of uncertainty (e.g., overshoot=1%, rise time=0.48 second, steady state error = 1.3e-9 and RMS error=1.8e-11).
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Design Mathematical Tunable Gain PID-Like Sliding Mode Fuzzy Controller With ...Waqas Tariq
In this study, a mathematical tunable gain model free PID-like sliding mode fuzzy controller (GTSMFC) is designed to rich the best performance. Sliding mode fuzzy controller is studied because of its model free, stable and high performance. Today, most of systems (e.g., robot manipulators) are used in unknown and unstructured environment and caused to provide sophisticated systems, therefore strong mathematical tools (e.g., nonlinear sliding mode controller) are used in artificial intelligent control methodologies to design model free nonlinear robust controller with high performance (e.g., minimum error, good trajectory, disturbance rejection). Non linear classical theories have been applied successfully in many applications, but they also have some limitation. One of the best nonlinear robust controller which can be used in uncertainty nonlinear systems, are sliding mode controller but pure sliding mode controller has some disadvantages therefore this research focuses on applied sliding mode controller in fuzzy logic theory to solve the limitation in fuzzy logic controller and sliding mode controller. One of the most important challenging in pure sliding mode controller and sliding mode fuzzy controller is sliding surface slope. This paper focuses on adjusting the gain updating factor and sliding surface slope in PID like sliding mode fuzzy controller to have the best performance and reduce the limitation.
Control of IC Engine: Design a Novel MIMO Fuzzy Backstepping Adaptive Based F...Waqas Tariq
This paper expands a Multi Input Multi Output (MIMO) fuzzy estimator variable structure control (VSC) which controller coefficient is on-line tuned by fuzzy backstepping algorithm. The main goal is to guarantee acceptable trajectories tracking between the internal combustion engine (IC engine) air to fuel ratio and the desired input. The fuzzy controller in proposed fuzzy estimator variable structure controller is based on Lyapunov fuzzy inference system (FIS) with minimum model based rule base. The input represents the function between variable structure function, error and the rate of error. The outputs represent fuel ratio, respectively. The fuzzy backstepping methodology is on-line tune the variable structure function based on adaptive methodology. The performance of the MIMO fuzzy estimator VSC which controller coefficient is on-line tuned by fuzzy backstepping algorithm (FBAFVSC) is validated through comparison with VSC and proposed method. Simulation results signify good performance of fuel ratio in presence of uncertainty and external disturbance.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Speed Sensorless Vector Control of Unbalanced Three-Phase Induction Motor wit...IAES-IJPEDS
This paper presents a technique for speed sensorless Rotor Flux Oriented Control (RFOC) of 3-phase Induction Motor (IM) under open-phase fault (unbalanced or faulty IM). The presented RFOC strategy is based on rotational transformation. An adaptive sliding mode control system with an adaptive switching gain is proposed instead of the speed PI controller. Using an adaptive sliding mode control causes the proposed speed sensorless RFOC drive system to become insensitive to uncertainties such as load disturbances and parameter variations. Moreover, with adaptation of the sliding switching gain, calculation of the system uncertainties upper bound is not needed. Finally, simulation results have been presented to confirm the good performance of the proposed method.
Novel Method of FOC to Speed Control in Three-Phase IM under Normal and Fault...IJPEDS-IAES
This paper proposes a novel method for speed control of three-phase
induction motor (IM) which can be used for both healthy three-phase IM and
three-phase IM under open-phase fault. The proposed fault-tolerant control
system is derived from conventional field-oriented control (FOC) algorithm
with minor changes on it. The presented drive system is based on using an
appropriate transformation matrix for the stator current variables. The
presented method in this paper can be also used for speed control of singlephase
IMs with two windings. The feasibility of the proposed strategy is
verified by simulation results.
This document presents a seminar on field oriented control of induction motors. It discusses direct and indirect field oriented control, with direct being better at high speeds but indirect not requiring additional sensors. It also discusses applications in industries like pumps, fans, conveyors and elevators. The document presents Matlab simulations comparing control methods, showing fuzzy logic control has better speed and torque response than PI control, with improved performance at low speeds. It concludes the hybrid model using current estimation could improve indirect control at low speeds.
Automation is a preferred for faster and precise operations as compare to manual operation. This paper provides a mean of simple yet effective fixed type of automation for sorting the products. Two products identical in shape and size are sorted out automatically on the basis of drilled or undrilled product. LED and photo transistor arrangement is used for hole detection. Vertical zigzag conveyor is employed instead of usual flat belt conveyor in order to utilise gravitational force as a driving force for feeding the products. 8051 Microcontroller is used for controlling the sorting mechanism by using program or coding. The cost of project is nearly negligible as it is made from the waste except electrical components used for the project. Sincere efforts are taken to set an example of a inexpensive, reliable and easy to manufacture automated machine. The Paper focuses on the aspect of sorting mechanism with microcontroller programming and the automation tasks and solutions is obtained.
Sliding Mode Methodology Vs. Computed Torque Methodology Using MATLAB/SIMULIN...CSCJournals
Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of the most important challenging works. This paper focuses on the design, implementation and analysis of a chattering free sliding mode controller for highly nonlinear dynamic PUMA robot manipulator and compare to computed torque controller, in presence of uncertainties. In order to provide high performance nonlinear methodology, sliding mode controller and computed torque controller are selected. Pure sliding mode controller and computed torque controller can be used to control of partly known nonlinear dynamic parameters of robot manipulator. Conversely, pure sliding mode controller is used in many applications; it has an important drawback namely; chattering phenomenon which it can causes some problems such as saturation and heat the mechanical parts of robot manipulators or drivers. In order to reduce the chattering this research is used the linear saturation function boundary layer method instead of switching function method in pure sliding mode controller. These simulation models are developed as a part of a software laboratory to support and enhance graduate/undergraduate robotics courses, nonlinear control courses and MATLAB/SIMULINK courses at research and development company (SSP Co.) research center, Shiraz, Iran.
This chapter introduces sliding control methodology for nonlinear systems with structured or unstructured uncertainties. A sliding surface is defined in the state space based on a tracking error variable. The sliding condition requires the system trajectories to point towards the sliding surface. Satisfying the sliding condition guarantees the system states will reach and remain on the sliding surface in finite time. The equivalent control law that would maintain the system on the surface can be constructed. Practically, a discontinuous control is added to the estimated equivalent control to satisfy the sliding condition despite model uncertainties. The control discontinuity must increase with greater parametric uncertainty or uncertainty in the control gain. An example of controlling an underwater vehicle's position is provided to illustrate the methodology.
Sliding Mode Controller for Robotic Flexible Arm Jointomkarharshe
The problem of joint flexibility was of critical importance since the use of robot started in the fields such as space science and surveillance. This project addresses this issue by applying a stabilizing control law, ensuring robustness against plant uncertainties and disturbances.
This document provides an overview and table of contents for a book on advanced sliding mode control for mechanical systems. The book covers topics such as sliding mode controller design, analysis, and MATLAB simulation. It includes 11 chapters that discuss various sliding mode control approaches, such as normal sliding mode control, advanced sliding mode control, discrete sliding mode control, adaptive sliding mode control, terminal sliding mode control, and sliding mode control based on observers. Case studies and MATLAB programs are provided to illustrate practical applications of the theory.
Sliding Mode Control (SMC) is a type of Variable Structure Control that uses a discontinuous control law to drive the system states towards a switching surface, called the sliding surface, in finite time, resulting in robust behavior to disturbances and uncertainties. The summary discusses:
1) SMC involves selecting a sliding surface and designing discontinuous feedback gains such that the system trajectory intersects and stays on the sliding surface.
2) For a sliding mode to exist, the system state trajectory velocity must always be directed towards the sliding surface in its vicinity.
3) Using Lyapunov stability analysis, sufficient conditions are presented to guarantee the existence of a sliding mode and that the sliding surface is reached in finite
The sliding mode control approach is recognized as one of the
efficient tools to design robust controllers for complex high-order non-linear dynamic plant operating under uncertainty conditions.
The document provides an introduction to robotics, including definitions of key terms:
- A robot is an automatically controlled, reprogrammable device designed to perform tasks normally done by humans.
- Robots are classified based on their drive technology, work envelope/coordinate geometry, and motion control methods.
- Robotic systems have components like manipulators, end effectors, actuators, sensors, controllers, and software to allow various applications in manufacturing and other industries.
This paper expands a sliding mode fuzzy controller which sliding surface gain is on-line tuned by minimum fuzzy inference algorithm. The main goal is to guarantee acceptable trajectories tracking between the second order nonlinear system (robot manipulator) actual and the desired trajectory. The fuzzy controller in proposed sliding mode fuzzy controller is based on Mamdani’s fuzzy inference system (FIS) and it has one input and one output. The input represents the function between sliding function, error and the rate of error. The outputs represent torque, respectively. The fuzzy inference system methodology is on-line tune the sliding surface gain based on error-based fuzzy tuning methodology. Pure sliding mode fuzzy controller has difficulty in handling unstructured model uncertainties. To solve this problem applied fuzzy-based tuning method to sliding mode fuzzy controller for adjusting the sliding surface gain (ë ). Since the sliding surface gain (ë) is adjusted by fuzzy-based tuning method, it is nonlinear and continuous. Fuzzy-based tuning sliding mode fuzzy controller is stable model-free controller which eliminates the chattering phenomenon without to use the boundary layer saturation function. Lyapunov stability is proved in fuzzy-based tuning sliding mode fuzzy controller based on switching (sign) function. This controller has acceptable performance in presence of uncertainty (e.g., overshoot=0%, rise time=0.8 second, steady state error = 1e-9 and RMS error=1.8e-12).
PUMA-560 Robot Manipulator Position Sliding Mode Control Methods Using MATLAB...Waqas Tariq
This paper describes the MATLAB/SIMULINK realization, modeling and implementation of the PUMA 560 robot manipulator. This paper focuses on robot manipulator analysis and implementation and analyzed. This simulation models are developed as a part of a software laboratory to support and enhance graduate robotics courses, and MATLAB/SIMULINK courses at research and development company (SSP Co.) research center, Shiraz, Iran.
Design and Implementation of Sliding Mode Algorithm: Applied to Robot Manipul...Waqas Tariq
Refer to the research, review of sliding mode controller is introduced and application to robot manipulator has proposed in order to design high performance nonlinear controller in the presence of uncertainties. Regarding to the positive points in sliding mode controller, fuzzy logic controller and adaptive method, the output in most of research have improved. Each method by adding to the previous algorithm has covered negative points. Obviously robot manipulator is nonlinear, and a number of parameters are uncertain, this research focuses on comparison between sliding mode algorithm which analyzed by many researcher. Sliding mode controller (SMC) is one of the nonlinear robust controllers which it can be used in uncertainty nonlinear dynamic systems. This nonlinear controller has two challenges namely nonlinear dynamic equivalent part and chattering phenomenon. A review of sliding mode controller for robot manipulator will be investigated in this research.
PUMA-560 Robot Manipulator Position Computed Torque Control Methods Using MAT...Waqas Tariq
This document describes the implementation of a computed torque controller for controlling the position of a PUMA 560 robot manipulator using MATLAB/Simulink. It first presents the dynamic equations of motion for the PUMA 560 robot. It then provides details on computed torque control, including its mathematical formulation and how it was modeled in Simulink. Simulation results are presented to validate the controller's performance in tracking desired joint positions for the robot.
On line Tuning Premise and Consequence FIS: Design Fuzzy Adaptive Fuzzy Slidi...Waqas Tariq
This document presents a study on tuning premise and consequence parts of fuzzy inference system rules online to design an adaptive fuzzy sliding mode controller for a robot manipulator. Classical sliding mode controllers are robust but suffer from chattering. Previous work has combined fuzzy systems and sliding mode control to address chattering and model uncertainties, but require large rule bases. The proposed method uses an adaptive law to tune fuzzy rule parameters online, ensuring moderate computational load while approximating the uncertain nonlinear system dynamics. Simulation results will demonstrate the effectiveness of the approach.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
IRJET- Singular Identification of a Constrained Rigid RobotIRJET Journal
This document presents a singular identification procedure for identifying the parameters of a constrained rigid robot model. It begins with describing the constrained robot model and how it can be represented as a singular system. It then discusses singular equivalency, in particular strong equivalency, which transforms the original singular system into an equivalent regular state space model. This is important to reduce the number of initial conditions and improve identification. The document proposes using recursive least squares identification on the strongly equivalent model to identify the robot parameters. Simulation results on a robot arm model show that this approach provides significantly better parameter estimation convergence and output tracking compared to previous identification techniques for constrained robot models.
IRJET- Domestic Water Conservation by IoT (Smart Home)IRJET Journal
This document discusses singular system identification for a constrained rigid robot model. It begins by introducing constrained robot models and noting they can be considered singular systems. It then discusses the importance of singular system equivalency in identification, as an inappropriate equivalency can cause large errors. The document proposes using strong equivalency to transform the constrained robot model before identification. It applies recursive least squares identification to the strongly equivalent system. Simulation results show this approach improves identification error convergence and output tracking compared to previous techniques for constrained robot models.
Intelligent swarm algorithms for optimizing nonlinear sliding mode controller...IJECEIAES
This document describes research into using intelligent swarm algorithms to optimize the parameters of a nonlinear sliding mode controller for a robot manipulator. Specifically, particle swarm optimization and social spider optimization were used to determine optimal values for the parameters of an integral sliding mode controller designed to control a 6 degree-of-freedom PUMA robot manipulator. Simulation results showed that social spider optimization achieved the best fitness value and performance in minimizing error for the robot controller parameters.
Digital Implementation of Fuzzy Logic Controller for Real Time Position Contr...IOSR Journals
Abstract: Fuzzy Logic Controller (FLC) systems have emerged as one of the most promising areas for Industrial Applications. The highly growth of fuzzy logic applications led to the need of finding efficient way to hardware implementation. Field Programmable Gate Array (FPGA) is the most important tool for hardware implementation due to low consumption of energy, high speed of operation and large capacity of data storage. In this paper, instead of an introduction to fuzzy logic control methodology, we have demonstrated the implementation of a FLC through the use of the Very high speed integrated circuits Hardware Description Language (VHDL) code. FLC is designed for position control of BLDC Motor. VHDL has been used to develop FLC on FPGA. A Mamdani type FLC structure has been used to obtain the controller output. The controller algorithm developed synthesized, simulated and implemented on FPGA Spartan 3E board. Keywords – BLDC Motor, FLC, Hardware Implementation, Spartan3 FPGA, VHDL
This paper deals with a comparative study of circle criterion based nonlinear observer and H∞ observer for induction motor (IM) drive. The advantage of the circle criterion approach for nonlinear observer design is that it directly handles the nonlinearities of the system with less restriction conditions in contrast of the other methods which attempt to eliminate them. However the H∞ observer guaranteed the stability taking into account disturbance and noise attenuation. Linear matrix inequality (LMI) optimization approach is used to compute the gains matrices for the two observers. The simulation results show the superiority of H∞ observer in the sense that it can achieve convergence to the true state, despite the nonlinearity of model and the presence of disturbance.
IRJET- Design and Fabrication of PLC and SCADA based Robotic Arm for Material...IRJET Journal
This document describes the design and fabrication of a PLC and SCADA-controlled robotic arm for material handling. The robotic arm uses pneumatic cylinders connected by joints to move along three axes (X, Y, and Z). A mechanical gripper is attached to the end of the arm to grip objects on a conveyor belt. The movements of the pneumatic cylinders and gripper are controlled by a PLC based on sensor inputs from the conveyor belt. The PLC and robotic arm are integrated with a SCADA system for centralized control and monitoring. The robotic arm is intended to automate repetitive picking and placing tasks to reduce labor costs compared to manual operations.
Impact analysis of actuator torque degradation on the IRB 120 robot performan...IJECEIAES
Actuators in a robot system may become faulty during their life cycle. Locked joints, free-moving joints, and the loss of actuator torque are common faulty types of robot joints where the actuators fail. Locked and free-moving joint issues are addressed by many published articles, whereas the actuator torque loss still opens attractive investigation challenges. The objectives of this study are to classify the loss of robot actuator torque, named actuator torque degradation, into three different cases: Boundary degradation of torque, boundary degradation of torque rate, and proportional degradation of torque, and to analyze their impact on the performance of a typical 6-DOF robot (i.e., the IRB 120 robot). Typically, controllers of robots are not pre-designed specifically for anticipating these faults. To isolate and focus on the impact of only actuator torque degradation faults, all robot parameters are assumed to be known precisely, and a popular closed-loop controller is used to investigate the robot’s responses under these faults. By exploiting MATLAB-the reliable simulation environment, a simscape-based quasi-physical model of the robot is built and utilized instead of an actual expensive prototype. The simulation results indicate that the robot responses cannot follow the desired path properly in most fault cases.
Synthesis of the Decentralized Control System for Robot-Manipulator with Inpu...ITIIIndustries
It is discussed the problem of synthesis of the decentralized adaptive-periodic control system for two degrees of freedom robotic manipulator which have an input limitations. The solution of the problem is based on the use of hyperstability criterion, L-dissipativity conditions and dynamic filter-corrector.
Fractional-order sliding mode controller for the two-link robot arm IJECEIAES
This study presents a control system of the two-link robot arm based on the sliding mode controller with the fractional-order. Firstly, the equations of the two-link robot arm are analyzed, then the author proposes the controller for each joint based on these equations. The controller is a sliding mode controller with its order is not an integer value. The task of the control system is controlling the torques acted on the joints so that the response angle of each link equal to the desired angle. The effectiveness of the proposed control system is demonstrated through Matlab-Simulink software. The robot model and controller are built for investigating the efficiency of the system. The result shows that the system quality is very good: there is not the chattering phenomenon of torques, the response angle of two links always follow the desired angle with the short transaction time and the static error of zero.
Optimal Design of Super Twisting Control with PSO Algorithm for Robotic Manip...CSCJournals
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Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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The chapter Lifelines of National Economy in Class 10 Geography focuses on the various modes of transportation and communication that play a vital role in the economic development of a country. These lifelines are crucial for the movement of goods, services, and people, thereby connecting different regions and promoting economic activities.
Lifelines of National Economy chapter for Class 10 STUDY MATERIAL PDF
A New Estimate Sliding Mode Fuzzy Controller for Robotic Manipulator
1. Farzin Piltan, F. Aghayari, M. Rashidian & M. Shamsodini
International Journal of Robotics and Automation (IJRA), Volume (3) : Issue (1) : 2012 45
A New Estimate Sliding Mode Fuzzy Controller for Robotic
Manipulator
Farzin Piltan SSP.ROBOTIC@yahoo.com
Industrial Electrical and Electronic
Engineering SanatkadeheSabze
Pasargad. CO (S.S.P. Co), NO:16
,PO.Code 71347-66773, Fourth floor
Dena Apr , Seven Tir Ave , Shiraz , Iran
Farid Aghayari SSP.ROBOTIC@yahoo.com
Industrial Electrical and Electronic
Engineering SanatkadeheSabze
Pasargad. CO (S.S.P. Co), NO:16
,PO.Code 71347-66773, Fourth floor
Dena Apr , Seven Tir Ave , Shiraz , Iran
Mohammad Reza Rashidian SSP.ROBOTIC@yahoo.com
Industrial Electrical and Electronic
Engineering SanatkadeheSabze
Pasargad. CO (S.S.P. Co), NO:16
,PO.Code 71347-66773, Fourth floor
Dena Apr , Seven Tir Ave , Shiraz , Iran
Mohammad Shamsodini SSP.ROBOTIC@yahoo.com
Industrial Electrical and Electronic
Engineering SanatkadeheSabze
Pasargad. CO (S.S.P. Co), NO:16
,PO.Code 71347-66773, Fourth floor
Dena Apr , Seven Tir Ave , Shiraz , Iran
Abstract
One of the most active research areas in field of robotics is control of robot manipulator because
this system has highly nonlinear dynamic parameters and most of dynamic parameters are
unknown so design an acceptable controller is the main goal in this work. To solve this challenge
position new estimation sliding mode fuzzy controller is introduced and applied to robot
manipulator. This controller can solve to most important challenge in classical sliding mode
controller in presence of highly uncertainty, namely; chattering phenomenon based on fuzzy
estimator and online tuning and equivalent nonlinear dynamic based on estimation. Proposed
method has acceptable performance in presence of uncertainty (e.g., overshoot=0%, rise
time=0.8 s, steady state error = 1e-9 and RMS error=0.0001632).
Keywords: Sliding Mode Controller, Fuzzy Logic Methodology, Estimation, Sliding Mode Fuzzy
Methodology, Robotic Manipulator.
1. INTRODUCTION, BACKGROUND and MOTIVATION
A robot is a machine that can be programmed to perform a variety of tasks it is divided into three
main groups: Robot manipulator, Mobile robot and Hybrid robot, which Robot Manipulator is a set
of rigid links interconnected by joints; it is divided into two main groups: open-chain manipulators
and closed-chain robot manipulators. A serial link robot is a sequence of joints and links which
begins with a base frame and ends with an end-effector. This type of robot manipulators,
comparing with the load capacitance is more weightily because each link must be supported the
2. Farzin Piltan, F. Aghayari, M. Rashidian & M. Shamsodini
International Journal of Robotics and Automation (IJRA), Volume (3) : Issue (1) : 2012 46
weights of all next links and actuators between the present link and end-effector[6]. Serial robot
manipulators have been used in automotive industry, medical application, and also in research
laboratories. PUMA 560 robot manipulator is a 6 DOF serial robot manipulator. It has many
applications in industrial and academic. From the control point of view, robot manipulator is
divided into two main subparts: Kinematics and Dynamics. Robot manipulator kinematics is
essential part to calculate the relationship between rigid bodies and end-effector without any
forces. Study of this part is fundamental to design a controller with acceptable performance, in
real situations and practical applications. Dynamic is the study of motion with regard to forces.
Dynamic modeling is important for control, mechanical design, and simulation. Dynamic
parameters are used to describe the behavior of systems, the relationship between displacement,
velocity and acceleration to forces acting on robot manipulator [1-2].
Some of robot manipulators which work in industrial processes are controlled by linear PID
controllers, but while system works with various parameters and hard nonlinearities this technique
is very tricky and it has some limitations such as working near the system operating point.
Nonlinear control methodology can deal with nonlinear equations in the dynamics of robotic
manipulators. Conventional nonlinear control methodology cannot provide good robustness for
controlling robot manipulators. The control system designer is often unsure of the exact value of
the robot manipulator dynamic parameters which describe the behavior of robot manipulator.
Sliding mode controller is an influential nonlinear controller to certain and uncertain systems
which it is based on nonlinear Lyapunov formulation and computes the required arm torques
using the nonlinear feedback control law. Sliding mode control theory for control of robot
manipulator was first proposed in 1978 by Young to solve the set point problem ( by
discontinuous method in the following form;
(1)
where is sliding surface (switching surface), for n-DOF robot manipulator,
is the torque of joint. Sliding mode controller is divided into two main sub controllers:
discontinues controller and equivalent controller . Discontinues controller causes an
acceptable tracking performance at the expense of very fast switching. In the theory of infinity fast
switching can provide a good tracking performance but it also can provide some problems (e.g.,
system instability and chattering phenomenon). After going toward the sliding surface by
discontinues term, equivalent term help to the system dynamics match to the sliding surface[1, 6].
When all dynamic and physical parameters are known or limitation unknown the controller works
superbly and output responses are good quality ; practically a large amount of systems have
unlimited or highly uncertainties and sliding mode controller with estimator methodology reduce
this kind of limitation. Conversely, pure sliding mode controller is used in many applications; it has
two important drawbacks namely; chattering phenomenon, and nonlinear equivalent dynamic
formulation in uncertain dynamic parameter. Chattering phenomenon can causes some problems
such as saturation and heat the mechanical parts of robot manipulators or drivers. To reduce or
eliminate the chattering, various papers have been reported by many researchers which
classified into two most important methods: boundary layer saturation method and estimated
uncertainties method [1]. In boundary layer saturation method, the basic idea is the discontinuous
method replacement by saturation (linear) method with small neighborhood of the switching
surface. This replacement caused to increase the error performance against with the
considerable chattering reduction. Slotine and Sastry have introduced boundary layer method
instead of discontinuous method to reduce the chattering[21]. Slotine has presented sliding mode
with boundary layer to improve the industry application [22]. Estimated uncertainty method used
in term of uncertainty estimator to compensation of the system uncertainties. It has been used to
solve the chattering phenomenon and also nonlinear equivalent dynamic. If estimator has an
acceptable performance to compensate the uncertainties, the chattering is reduced. Research on
estimated uncertainty to reduce the chattering is significantly growing as their applications such
as industrial automation and robot manipulator. For instance, the applications of artificial
3. Farzin Piltan, F. Aghayari, M. Rashidian & M. Shamsodini
International Journal of Robotics and Automation (IJRA), Volume (3) : Issue (1) : 2012 47
intelligence, neural networks and fuzzy logic on estimated uncertainty method have been
reported in [25-28]. Wang et al. [5] have proposed a simple fuzzy estimator controller beside the
discontinuous and equivalent control terms to reduce the chattering. Their design had three main
parts i.e. equivalent, discontinuous and fuzzy estimator tuning part which has reduced the
chattering very well.
Application of fuzzy logic to automatic control was first reported in [10], where, based on Zadeh's
proposition, Mamdani buit a controller for a steam engine and boiler combination by synthesizing
a set of linguistic expressions in the form of IF-THEN rules as follows: IF (system state) THEN
(control action), which will be referred to as "Mamdani controller' hereafter. In Mamdani's
controller the knowledge of the system state (the IF part) and the set of actions (the THEN part)
are obtained from the experienced human operators [11]. Fuzzy control has gradually been
recognized as the most significant and fruitful application for fuzzy logic. In the past three
decades, more diversified application domains for fuzzy logic controllers have been created,
which range from water cleaning process, home appliances such as air conditioning systems and
online recognition of handwritten symbols [10-15, 20, 36].
However sliding mode controller has an acceptable performance but when system has unlimited
uncertainty it cannot guarantee the best output performance so fuzzy logic method with applied
system estimation improve the output response. Many dynamic systems to be controlled have
unknown or varying uncertain parameters. For instance, robot manipulators may carry large
objects with unknown inertial parameters. Generally, the basic objective of control estimation is to
maintain performance of the closed-loop system in the presence of uncertainty (e.g., variation in
parameters of a robot manipulator). The above objective can be achieved by estimating the
uncertain parameters (or equivalently, the corresponding controller parameters) on-line, and
based on the measured system signals. The estimated parameters are used in the computation
of the control input. An adaptive system can thus be regarded as a control system with on-line
parameter estimation [3, 16-29]. In conventional nonlinear adaptive controllers, the controller
attempts to learn the uncertain parameters of particular structured dynamics, and can achieve
fine control and compensate for the structure uncertainties and bounded disturbances. On the
other hand, adaptive control techniques are restricted to the parameterization of known functional
dependency but of unknown Constance. Consequently, these factors affect the existing nonlinear
adaptive controllers in cases with a poorly known dynamic model or when the fast real-time
control is required. Adaptive control methodologies and their applications to the robot
manipulators have widely been studied and discussed in the following references [4-5, 16-45].
In this research we will highlight a new SISO estimate sliding mode fuzzy algorithm with
estimates the nonlinear dynamic part derived in the Lyapunov sense. This algorithm will be
analyzed and evaluated on robotic manipulators. Section 2, is served as a problem statements,
robot manipulator dynamics and introduction to the classical sliding mode controller with proof of
stability and its application to robot manipulator. Part 3, introduces and describes the
methodology algorithms and proves Lyapunov stability. Section 4 presents the simulation results
of this algorithm applied to a 3 degree-of-freedom robot manipulator and the final section is
describe the conclusion.
2. ROBOT MANIPULATOR DYNAMICS, PROBLEM STATEMENTS and
SLIDING MODE CONTROLLER FORMULATION
Robot Manipulator Dynamic Formulation: The equation of an n-DOF robot manipulator
governed by the following equation [1, 3, 16-28, 30, 38-40]:
(2)
Where τ is actuation torque, M (q) is a symmetric and positive define inertia matrix, is the
vector of nonlinearity term. This robot manipulator dynamic equation can also be written in a
following form:
(3)
4. Farzin Piltan, F. Aghayari, M. Rashidian & M. Shamsodini
International Journal of Robotics and Automation (IJRA), Volume (3) : Issue (1) : 2012 48
Where B(q) is the matrix of coriolios torques, C(q) is the matrix of centrifugal torques, and G(q) is
the vector of gravity force. The dynamic terms in equation (3) are only manipulator position. This is
a decoupled system with simple second order linear differential dynamics. In other words, the
component influences, with a double integrator relationship, only the joint variable ,
independently of the motion of the other joints. Therefore, the angular acceleration is found as to
be [3, 16-28]:
(4)
Sliding Mode Control: This technique is very attractive from a control point of view. The central
idea of sliding mode control (SMC) is based on nonlinear dynamic equivalent. It has assumed that
the desired motion trajectory for the manipulator , as determined, by a path planner. Defines
the tracking error as [4-9, 18, 21, 31-44]:
(5)
Where e(t) is error of the plant, is desired input variable, that in our system is desired
displacement, is actual displacement. Consider a nonlinear single input dynamic system of
the form [6]:
(6)
Where u is the vector of control input, is the derivation of , is the
state vector, is unknown or uncertainty, and is of known sign function. The control
problem is truck to the desired state; , and have an acceptable error
which is given by:
(7)
A time-varying sliding surface is given by the following equation:
(8)
where λ is the positive constant. To further penalize tracking error integral part can be used in
sliding surface part as follows:
(9)
The main target in this methodology is kept the sliding surface slope near to the zero.
Therefore, one of the common strategies is to find input outside of .
(10)
where ζ is positive constant.
If S(0)>0 (11)
To eliminate the derivative term, it is used an integral term from t=0 to t=
(12)
Where is the time that trajectories reach to the sliding surface so, suppose S(
defined as
(13)
and
(14)
Equation (14) guarantees time to reach the sliding surface is smaller than since the
trajectories are outside of .
5. Farzin Piltan, F. Aghayari, M. Rashidian & M. Shamsodini
International Journal of Robotics and Automation (IJRA), Volume (3) : Issue (1) : 2012 49
(15)
suppose S is defined as
(16)
The derivation of S, namely, can be calculated as the following;
(17)
suppose the second order system is defined as;
(18)
Where is the dynamic uncertain, and also since , to have the best
approximation , is defined as
(19)
A simple solution to get the sliding condition when the dynamic parameters have uncertainty is
the switching control law:
(20)
where the switching function is defined as
(21)
and the is the positive constant. Suppose by (10) the following equation can be written as,
(22)
and if the equation (14) instead of (13) the sliding surface can be calculated as
(23)
in this method the approximation of is computed as
(24)
Based on above discussion, the control law for a multi degrees of freedom robot
manipulator is written as:
(25)
Where, the model-based component is the nominal dynamics of systems and can be
calculate as follows:
(26)
Where [15-44]
,
6. Farzin Piltan, F. Aghayari, M. Rashidian & M. Shamsodini
International Journal of Robotics and Automation (IJRA), Volume (3) : Issue (1) : 2012 50
and
and is computed as;
(27)
where
The result scheme is shown in Figure 1.
Problem Statement: Even though, SMC is used in wide range areas but, pure SMC has the
following disadvantages; namely; chattering phenomenon and nonlinear equivalent dynamic
formulation in presence of structure and unstructured uncertain system. Proposed method
focuses on substitution fuzzy logic system applied to main controller (SMC) to compensate the
uncertainty in nonlinear dynamic equation to implement easily.
Proof of Stability: The proof of Lyapunov function can be determined by the following equations.
The dynamic formulation of robot manipulate can be written by the following equation
(28)
FIGURE 1: Block diagram of Sliding Mode Controller (SMC)
7. Farzin Piltan, F. Aghayari, M. Rashidian & M. Shamsodini
International Journal of Robotics and Automation (IJRA), Volume (3) : Issue (1) : 2012 51
the lyapunov formulation can be written as follows,
(29)
the derivation of can be determined as,
(30)
the dynamic equation of robot manipulator can be written based on the sliding surface as
(31)
it is assumed that
(32)
by substituting (31) in (30)
(33)
suppose the control input is written as follows
(34)
by replacing the equation (34) in (33)
(35)
it is obvious that
(36)
the Lemma equation in robot manipulator system can be written as follows
(37)
the equation (32) can be written as
(38)
therefore, it can be shown that
(39)
Consequently the equation (39) guaranties the stability of the Lyapunov equation.
3. METHODOLOGY: DESIGN A NEW STIMATE SLIDING MODE FUZZY
CONTROLLER
First Step, Design Sliding Mode Fuzzy Controller: In recent years, artificial intelligence theory
has been used in robotic systems. Neural network, fuzzy logic, and neuro-fuzzy are combined
with nonlinear methods and used in nonlinear, time variant, and uncertainty plant (e.g., robot
manipulator). This controller can be used to control of nonlinear, uncertain, and noisy systems.
This method is free of some model-based techniques that used in classical controllers. The main
reasons to use fuzzy logic technology are able to give approximate recommended solution for
unclear and complicated systems to easy understanding and flexible. Fuzzy logic provides a
method which is able to model a controller for nonlinear plant with a set of IF-THEN rules, or it can
identify the control actions and describe them by using fuzzy rules. Besides applying fuzzy logic in
the main controller of a control loop, it can be used to design adaptive control, tuning parameters,
working in a parallel with the classical and soft computing control method. The fuzzy inference
8. Farzin Piltan, F. Aghayari, M. Rashidian & M. Shamsodini
International Journal of Robotics and Automation (IJRA), Volume (3) : Issue (1) : 2012 52
mechanism provides a mechanism for referring the rule base in fuzzy set. There are two most
commonly method that can be used in fuzzy logic controllers, namely, Mamdani method and
Sugeno method, which Mamdani built one of the first fuzzy controller to control of system engine
and Michio Sugeno suggested to use a singleton as a membership function of the rule
consequent. The Mamdani fuzzy inference method has four steps, namely, fuzzification, rule
evaluation, aggregation of the rule outputs and defuzzification. Sugeno method is very similar to
Mamdani method but Sugeno changed the consequent rule base that he used the mathematical
function of the input rule base instead of fuzzy set [15-44].
Fuzzification: Fuzzification is used to determine the membership degrees for antecedent part
when x and y have crisp values. The first step in fuzzification is determine inputs and outputs
which, it has one input ( ) and one output ( ). The input is which measures the
summation of discontinuous and equivalent part in main controller. The second step is chosen an
appropriate membership function for inputs and output which, for simplicity in implementation and
also to have an acceptable performance the researcher is selected the triangular membership
function. The third step is chosen the correct labels for each fuzzy set which, in this research
namely as linguistic variable. The linguistic variables for input ( ) are; Negative Big (NB),
Negative Medium (NM), Negative Small (NS), Zero (Z), Positive Small (PS), Positive Medium
(PM), Positive Big (PB), and it is quantized in to thirteen levels represented by: -1, -0.83, -0.66, -
0.5, -0.33, -0.16, 0, 0.16, 0.33, 0.5, 0.66, 0.83, 1 and the linguistic variables to find the output are;
Large Left (LL), Medium Left (ML), Small Left (SL), Zero (Z), Small Right (SR), Medium Right
(MR), Large Right (LR) and it is quantized in to thirteen levels represented by: -6, -5, -4, -3, -2, -1,
0, 1, 2, 3, 4, 5, 6.
Fuzzy Rule Base and Rule Evaluation: The first step in rule base and evaluation is provide a
least structured method to derive the fuzzy rule base which, expert experience and control
engineering knowledge is used because this method is the least structure of the other one and the
researcher derivation the fuzzy rule base from the knowledge of system operate and/or the
classical controller. Design the rule base of fuzzy inference system can play important role to
design the best performance of fuzzy sliding mode controller, that to calculate the fuzzy rule base
the researcher is used to heuristic method which, it is based on the behavior of the control of robot
manipulator suppose that the fuzzy rules in this controller is [36];
F.R
1
: IF is NB, THEN is LL. (40)
The complete rule base for this controller is shown in Table 1. Rule evaluation focuses on
operation in the antecedent of the fuzzy rules in fuzzy sliding mode controller. This part is used
fuzzy operation in antecedent part which operation is used.
Aggregation of the Rule Output (Fuzzy Inference): There are several methodologies in
aggregation of the rule outputs that can be used in fuzzy logic controllers, namely, Max-Min
aggregation, Sum-Min aggregation, Max-bounded product, Max-drastic product, Max-bounded
sum, Max-algebraic sum and Min-max. Two most common methods that used in fuzzy logic
controllers are Max-min aggregation and Sum-min aggregation. Max-Min aggregation is used to
this work which the calculation is defined as follows;
(41)
Deffuzzification: The last step to design fuzzy inference in our sliding mode fuzzy controller is
defuzzification. This part is used to transform fuzzy set to crisp set, therefore the input for
defuzzification is the aggregate output and the output of it is a crisp number. There are several
methodologies in defuzzification of the rule outputs that can be used in fuzzy logic controllers but
two most common defuzzification methods are: Center of gravity method (COG) and Center of
area (COA) method. In this design the Center of gravity method is used and calculated by
the following equation [36];
(42)
9. Farzin Piltan, F. Aghayari, M. Rashidian & M. Shamsodini
International Journal of Robotics and Automation (IJRA), Volume (3) : Issue (1) : 2012 53
This table has 7 cells, and used to describe the dynamics behavior of sliding mode fuzzy
controller.
NB NM NS Z PS PM PB
LL ML SL Z SR MR LR
TABLE 1: Rule table
Figure 2 is shown the sliding mode fuzzy estimator controller based on fuzzy logic controller and
minimum rule base.
FIGURE2: Block Diagram of sliding mode Fuzzy estimator Controller with Minimum Rule Base
Second step; design sliding mode fuzzy estimator and applied an input fuzzy logic
methodology to online tuning: All conventional controller have common difficulty, they need to
find and estimate several nonlinear parameters. Tuning sliding mode fuzzy method can tune
duration nonlinear parameters automatically the scale parameters using artificial intelligence
method. To keep the structure of the controller as simple as possible and to avoid heavy
computation, in this design one input Mamdani fuzzy supervisor tuner is selected. In this method
the tuneable controller tunes the nonlinear uncertainty and removes the chattering in opt to
applied fuzzy logic method to previous fuzzy logic estimator. However pure inverse sliding mode
controller has satisfactory performance in a limit uncertainty but tune the performance of this
controller in highly nonlinear and uncertain parameters (e.g., robot manipulator) is a difficult work
which proposed methodology can solve above challenge by applied two fuzzy methodology; the
first one for estimation and the second one for online tuning. The lyapunov candidate formulation
for our design is defined by:
(43)
Where is positive coefficient,
Since is skew-symetric matrix, we can get
(44)
10. Farzin Piltan, F. Aghayari, M. Rashidian & M. Shamsodini
International Journal of Robotics and Automation (IJRA), Volume (3) : Issue (1) : 2012 54
From following two functions:
(45)
And
(46)
We can get:
(47)
Since; then
(48)
The derivative of V defined by;
(49)
suppose is defined as follows
(50)
Where and
(51)
Based on
(52)
11. Farzin Piltan, F. Aghayari, M. Rashidian & M. Shamsodini
International Journal of Robotics and Automation (IJRA), Volume (3) : Issue (1) : 2012 55
where is adaption law,
consequently can be considered by
(53)
If the minimum error can be defined by
(54)
is intended as follows
(55)
For continuous function , and suppose it is defined the fuzzy logic system in form of
the minimum approximation error is very small.
Figure 3 is shown the block diagram of proposed fuzzy online applied to sliding mode fuzzy
estimator controller.
FIGURE 3: Design fuzzy online sliding mode fuzzy estimator controller
12. Farzin Piltan, F. Aghayari, M. Rashidian & M. Shamsodini
International Journal of Robotics and Automation (IJRA), Volume (3) : Issue (1) : 2012 56
4 SIMULATION RESULTS
Pure sliding mode controller (SMC) and fuzzy online sliding mode fuzzy estimator controller
(ASMFC) are implemented in Matlab/Simulink environment. Tracking performance and
disturbance rejection is compared.
Tracking Performances: From the simulation for first, second and third trajectory without
any disturbance, it was seen that SMC and ASMFC have the about the same performance
because this system is worked on certain environment and in sliding mode controller also is a
robust nonlinear controller with acceptable performance. Figure 4 shows tracking performance
without any disturbance for SMC and ASMFC.
FIGURE 4: SMC Vs. ASMFC: applied to 3DOF’s robot manipulator
By comparing trajectory response trajectory without disturbance in SMC and ASMFC, it is found
that the SMFC’s overshoot (0%) is lower than IDC's (3.3%) and the rise time in both of controllers
are the same.
Disturbance Rejection: Figure 5 has shown the power disturbance elimination in SMC and
ASMFC. The main targets in these controllers are disturbance rejection as well as the remove the
chattering phenomenon. A band limited white noise with predefined of 40% the power of input
signal is applied to the SMC and SMFC. It found fairly fluctuations in SMC trajectory responses.
13. Farzin Piltan, F. Aghayari, M. Rashidian & M. Shamsodini
International Journal of Robotics and Automation (IJRA), Volume (3) : Issue (1) : 2012 57
FIGURE 5: SMC Vs. ASMFC in presence of uncertainty: applied to robot manipulator.
Among above graph relating to trajectory following with external disturbance, SMC has fairly
fluctuations. By comparing some control parameters such as overshoot and rise time it found that
the ASMFC’s overshoot (0%) is lower than SMC’s (12%), although both of them have about the
same rise time.
5 CONCLUSIONS
Refer to the research, a position online tuning sliding mode fuzzy estimator controller (ASMFC)
design and application to robot manipulator has proposed. This research is based on sliding mode
controller which eliminate the chattering phenomenon based on nonlinear fuzzy logic method and
estimator block, eliminate the uncertainty unknown nonlinearity part based on applied online
tuning fuzzy logic methodology in SMFC and reduce the uncertainty challenge based on fuzzy
logic methodology and estimate via fuzzy estimator method. As a result proposed method has
superior performance in presence of structure and unstructured uncertainty (e.g., overshoot=0%,
rise time=0.8 s, steady state error = 1e-9 and RMS error=0.0001632) and eliminate the chattering.
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