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.
IRJET- Hybrid Intelligent Controller for Interior Permanent Magnet Synchr...IRJET Journal
The document presents a hybrid neuro-fuzzy controller for speed control of an interior permanent magnet synchronous motor (IPMSM) drive. Initially, a genetic algorithm is used to optimize the parameters of a proportional-integral controller for different operating conditions. Then, a fuzzy basis function network is trained to tune the controller parameters online and provide robust speed control under various disturbances. The controller is implemented on a digital signal processor and tested on a 1-hp IPMSM. Simulation and experimental results show the controller provides accurate speed control during sudden load changes and speed command variations.
IRJET- A Performance of Hybrid Control in Nonlinear Dynamic Multirotor UAVIRJET Journal
This document summarizes a research paper that models and evaluates control techniques for multirotor unmanned aerial vehicles (UAVs). It begins with an abstract that outlines the paper's contributions: developing an accurate mathematical model of a multirotor UAV using Newton-Euler dynamics, developing nonlinear control algorithms based on this model, and comparing the performance of backstepping control, sliding mode control, and fuzzy logic control through simulation. The document then provides details of the multirotor dynamic model and each control approach, evaluating their ability to stabilize the system and reject disturbances. It concludes that hybrid control systems combining advantages of different methods should be considered.
Pick and Place Robotic Claw for Engineering ApplicationsIRJET Journal
This document describes a pick and place robotic claw system controlled by a programmable logic controller (PLC). The robotic claw uses stepper motors and sensors to pick up and move objects automatically. It discusses the hardware components, including the PLC, proximity sensors, and stepper motors. The robotic claw has applications in material handling, packaging, construction, and other industries. It allows for accurate and flexible automation of repetitive tasks while maximizing safety and efficiency. Potential future applications mentioned include high-speed pick and place of small items, handling of flexible packages, and palletizing/de-palletizing.
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.
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.
Simulation design of trajectory planning robot manipulatorjournalBEEI
Robots can be mathematically modeled with computer programs where the results can be displayed visually, so it can be used to determine the input, gain, attenuate and error parameters of the control system. In addition to the robot motion control system, to achieve the target points should need a research to get the best trajectory, so the movement of robots can be more efficient. One method that can be used to get the best path is the SOM (Self Organizing Maps) neural network. This research proposes the usage of SOM in combination with PID and Fuzzy-PD control for finding an optimal path between source and destination. SOM Neural network process is able to guide the robot manipulator through the target points. The results presented emphasize that a satisfactory trajectory tracking precision and stability could be achieved using SOM Neural networking combination with PID and Fuzzy-PD controller.The obtained average error to reach the target point when using Fuzzy-PD=2.225% and when using PID=1.965%.
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.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IRJET- Hybrid Intelligent Controller for Interior Permanent Magnet Synchr...IRJET Journal
The document presents a hybrid neuro-fuzzy controller for speed control of an interior permanent magnet synchronous motor (IPMSM) drive. Initially, a genetic algorithm is used to optimize the parameters of a proportional-integral controller for different operating conditions. Then, a fuzzy basis function network is trained to tune the controller parameters online and provide robust speed control under various disturbances. The controller is implemented on a digital signal processor and tested on a 1-hp IPMSM. Simulation and experimental results show the controller provides accurate speed control during sudden load changes and speed command variations.
IRJET- A Performance of Hybrid Control in Nonlinear Dynamic Multirotor UAVIRJET Journal
This document summarizes a research paper that models and evaluates control techniques for multirotor unmanned aerial vehicles (UAVs). It begins with an abstract that outlines the paper's contributions: developing an accurate mathematical model of a multirotor UAV using Newton-Euler dynamics, developing nonlinear control algorithms based on this model, and comparing the performance of backstepping control, sliding mode control, and fuzzy logic control through simulation. The document then provides details of the multirotor dynamic model and each control approach, evaluating their ability to stabilize the system and reject disturbances. It concludes that hybrid control systems combining advantages of different methods should be considered.
Pick and Place Robotic Claw for Engineering ApplicationsIRJET Journal
This document describes a pick and place robotic claw system controlled by a programmable logic controller (PLC). The robotic claw uses stepper motors and sensors to pick up and move objects automatically. It discusses the hardware components, including the PLC, proximity sensors, and stepper motors. The robotic claw has applications in material handling, packaging, construction, and other industries. It allows for accurate and flexible automation of repetitive tasks while maximizing safety and efficiency. Potential future applications mentioned include high-speed pick and place of small items, handling of flexible packages, and palletizing/de-palletizing.
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.
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.
Simulation design of trajectory planning robot manipulatorjournalBEEI
Robots can be mathematically modeled with computer programs where the results can be displayed visually, so it can be used to determine the input, gain, attenuate and error parameters of the control system. In addition to the robot motion control system, to achieve the target points should need a research to get the best trajectory, so the movement of robots can be more efficient. One method that can be used to get the best path is the SOM (Self Organizing Maps) neural network. This research proposes the usage of SOM in combination with PID and Fuzzy-PD control for finding an optimal path between source and destination. SOM Neural network process is able to guide the robot manipulator through the target points. The results presented emphasize that a satisfactory trajectory tracking precision and stability could be achieved using SOM Neural networking combination with PID and Fuzzy-PD controller.The obtained average error to reach the target point when using Fuzzy-PD=2.225% and when using PID=1.965%.
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.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Ziegler nichols pid controller for effective pay-load torque responses and ti...eSAT Journals
Abstract Robotic Technology is an imitation for human being’s. It is an electro mechanical modelling objects are important aspect of manipulator. A Manipulator is a machine able to drive the robot. This paper describes and investigates on effective pay-load torque responses, tip-vibrations. This paper presents modelling and simulation of the double link manipulator using the proposed PID Controller. Here the proposed Ziegler-Nichols proportional–integral–derivative controller (PID controller) initiates more advantageous in the view of better performance and flexible operation of manipulator. First, the electro mechanical object was modeled and simulated using State space technique. Here the torque responses and end tip vibrations are assigned by state space variables. The entire two link manipulator topology was investigated and modeled, simulated. The proposed control strategy carries a back –back feed forward controller can be used for flexible operation of manipulator. Here, the simulation was done by using M-File Technique in Control tool box of MATLAB. Keywords: Double Link Manipulator, Ziegler-Nichols PID Controller, Pay-Load Torque Responses, Tip-Vibrations, State space technique, back –back feed forward controller
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.
Induction motors are work-horse of the industry and major element in energy conversion. The replacement of the existing non-adjustable speed drives with the modern variable frequency drives would save considerable amount of electricity. A proper control scheme for variable frequency drives can enhance the efficiency and performance of the drive. This paper attempt to provide a rigorous review of various control schemes for the induction motor control and provides critical analysis and guidelines for the future research work. A detailed study of sensor based control schemes and sensor-less control schemes has been investigated. The operation, advantages, and limitations of the various control schemes are highlighted and different types of optimization techniques have been suggested to overcome the limitations of control techniques.
Artificial Control of PUMA Robot Manipulator: A-Review of Fuzzy Inference Eng...Waqas Tariq
One of the most important challenges in the field of robotics is robot manipulators control with acceptable performance, because these systems are multi-input multi-output (MIMO), nonlinear and uncertainty. Presently, robot manipulators are used in different (unknown and/or unstructured) situation consequently caused to provide complicated systems, as a result strong mathematical theory are used in new control methodologies to design nonlinear robust controller with acceptable performance (e.g., minimum error, good trajectory, disturbance rejection). Classical and non-classical methods are two main categories of robot manipulators control, where the conventional (classical) control theory uses the classical method and the non-classical control theory (e.g., fuzzy logic, neural network, and neuro fuzzy) uses the artificial intelligence methods. However both of conventional and artificial intelligence theories have applied effectively in many areas, but these methods also have some limitations. This paper is focused on review of fuzzy logic controller and applied to PUMA robot manipulator.
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.
If you are interested in industrial robot controllers, this site is the right place for you. You can find out the every aspect of robot controllers and figure out how to design and implement robot controllers. http://open-robotics.com/
This document presents a method for tuning the parameters of a PID controller for a brushless DC motor using particle swarm optimization. PSO is used to find the optimal proportional, integral and derivative gains to minimize error metrics for the motor's step response. The BLDC motor is modeled in Simulink. PSO searches through potential solutions in a multi-dimensional space to determine PID parameters that produce the best step response with minimal overshoot, rise time, settling time and steady state error. The results show the PSO-tuned PID controller achieves better dynamic performance than other methods.
Shared Steering Control between a Driver and an Automation: Stability in the ...paperpublications3
Abstract: Now-a-days the Automatic control has been increasingly implemented for vehicle control system. Especially the steering control is essential for preventing accidents. In the existing systems there is no fully automatic steering control and it has serious problems. When it is made automatic, the system complexity is more. So, the shared steering concept is used in the proposed system to avoid accidents. In this, the position of the road is found using the web camera installed in front of the vehicle which is connected to the PC installed with MATLAB. Using MATLAB the image is processed to check the road characteristics. This paper presents an advanced driver assistance system (ADAS) for lane keeping, together with an analysis of its performance and stability with respect to variations in driver behavior. The automotive ADAS proposed is designed to share control of the steering wheel with the driver in the best possible way. Its development was derived from an H2-Preview optimization control problem, which is based on a global driver–vehicle–road (DVR) system. The DVR model makes use of a cybernetic driver model to take into account any driver–vehicle interactions. Such a formulation allows 1) Considering driver assistance cooperation criteria in the control synthesis, 2) improving the performance of the assistance as a cooperative copilot, and 3) analyzing the stability of the whole system in the presence of driver model uncertainty. The developed assistance system improved lane-keeping performance and reduced the risk of a lane departure accident. Good results were obtained using several criteria for human–machine cooperation. Poor stability situations were successfully avoided due to the robustness of the whole system, in spite of a large range of driver model uncertainty.
This document summarizes a study that simulated the performance of a brushless direct current (BLDC) motor for use in an electrical system for an energy-efficient car. The simulation was conducted using Simulink MATLAB and showed that a BLDC motor producing 30 Nm of torque and 960 W of power at speeds of 100-900 rpm meets the design needs for an electric vehicle. The document discusses the regulations for the national Kontes Mobil Hemat Energi competition in Indonesia and the methods used in the simulation, including a proportional-integral-derivative control system.
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.
This document summarizes a research paper that proposes an adaptive PID speed controller for a brushless DC motor. The paper begins with an introduction to brushless DC motors and common speed control methods like PI, PID, fuzzy logic and PWM controllers. It then discusses developing an adaptive PID controller that combines a PID controller with an auto-tuning method. This allows the controller to adapt to changing system parameters. The paper describes modeling the BLDC motor and speed control systems in MATLAB/Simulink. Simulation results are presented and analyzed to verify the adaptive PID controller's performance. The adaptive PID controller is found to improve system adaptability compared to other control methods.
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).
Fuzzy Control of Yaw and Roll Angles of a Simulated Helicopter Model Includes...ijeei-iaes
Fuzzy logic controller (FLC) is a heuristic method by If-Then Rules which resembles human intelligence and it is a good method for designing Non-linear control systems. In this paper, an arbitrary helicopter model includes articulated manipulators has been simulated with Matlab SimMechanics toolbox. Due to the difficulties of modeling this complex system, a fuzzy controller with simple fuzzy rules has been designed for its yaw and roll angles in order to stabilize the helicopter while it is in the presence of disturbances or its manipulators are moving for a task. Results reveal that a simple FLC can appropriately control this system.
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.
Modeling and control of double star induction machine by active disturbance r...TELKOMNIKA JOURNAL
This paper aims to contribute to the modeling and control of the double star induction machine (DSIM) by a robust method called active disturbance rejection control (ADRC). The ADRC has become in the last decade one of the most important techniques of regulation. This method is based on the use of an ESO (Extended State Observer) which estimates in real-time and at the same time the external disturbances and the errors due to the variations of the parameters of the machine and to the uncertainties of modeling. The two stators of DSIM are powered by three-phase inverters based on transistors and MLI control and the entire system is modeled in Park's reference. We analyze in the Matlab/Simulink environment the dynamic behavior of the system and the different ADRC controllers under different operating conditions. The result has demonstrated the performance and effectiveness of the ADRC.
PSO APPLIED TO DESIGN OPTIMAL PD CONTROL FOR A UNICYCLE MOBILE ROBOTJaresJournal
In this work, we propose a Particle Swarm Optimization (PSO) to design Proportional Derivative
controllers (PD) for the control of Unicycle Mobile Robot. To stabilize and drive the robot precisely with
the predefined trajectory, a decentralized control structure is adopted where four PD controllers are used.
Their parameters are given simultaneously by the proposed algorithm (PSO). The performance of the
system from its desired behavior is quantified by an objective function (SE). Simulation results are
presented to show the efficiency of the method. ).
The results are very conclusive and satisfactory in terms of stability and trajectory tracking of unicycle
mobile robot
International Journal of Engineering Research and Development (IJERD)IJERD Editor
This document presents a comparison of PID and fuzzy PID controllers for position control of a DC motor. It first describes the modeling of a DC motor transfer function. It then provides details on designing a PID controller using Ziegler-Nichols tuning methods. A fuzzy PID controller is also designed using triangular membership functions for error and change in error inputs. Simulation results in MATLAB/Simulink show that the fuzzy PID controller provides better tracking of setpoint changes with less overshoot compared to the ZN-tuned PID controller. The fuzzy PID controller therefore demonstrates better performance for position control of DC motors.
Modeling and Control of a Spherical Rolling Robot Using Model Reference Adapt...IRJET Journal
The document describes modeling and control of a spherical rolling robot using model reference adaptive control (MRAC). It presents the mathematical model developed for the robot based on its nonholonomic constraints. MRAC is used to control the robot by adjusting controller parameters to minimize the error between the robot's actual output and the output of a reference model for ideal behavior. Simulation results show the proposed MRAC scheme eliminates steady state error and improves transient response without requiring knowledge of the robot's dynamic equations.
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.
Modeling the enablers for implementing ict enabled wireless control in industryIAEME Publication
This document discusses modeling the enablers for implementing ICT-enabled wireless control in industry using interpretive structural modeling (ISM). It identifies 11 key enablers for ICT-enabled wireless process control systems based on expert interviews. These enablers are analyzed using ISM to determine their relationships and relative importance. Structural matrices are developed and levels are partitioned to create a diagraph and final ISM model that can be used to prioritize actions to enhance high-impact enablers.
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.
Balancing a Segway robot using LQR controller based on genetic and bacteria f...TELKOMNIKA JOURNAL
A two-wheeled single seat Segway robot is a special kind of wheeled mobile robot, using it as a human transporter system needs applying a robust control system to overcome its inherent unstable problem. The mathematical model of the system dynamics is derived and then state space formulation for the system is presented to enable design state feedback controller scheme. In this research, an optimal control system based on linear quadratic regulator (LQR) technique is proposed to stabilize the mobile robot. The LQR controller is designed to control the position and yaw rotation of the two-wheeled vehicle. The proposed balancing robot system is validated by simulating the LQR using Matlab software. Two tuning methods, genetic algorithm (GA) and bacteria foraging optimization algorithm (BFOA) are used to obtain optimal values for controller parameters. A comparison between the performance of both controllers GA-LQR and BFO-LQR is achieved based on the standard control criteria which includes rise time, maximum overshoot, settling time and control input of the system. Simulation results suggest that the BFOA-LQR controller can be adopted to balance the Segway robot with minimal overshoot and oscillation frequency.
Ziegler nichols pid controller for effective pay-load torque responses and ti...eSAT Journals
Abstract Robotic Technology is an imitation for human being’s. It is an electro mechanical modelling objects are important aspect of manipulator. A Manipulator is a machine able to drive the robot. This paper describes and investigates on effective pay-load torque responses, tip-vibrations. This paper presents modelling and simulation of the double link manipulator using the proposed PID Controller. Here the proposed Ziegler-Nichols proportional–integral–derivative controller (PID controller) initiates more advantageous in the view of better performance and flexible operation of manipulator. First, the electro mechanical object was modeled and simulated using State space technique. Here the torque responses and end tip vibrations are assigned by state space variables. The entire two link manipulator topology was investigated and modeled, simulated. The proposed control strategy carries a back –back feed forward controller can be used for flexible operation of manipulator. Here, the simulation was done by using M-File Technique in Control tool box of MATLAB. Keywords: Double Link Manipulator, Ziegler-Nichols PID Controller, Pay-Load Torque Responses, Tip-Vibrations, State space technique, back –back feed forward controller
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.
Induction motors are work-horse of the industry and major element in energy conversion. The replacement of the existing non-adjustable speed drives with the modern variable frequency drives would save considerable amount of electricity. A proper control scheme for variable frequency drives can enhance the efficiency and performance of the drive. This paper attempt to provide a rigorous review of various control schemes for the induction motor control and provides critical analysis and guidelines for the future research work. A detailed study of sensor based control schemes and sensor-less control schemes has been investigated. The operation, advantages, and limitations of the various control schemes are highlighted and different types of optimization techniques have been suggested to overcome the limitations of control techniques.
Artificial Control of PUMA Robot Manipulator: A-Review of Fuzzy Inference Eng...Waqas Tariq
One of the most important challenges in the field of robotics is robot manipulators control with acceptable performance, because these systems are multi-input multi-output (MIMO), nonlinear and uncertainty. Presently, robot manipulators are used in different (unknown and/or unstructured) situation consequently caused to provide complicated systems, as a result strong mathematical theory are used in new control methodologies to design nonlinear robust controller with acceptable performance (e.g., minimum error, good trajectory, disturbance rejection). Classical and non-classical methods are two main categories of robot manipulators control, where the conventional (classical) control theory uses the classical method and the non-classical control theory (e.g., fuzzy logic, neural network, and neuro fuzzy) uses the artificial intelligence methods. However both of conventional and artificial intelligence theories have applied effectively in many areas, but these methods also have some limitations. This paper is focused on review of fuzzy logic controller and applied to PUMA robot manipulator.
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.
If you are interested in industrial robot controllers, this site is the right place for you. You can find out the every aspect of robot controllers and figure out how to design and implement robot controllers. http://open-robotics.com/
This document presents a method for tuning the parameters of a PID controller for a brushless DC motor using particle swarm optimization. PSO is used to find the optimal proportional, integral and derivative gains to minimize error metrics for the motor's step response. The BLDC motor is modeled in Simulink. PSO searches through potential solutions in a multi-dimensional space to determine PID parameters that produce the best step response with minimal overshoot, rise time, settling time and steady state error. The results show the PSO-tuned PID controller achieves better dynamic performance than other methods.
Shared Steering Control between a Driver and an Automation: Stability in the ...paperpublications3
Abstract: Now-a-days the Automatic control has been increasingly implemented for vehicle control system. Especially the steering control is essential for preventing accidents. In the existing systems there is no fully automatic steering control and it has serious problems. When it is made automatic, the system complexity is more. So, the shared steering concept is used in the proposed system to avoid accidents. In this, the position of the road is found using the web camera installed in front of the vehicle which is connected to the PC installed with MATLAB. Using MATLAB the image is processed to check the road characteristics. This paper presents an advanced driver assistance system (ADAS) for lane keeping, together with an analysis of its performance and stability with respect to variations in driver behavior. The automotive ADAS proposed is designed to share control of the steering wheel with the driver in the best possible way. Its development was derived from an H2-Preview optimization control problem, which is based on a global driver–vehicle–road (DVR) system. The DVR model makes use of a cybernetic driver model to take into account any driver–vehicle interactions. Such a formulation allows 1) Considering driver assistance cooperation criteria in the control synthesis, 2) improving the performance of the assistance as a cooperative copilot, and 3) analyzing the stability of the whole system in the presence of driver model uncertainty. The developed assistance system improved lane-keeping performance and reduced the risk of a lane departure accident. Good results were obtained using several criteria for human–machine cooperation. Poor stability situations were successfully avoided due to the robustness of the whole system, in spite of a large range of driver model uncertainty.
This document summarizes a study that simulated the performance of a brushless direct current (BLDC) motor for use in an electrical system for an energy-efficient car. The simulation was conducted using Simulink MATLAB and showed that a BLDC motor producing 30 Nm of torque and 960 W of power at speeds of 100-900 rpm meets the design needs for an electric vehicle. The document discusses the regulations for the national Kontes Mobil Hemat Energi competition in Indonesia and the methods used in the simulation, including a proportional-integral-derivative control system.
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.
This document summarizes a research paper that proposes an adaptive PID speed controller for a brushless DC motor. The paper begins with an introduction to brushless DC motors and common speed control methods like PI, PID, fuzzy logic and PWM controllers. It then discusses developing an adaptive PID controller that combines a PID controller with an auto-tuning method. This allows the controller to adapt to changing system parameters. The paper describes modeling the BLDC motor and speed control systems in MATLAB/Simulink. Simulation results are presented and analyzed to verify the adaptive PID controller's performance. The adaptive PID controller is found to improve system adaptability compared to other control methods.
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).
Fuzzy Control of Yaw and Roll Angles of a Simulated Helicopter Model Includes...ijeei-iaes
Fuzzy logic controller (FLC) is a heuristic method by If-Then Rules which resembles human intelligence and it is a good method for designing Non-linear control systems. In this paper, an arbitrary helicopter model includes articulated manipulators has been simulated with Matlab SimMechanics toolbox. Due to the difficulties of modeling this complex system, a fuzzy controller with simple fuzzy rules has been designed for its yaw and roll angles in order to stabilize the helicopter while it is in the presence of disturbances or its manipulators are moving for a task. Results reveal that a simple FLC can appropriately control this system.
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.
Modeling and control of double star induction machine by active disturbance r...TELKOMNIKA JOURNAL
This paper aims to contribute to the modeling and control of the double star induction machine (DSIM) by a robust method called active disturbance rejection control (ADRC). The ADRC has become in the last decade one of the most important techniques of regulation. This method is based on the use of an ESO (Extended State Observer) which estimates in real-time and at the same time the external disturbances and the errors due to the variations of the parameters of the machine and to the uncertainties of modeling. The two stators of DSIM are powered by three-phase inverters based on transistors and MLI control and the entire system is modeled in Park's reference. We analyze in the Matlab/Simulink environment the dynamic behavior of the system and the different ADRC controllers under different operating conditions. The result has demonstrated the performance and effectiveness of the ADRC.
PSO APPLIED TO DESIGN OPTIMAL PD CONTROL FOR A UNICYCLE MOBILE ROBOTJaresJournal
In this work, we propose a Particle Swarm Optimization (PSO) to design Proportional Derivative
controllers (PD) for the control of Unicycle Mobile Robot. To stabilize and drive the robot precisely with
the predefined trajectory, a decentralized control structure is adopted where four PD controllers are used.
Their parameters are given simultaneously by the proposed algorithm (PSO). The performance of the
system from its desired behavior is quantified by an objective function (SE). Simulation results are
presented to show the efficiency of the method. ).
The results are very conclusive and satisfactory in terms of stability and trajectory tracking of unicycle
mobile robot
International Journal of Engineering Research and Development (IJERD)IJERD Editor
This document presents a comparison of PID and fuzzy PID controllers for position control of a DC motor. It first describes the modeling of a DC motor transfer function. It then provides details on designing a PID controller using Ziegler-Nichols tuning methods. A fuzzy PID controller is also designed using triangular membership functions for error and change in error inputs. Simulation results in MATLAB/Simulink show that the fuzzy PID controller provides better tracking of setpoint changes with less overshoot compared to the ZN-tuned PID controller. The fuzzy PID controller therefore demonstrates better performance for position control of DC motors.
Modeling and Control of a Spherical Rolling Robot Using Model Reference Adapt...IRJET Journal
The document describes modeling and control of a spherical rolling robot using model reference adaptive control (MRAC). It presents the mathematical model developed for the robot based on its nonholonomic constraints. MRAC is used to control the robot by adjusting controller parameters to minimize the error between the robot's actual output and the output of a reference model for ideal behavior. Simulation results show the proposed MRAC scheme eliminates steady state error and improves transient response without requiring knowledge of the robot's dynamic equations.
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.
Modeling the enablers for implementing ict enabled wireless control in industryIAEME Publication
This document discusses modeling the enablers for implementing ICT-enabled wireless control in industry using interpretive structural modeling (ISM). It identifies 11 key enablers for ICT-enabled wireless process control systems based on expert interviews. These enablers are analyzed using ISM to determine their relationships and relative importance. Structural matrices are developed and levels are partitioned to create a diagraph and final ISM model that can be used to prioritize actions to enhance high-impact enablers.
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.
Balancing a Segway robot using LQR controller based on genetic and bacteria f...TELKOMNIKA JOURNAL
A two-wheeled single seat Segway robot is a special kind of wheeled mobile robot, using it as a human transporter system needs applying a robust control system to overcome its inherent unstable problem. The mathematical model of the system dynamics is derived and then state space formulation for the system is presented to enable design state feedback controller scheme. In this research, an optimal control system based on linear quadratic regulator (LQR) technique is proposed to stabilize the mobile robot. The LQR controller is designed to control the position and yaw rotation of the two-wheeled vehicle. The proposed balancing robot system is validated by simulating the LQR using Matlab software. Two tuning methods, genetic algorithm (GA) and bacteria foraging optimization algorithm (BFOA) are used to obtain optimal values for controller parameters. A comparison between the performance of both controllers GA-LQR and BFO-LQR is achieved based on the standard control criteria which includes rise time, maximum overshoot, settling time and control input of the system. Simulation results suggest that the BFOA-LQR controller can be adopted to balance the Segway robot with minimal overshoot and oscillation frequency.
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.
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.
Path tracking control of differential drive mobile robot based on chaotic-bi...IJECEIAES
This summary provides the key details about the document in 3 sentences:
The document presents a new chaotic-billiards optimizer (C-BO) algorithm to optimize the parameters of a controller for a differential-drive mobile robot. The C-BO algorithm is used to tune the controller parameters to improve path tracking performance. Simulation results show that the C-BO algorithm achieves better path tracking accuracy compared to an ant colony optimization algorithm, with a steady state error of 0.6% versus 0.8%.
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).
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.
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.
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.
Optimal Design of Super Twisting Control with PSO Algorithm for Robotic Manip...CSCJournals
Robotic manipulators are nonlinear and coupling systems exposing to external disturbance. They are used in wide industrial applications; the suitable selection of a nonlinear robust controller is required. Sliding Mode Controller (SMC) was designed to achieve these requirements, but unfortunately the chattering phenomenon was the main drawback of the conventional SMC. It leads to destructive of some components of a real system and subsequent loss in its accuracy. Hence, the design of Super-Twisting Controller (STC) is suggested for chattering elimination. In previous literatures, the accomplishment of the manual adjustment for the parameters of STC was a large burden and time consuming process. Therefore, a new combination of Particle Swarm Optimization (PSO) algorithm with STC is proposed for optimal tuning of STC parameters. The simulation results demonstrate the superiority of the super twisting technique for chattering mitigation comparing to the conventional SMC. Also, STC tuned via PSO proves its effectiveness and robustness to different types of external disturbances without the needs for the knowledge of their upper boundary values. Besides, the performance of the controlled system is faster and more accurate in the criteria of overshoot, settling time and rise time compared to the manual adjusting of super twisting controllers.
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.
A New Estimate Sliding Mode Fuzzy Controller for Robotic ManipulatorWaqas Tariq
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).
This document introduces the fuzzy model reference learning control (FMRLC) method. FMRLC uses a reference model to provide feedback to modify the membership functions of a fuzzy controller. This allows the closed-loop system to behave like the reference model and achieve the desired performance. The effectiveness of FMRLC is demonstrated through its application to rocket velocity control and robot manipulator control. FMRLC can achieve high performance learning control for nonlinear, time-varying systems.
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.
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).
Adaptive Control strategies helps to get desirable output for system with partial unknown dynamics or systems having unknown and unmodeled load variation. DC servo motors are useful to track rapid speed trajectory for various applications, particularly with need of high starting torque and low inertia. Model Reference Adaptive Control (MRAC) parameter data of results with Lyapunov stability MRAC has been used to generate adaptation parameter for DC motor speed controller.
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.
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.
Fuzzy rules incorporated skyhook theory based vehicular suspension design for...IJERA Editor
The vehicle suspension system supports and isolate the vehicle body and payload from road vibrations due to surface roughness by maintaining a controllable damping traction force between tires and road surface. In modern luxury vehicles semi active suspension system are offering both the reliability and accuracy that has enhanced the passenger ride comfort with less power demand. In this paper we have proposed the design of a hybrid control system having a combination of skyhook theory with fuzzy logic control and applied on a semi-active vehicle suspension system for its ride comfort enhancement. A two degree of freedom dynamic model is simulated using Matlab/Simulink for a vehicle equipped with semi-active suspension system with focused on the passenger‟s ride comfort performance.
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.
Similar to Intelligent swarm algorithms for optimizing nonlinear sliding mode controller for robot manipulator (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
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Intelligent swarm algorithms for optimizing nonlinear sliding mode controller for robot manipulator
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 11, No. 5, October 2021, pp. 3943~3955
ISSN: 2088-8708, DOI: 10.11591/ijece.v11i5.pp3943-3955 3943
Journal homepage: http://ijece.iaescore.com
Intelligent swarm algorithms for optimizing nonlinear sliding
mode controller for robot manipulator
Suhad Qasim G. Haddad1
, Hanan A. R. Akkar2
1
Department of Computer Engineering, University of Technology, Iraq, Baghdad, Baghdad, Iraq
2
Department of Electrical Engineering, University of Technology, Iraq, Baghdad, Baghdad, Iraq
Article Info ABSTRACT
Article history:
Received Nov 28, 2020
Revised Mar 17, 2021
Accepted Mar 27, 2021
This work introduces an accurate and fast approach for optimizing the
parameters of robot manipulator controller. The approach of sliding mode
control (SMC) was proposed as it documented an effective tool for designing
robust controllers for complex high-order linear and nonlinear dynamic
systems operating under uncertain conditions. In this work Intelligent particle
swarm optimization (PSO) and social spider optimization (SSO) were used
for obtaining the best values for the parameters of sliding mode control
(SMC) to achieve consistency, stability and robustness. Additional design of
integral sliding mode control (ISMC) was implemented to the dynamic
system to achieve the high control theory of sliding mode controller. For
designing particle swarm optimizer (PSO) and social spider optimization
(SSO) processes, mean square error performances index was considered. The
effectiveness of the proposed system was tested with six degrees of freedom
robot manipulator by using (PUMA) robot. The iteration of SSO and PSO
algorithms with mean square error and objective function were obtained,
with best fitness for (SSO =4.4876 𝑒−6 and (PSO)=3.4948 𝑒−4.
Keywords:
Artificial intelligence
Particle swarm optimizer
Sliding mode control
Social spider optimizer
Swarm intelligence
This is an open access article under the CC BY-SA license.
Corresponding Author:
Suhad Qasim G. Haddad
Department of Computer Engineering
University of Technology, Al Sinaa Street, Baghdad, Iraq
Email: suhadhadad.sh@gmail.com
1. INTRODUCTION
Programming the multi degrees of freedom (DOF) of robot arms in order to maximize their
functionality has recently remained a challenging job, time-consuming and costly operation in particular.
Indeed, the coupling effects of multiple joints must be taken into account by controlling robot manipulator
parameters [1]. In this work, programmable universal manipulation arm (PUMA) robot, which has six
degrees of freedom (6-DOF), all are rotary joints with serial connections, was used as a study case. The first
three joints are used to control the robot's handle position. The second three joints are to obtain the
orientation of the robot's wrist locus. PUMA robot manipulator is widely used in medical, automotive,
education and other important applications. The parameters and dimensions of this robot were all known and
documented in different literatures [2]. Figure 1 illustrates the global model of PUMA robot the 6-DOF
manipulator.
Designing a stable and strong controller is an important part for sensitive and various applications
for robot manipulator. The most common nonlinear model-based controller is sliding mode controller (SMC)
which has been properly applied in various applications such as motor control, space system, automatic flight
control and finally robot control. It is considered as a powerful advance stable robotic manipulator control
system that can achieve and solve the most important tasks in control systems, which are robustness and
2. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 11, No. 5, October 2021 : 3943 - 3955
3944
stability [3]. Integral sliding mode controller (ISMC) was suggested in this work to boost the manipulator
output to accomplish the desired tasks with high stability.
Figure 1. The global model of puma 560 robot manipulator
The essential benefits of the SMC method are achieving of asymptotic reactivity and stability in
implementation regarding to all notched internal and external uncertainties and disturbances. The SMC can
be split into two key components, which are the discontinuous part (𝜏𝑑𝑖𝑠) that’s used to design appropriate
tracking performance centered on linear methodology requiring identical fast switching. But it is caused to
system instability and the occurrence of chatter. The second part is equivalent controller (𝜏𝑒𝑞) which is the
effect of nonlinear terms that induced reliability and used to fine-tune the sliding surface slopes [4]. In
addition, SMC has two major disadvantages, namely are chattering phenomenon in control response, and
nonlinear equivalent analogous functional formulation in indeterminate and uncertain dynamic parameters [5].
The rising in switching frequency of the control in discontinuous issue happened due to a phenomenon called
(chattering), which can be considered as an unwanted feature that appear in the control behavior of SMC. It is
a limited value with restricted frequency of oscillation that can cause some severe mechanical problems, such
as distortion and heating of robot manipulator mechanical components. This chattering influences on the
stability of the system, due to this reason chattering effect should be cancelled or minimized. So, adjusting
the sliding mode control parameters is an important part to reduce the chattering disadvantage and
developing a stable coefficient for nonlinear controllers [6]. In recent times, artificial intelligence (AI)
techniques have been implemented to overcome the disadvantage in conventional controller design, which is
an effective tool for solving many applications with intelligent techniques. These innovative approaches are
methods of biological inspired optimization, which can randomly transfer a solution of one candidate into a
new one with better fitness function [7]. Researchers have been obsessed with insect or animal species'
collective intelligent actions in nature, such as bird flocks, ant colonies, fish schools, bee swarms, where the
relation between the collection of insects or animals is referred to as swarm behaviour. This division of AI is
often referred to as swarm intelligence (SI), which deals with the mutual behavior of swarms by the active
coordination and interaction of individuals without supervision. SI provides a benefits that including
scalability, fault tolerance, flexibility, speed, mobility, autonomy, parallelism, and adaptation [8]. In this
work, particle swarm optimization (PSO), and social spider optimization (SSO), which are an efficient
algorithms of SI, were suggested to adjust the nonlinear coefficients and optimizing the parameters of the
proposed controllers. These algorithms can create the perfect solution, with a high quality and a lower
computing time than other techniques, with progressively stable assembly characteristics, they efficiently
change the parameters with sufficient convergence to achieve impressive acceleration of the system [9].
This paper has been arranged accordingly. Some related works are presented in the second section.
The third section deals with the analysis, modelling and simulation of the dynamic model of PUMA robot.
Section four concentrates on the strategy of controller model, and the method that used in designing SMC
and ISMC for controlling the dynamic part of the robot. Section 5 discusses the method of PSO and SSO
3. Int J Elec & Comp Eng ISSN: 2088-8708
Intelligent swarm algorithms for optimizing nonlinear sliding mode … (Suhad Qasim G. Haddad)
3945
algorithms. The outcome of the debate and simulation is shown in the sixth section. The last section in the
paper introduces the conclusion and future work.
2. LITERATURE REVIEW
Researchers have been working hard to control the robot manipulator and to create best solutions for
the dynamic model, to obtain stable response with minimum steady state error. Several controllers have been
developed with various SI algorithms. A concise summary of previous related works is shown in this section.
In [10] mentioned that to solve the trajectory monitoring problem for a nonlinear design of wheeled robot,
PSO has been used to get the best values for the parameters of sliding mode control. In [11], the robotic
device stability was demonstrated using Lyapunov impedance based technique, with PSO algorithm for
optimization the parameters of the control. Jalali [12] introduced an adaptive sliding-mode controller with
PSO algorithm for optimizing PUMA robot manipulator, this controller can respond and adapt itself to
change system parameters according to the external intervention. PSO algorithm was used to enhance the
parameters of the sliding function and minimize the chattering action. Boundjou [13] developed a stable
adaptive controller with Particle Swarm Optimizer for automatic and systematic tuning of PID gains for two
degrees of freedom robot arm, thus optimizing the cost function of the parameters. In [14], evaluating of
SMC efficiency with PID was done, taking into account the ziegler-nichols (ZN) and the PSO tuning
algorithm implemented in the sliding surface. The computational and simulation results showed that
compared to traditional ZN variables the SMC is able to do well with the PSO variables added to the sliding
board.
PID controller auto-tuning system was implemented for PUMA robot manipulators [15]. Two
methods of SI multi-objective optimization were tested, namely multi objective cuckoo scan (MOCS) and
multi objective particle swarm optimization (MOPSO). Comparison was made between the results of the two
algorithms in the case of achieving a predefined trajectory with a reasonable tracking accuracy. Sliding mode
controller with boundary layer was presented for dynamic design with control of 6-DOF robot arm
manipulator type IRB-120 [16]. The chattering that generates unreliable signal with high frequency
oscillation in the sliding mode control was eliminated by using the boundary layer which provides the
controller with stabilization and improve the total performance. New technique was presented for robot
manipulators with a robust high degree composite, super-twisting of sliding mode controller (HOS-TSMC)
[17]. The suggested approach extends the traditional and conventional controller of sliding mode (TSMC)
and the approximate state of sliding mode (ESMC) to ameliorate the chatter. To minimize the influence of
numerous external disturbances, the non-linear simulator of the saturation integral backstepping controller
was implemented and the particle swarm algorithm was used to evaluate the controller parameters [18].
Three types of external disturbances (constant, intermittent, and random disturbances) were used separately
during the quadrotor aircraft's flight control. The outcome of the simulation reveals that in anti-disturbance
power, the saturation integral backstepping controller was much stronger than the classical backstepping
controller and integral back stepping controller. A modern adaptive terminal sliding mode controller with a
non-single specialty was used to control the behavior of quad rotor flight [19]. Besides, the controller of
backstepping sliding mode, was introduced to control the path of the quadrotor, despite the influence of
external disturbances, this leads to quick and reliable observation. The design process was illustrated with
Lyapunov principle. Then, the proposed controllers of backstepping, integral sliding mode and the second
sliding mode, were investigate with several flights checks. Where the simulation result shows an
improvement in the convergence of time, with good robustness and chattering free for control efforts.
Akkar and Haddad [4] introduced a strategy for tuning three types of dynamic control techniques
that were developed for the PUMA 560 robot manipulator, which are proportional integral derived (PID),
sliding mode control (SMC), and the modified type of integral sliding mode control (ISMC). Intelligent
particle swarm optimization (PSO) has been suggested to achieve the best parameters in order to maximize
the parameters in the proposed strategies. The PSO optimization approach has improved the parameters of
each controller according to the initial values of the particles, such as their swarm size and initial velocity.
3. CONTRIBUTION
In this work, based on the theory of SMC, an integral optimal control law was designed for the 6-
DOF PUMA robot manipulator. The integral sliding mode controller (ISMC) was proposed to improve the
manipulator output to perform the desired tasks with high stability. Then, particle swarm algorithm for
optimization (PSO), and social spider optimization (SSO) algorithm, were suggested to adjust the nonlinear
coefficients and optimizing the parameters of the ISMC. Where, adjusting the integral sliding mode control
parameters is an important part to reduce the chattering disadvantage and developing a stable coefficient for
nonlinear controllers.
4. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 11, No. 5, October 2021 : 3943 - 3955
3946
4. DYNAMIC DESIGN OF PUMA ROBOT MANIPULATOR
The dynamic equation of multi DOF for PUMA robot manipulator is about the analysis of motion
concerning forces. Dynamic design modelling is required for mechanical part model, control and finally in
implementing the simulation. It is used to define the parameters of the dynamic and also to explain the
relationship between displacement, distance, acceleration and the force that acts on the manipulator of the
robot. Lagrange-Euler mathematical model of the robot is used for calculation the control laws and design the
dynamic system of the robot. In of a multi DOF for robot manipulator is calculate is being as [20], [21]:
𝐴(գ)գ̇ + 𝑁(գ գ ) = 𝜏 (1)
Dynamic equation as an outcome for the robot can be described is being as:
𝑁 (գ գ ) = 𝑉(գ գ) + 𝐺(գ) (2)
𝑉(գ գ) = 𝐵(գ) [գ գ] + 𝐶(գ) (գ)2
(3)
𝜏 = 𝐴(գ) գ̇ + 𝐵(գ)[գ գ̇] +𝐶(գ)(գ)2
+ 𝐺(գ) (4)
where: 𝐴(գ):Symmetric positive matrix considered for kinetic energy and inertia matrix,with 𝑛𝑥𝑛 dimension.
𝐵(գ): is for Coriolis torques matrix, with 𝑛𝑥𝑛 (𝑛 − 1)/2 dimension. 𝐶(գ) : is for centrifugal torques, with
nxn dimension. 𝐺(գ) : is for gravity torques, with nx1 dimension. գ: is the joint position (or joint angle), for
գ = [գ1, գ2, … գ𝑛 ], գ̇:is consider as n- vector for joint velocities, գ̈: is consider as n- vector of accelerations.
And τ: is consider as the joint force vector (torque). [գ̇2]: that can a vector given by [գ̇1
2
, գ̇2
2
, … … գ̇𝑛
2 ]𝑇
, [գ̇գ̇]:
that can a vector given :[գ̇1գ̇2, գ̇1գ̇3, . . գ̇1գ̇𝑛, գ̇2գ̇3 … . ]𝑇
. The input of the dynamic system is torque matrix in
the robotic manipulator arrangement while the outcomes are real variables displacement and joints, as a result
it can be written is being as:
գ̈= 𝐴−1(գ). [𝜏 − 𝑁 (գ գ̇)] (5)
All the parameters of the forward and inverse kinematics and matrix of PUMA robot have been
computed as mentioned in [2], [22], [23]. In this work only the first, second and third links of PUMA robot
manipulator will be taken into consideration. Figure 2 illustrates the block diagram for dynamic and
kinematics model for PUMA robot manipulator.
Figure 2. Block diagram for dynamic and kinematics model for PUMA robot
5. METHOD OF DESIGNING SLIDING MODE CONTROL SMC
SMC is applied to impose the system’s state path to pass through the sliding surface, then imposes
the state's system path to ''slide" along the switching surface till it stays on the origin [4]. In order to define
the equation that control the total design of SMC, the nonlinear input single for dynamic system define is
being as:
𝑍𝑛= 𝛿 (Ζ) +𝑏(Z)𝜐(𝑡) (6)
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where (υ) is consider as the vector of the control input , 𝑍𝑛 is the 𝑛𝑡ℎ derivative of the state vector 𝑍. δ is
consider as unknown nonlinear function or uncertainty of dynamic, 𝑏(Z) is switching [𝑆𝐼𝐺𝑁] function. The
key objective of designing SMC is to train the appropriate state desire position 𝑍𝑑 according to the actual
joint variables, the trucking error vector will be defined is being as [3], [5].
𝑒 =Z
̃ = Ζ – 𝑍𝑑 (7)
where: Ζ is for real and actual position, 𝑍𝑑 is the desired position, and 𝑍
̃ : is for estimated trucking error
vector. According to the theory of the sliding mode controller, sliding surface is the key important part to
design this controller, the calculation of time varying for sliding surface S (z, t) , and the integral part of
sliding surface is shown is being as:
𝑆(𝑧, 𝑡) =(
𝑑
𝑑𝑡
+ 𝜇)
𝑛−1
(Z
̃)=0 (8)
𝑆(𝑧, 𝑡) = (
𝑑
𝑑𝑡
+ 𝜇)
𝑛−1
(∫ Z
̃
𝑡
0
𝑑𝑡) =0 (9)
μ is the sliding surface slope coefficient and it is positive constant, in this method the main target is to keep
the sliding surface slope near to zero. Therefore, best strategies to achieve this is to find the input control υ
outside the sliding surface and remains on it. To keep the 𝑆( 𝑒 , 𝑒̇ ) close to zero, the law control is design to
achieve the sliding condition of Lyapunov function [4], [6], is being as:
𝑉 =
1
2
𝑆2
≥ 0 (10)
So, the time derivative becomes 𝑉̇ =(𝑆̇2
𝑆) and the control 𝑢 is chosen such that:
𝑆̇2
𝑆 ≤ 𝜂 |𝑆| (11)
where 𝜂 is consider appositive constant [3], [6], so the sliding surface will have computed is being as:
1
2
𝑑
𝑑𝑡
𝑆2
(𝑧, 𝑡) ≤ 𝜂|𝑆(𝑧, 𝑡)| (12)
when the surface (𝑆) ≈ Zero .So , error 𝑒=Z
̂=𝑍 – 𝑍𝑑 ≈ Zero . Let us consider that:
𝑆=𝑒̇ + 𝜇 𝑒 (13)
𝑒̇ =
𝑑𝑒
𝑑𝑡
=Ż - 𝑍̇𝑑 (14)
𝑆=(Ż -𝑍̇𝑑) + 𝜇 (𝑍 – 𝑍𝑑) (15)
Derivative of (15), will consider as the change in sliding surface, is being as:
𝑆̇=
𝑑𝑆
𝑑𝑡
=(Z̈- 𝑍̈𝑑) + 𝜇 (Ż-𝑍̇𝑑) (16)
Since : Z̈=𝛿 + 𝜐 So : 𝑆̇ = 𝛿 + 𝑈 - 𝑍̈𝑑 + 𝜇 ( Ż - 𝑍̇𝑑) (17)
By imposing 𝑆 → 0 also 𝑆̇ → 0 , If we put 𝑆̇ = 0, in (17), as shown in
0 = 𝛿 + 𝑈 - 𝑍̈𝑑 + 𝜇 (Ż- 𝑍̇𝑑) (18)
where 𝛿 is the uncertainty of dynamic, under this hypothesis we get the best approximation for control (𝑈
̂)
which can be define as:
𝑈
̂ = - 𝛿
̂ +𝑍̈𝑑 - 𝜇 (Ż- 𝑍̇𝑑) (19)
Using the uncertainty switching control low to control the dynamic parameters of sliding mode:
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𝑈𝑑𝑖𝑠=𝑈
̂ –𝐾(𝑧, 𝑡)𝑆𝑆𝑖𝑔𝑛 (20)
where 𝐾: is a positive constant function of ( 𝑧 ,𝑡), and 𝑆𝑆𝑖𝑔𝑛 is the switching function define is being as:
{
𝑆𝑆𝑖𝑔𝑛 = 1 𝑖𝑓 𝑆 > 0
𝑆𝑆𝑖𝑔𝑛 = 0 𝑖𝑓 𝑆 = 0
𝑆𝑆𝑖𝑔𝑛 = −1 𝑖𝑓 𝑆 < 0
(21)
Keep the 𝑆(𝑒, 𝑒̇) close to zero in order to satisfy the sliding condition of Lyapunov function. So (9)
and (61) can be arranged is being as:
1
2
𝑑
𝑑𝑡
𝑆2
(𝑧, 𝑡)=𝑆̇ 𝑆=[𝛿 − 𝛿
̂ − 𝐾 𝑆𝑆𝐼𝐺𝑁] 𝑆 = (𝛿 − 𝛿
̂) 𝑆 − Κ|𝑆| (22)
𝑆(𝑧, 𝑡) = (
𝑑
𝑑𝑡
+ 𝜇)
2
(∫ Z
̃
𝑡
0
𝑑𝑡) =(Ż − 𝑍̇𝑑)+2𝜇(Ż − 𝑍̇𝑑)+𝜇2
(𝑍– Z𝑑) (23)
So, the approximation of 𝑈
̂ is computed is being as:
𝑈
̂ = - 𝛿
̂ +𝑍̈𝑑 - 2 𝜇 (Ż- 𝑍̇𝑑 ) + 𝜇2
(𝑍 – 𝑍𝑑) (24)
The SMC for robot manipulator with multi DOF is being as. Figure 3 shows the total of the 𝜏𝑇𝑜𝑡𝑎𝑙:
𝜏𝑇𝑜𝑡𝑎𝑙 = 𝜏𝑒𝑞 + 𝜏𝑑𝑖𝑠 (25)
Figure 3. Block diagram for the total equation of the torque in SMC of PUMA robot manipulator
Finally, the dynamic model of PUMA robot manipulator can be computed is being as [4], [6]:
𝜏𝑒𝑞=[ 𝐴-1
(𝐵 + 𝐶+ 𝐺 ) + 𝑆̇ ] 𝐴 (26)
𝜏𝑑𝑖𝑠 = 𝐾 𝑆𝑆𝑖𝑔𝑛 (27)
𝜏𝑇𝑜𝑡𝑎𝑙 = 𝜏𝑒𝑞 + 𝐾 𝑆𝑆𝑖𝑔𝑛 (28)
𝜏𝑇𝑜𝑡𝑎𝑙=[𝐴−1
( 𝐵+ 𝐶+ 𝐺 ) + 𝑆̇] 𝐴 + 𝐾 .𝑆𝑆𝑖𝑔𝑛 (29)
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5.1. Modified integral approach for sliding mode controller (ISMC)
In order to enhance the performance designed of the proposed controller scheme which is based on
SMC, besides improve stability and minimize total error, integral sliding mode controller (ISMC) was
designed, our goal is to keep the sliding surface close to zero all the time [4], [6], [10]. The formula that
defines ISMC surface is being as:
𝑆𝐼𝑛𝑡𝑒𝑔𝑟𝑎𝑙 = 𝜇𝑒 + 𝑒̇ + (
1
2
)2 ∑ 𝑒 (30)
In the SMC model, two parameters (K, μ) must be adjusted. If these parameters are appropriately
changed, the controller will reject external disturbances disrupting the tracking path and increase the torque
of the robot joints. PSO and SSO are the two algorithms that used in this work to pick the best values to the
parameters of both SMC and ISMC. Figure 4 shows the total output torque for ISMC design.
Figure 4. Block diagram illustrates the design of ISMC of PUMA robot manipulator
6. INTELLIGENT SWARM OPTIMIZATION ALGORITHMS
6.1. Particle swarm optimization (PSO)
Particle swarm optimization (PSO) algorithm used initial random solutions called particles, which
fly over the search space size by following the obtained optimum particle and their own acquired
experiences. The principle of this optimization is to use its best known positions of particles to converge the
swarm population in the solution space to a single optimal. The PSO algorithm requires, at each point,
changing the location and velocity of the particle to its 𝑃𝑏𝑒𝑠𝑡 and 𝐺𝑏𝑒𝑠𝑡 for each particle, the velocity is
modified iteratively by its personal best position, which is found by the particle, and also by the best position
found by the particles in its vicinity [24], as shown in (31), (32):
𝜁𝑖,𝑔
(𝑘+1)
=Φ . 𝜁𝑖,𝑔
(𝑘)
+ 𝑐1 𝜑1 (𝑃𝑏𝑒𝑠𝑡𝑖,𝑔 – Χ𝑖,𝑔
(𝑘)
) + 𝑐2 𝜑2(𝐺𝑏𝑒𝑠𝑡𝑖,𝑔 – Χ𝑖,𝑔
(𝑘)
) (31)
Χ𝑖,𝑔
(𝑘+1)
=Χ𝑖,𝑔
(𝑘)
+ 𝜁𝑖,𝑔
(𝑘+1)
(32)
where:𝑖: is for particles number. 𝑔: for dimensions’ number. = which are two for SMC. Χ𝑖 : is for 𝑔-
dimensional position vector ( Χ𝑖1 , Χ𝑖2 , . . , Χ𝑖𝑔). 𝜁𝑖 : velocity of the particle (𝜁𝑖1 , 𝜁𝑖1 , ….., 𝜁𝑖𝑔). 𝑃𝑏𝑒𝑠𝑡 : best
visited position for the particles. 𝐺𝑏𝑒𝑠𝑡 : best position explored in the population. 𝜑1 𝜑2 : consider as random
integer between 0 and 1. Φ : inertia weight. 𝑐1 𝑐2: positive constants. 𝑘 : for iteration pointer. Coefficients
𝑐1𝑎𝑛𝑑 𝑐2 include the relative weight of the probabilities that accelerate each particle in 𝑃𝑏𝑒𝑠𝑡 and 𝐺𝑏𝑒𝑠𝑡
position. Sufficient choice of inertia weight (Φ) will allow for a balance between global and local
exploration, which enables reduced aggregate iteration in order to find a fairly optimum solution. Table 1
illustrates the global pseudo code for PSO [25].
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Table 1. Global pseudo code for PSO algorithm
Step No. Description
1 Generate random initial population and initial parameters (initialization of individuals).
2 While not (The population converges towards the desired and optimum solution or the ultimate iteration obtained).
3 For each dimension in the particle
4 Update particle velocities using ( 31)
5 Update particles positions using ( 32 )
6 Evaluate fitness function ( Χi,g
(𝑘)
)
7 If function( Χi,g
(k)
) < function(Pbesti,g) then (Pbesti,g) ← Χi,g
(k)
8 End If
9 If function( Χi,g
(k)
) < function(Gbesti,g)) then function(Gbesti,g) ← Χi,g
(k)
10 End If
11 End If and Loop (Next generation)
6.2. Social spider optimization (SSO)
The SSO algorithm recognizes two search agents, or two genders of male and female in spider’s
webs. Each entity is accomplished with a different set of evolutionary operators, based on the gender, which
simulate different cooperative activities normally seen in the colony. It is observed that, within a certain
range of distance, dominant males mate with their female neighbors and non-dominant males remain in a
position close to other network males and rely on nutrients for the latter. In addition to mating, spiders
communicate with each other by vibrations and the frequency of the vibrations depends on two important
factors: the heaviness of the spider and the distance between the spiders that communicate [26]. The male and
female spiders are the search agents whose population can be determined as:
𝑁𝑓=[(0.9 − 𝑟𝑎𝑛𝑑 ∗ 0.25) ∗ 𝑁𝑘] (33)
𝑁𝑚=𝑁𝑘 − 𝑁𝑓 (34)
where: 𝑁𝑘, 𝑁𝑓, 𝑎𝑛𝑑 𝑁𝑚 denote the total population of all spiders in the communal web, reflecting the number
of female spiders and male spiders respectively. The weight that represent the heaviness of each spider in the
population 𝑊𝑖 is found is being as:
𝑊𝑖 =
𝑓𝑖𝑡𝑛𝑒𝑠𝑠𝑖−𝑤𝑜𝑟𝑠𝑡
𝑏𝑒𝑠𝑡−𝑤𝑜𝑟𝑠𝑡
for 0 ≤ 𝑖 ≤ 𝑁𝑘 (35)
where: 𝑖 : represent the population of the web. 𝑁𝑘: The iteration in the total population. 𝑓𝑖𝑡𝑛𝑒𝑠𝑠𝑖, 𝑏𝑒𝑠𝑡 and
𝑤𝑜𝑟𝑠𝑡 are objective function values. In the common popular web, the spiders connect with each other’s by
using the vibrations that calculated is being as:
𝑉𝑖,𝑗 = 𝑊
𝑗 ∗ 𝑒−𝑑𝑖,𝑗
2
(36)
where: 𝑊
𝑗 : is related to the heaviness of the spider that transmits the vibration. 𝑑𝑖,𝑗 : is considered as the
Euclidean distance computed between the two interacting spiders. The vibrations between spiders in the web
may be classified depending on the transmitting spider as shown in:
The situation where the transmitting spider, 𝑆𝑐 is more heavier than the perceiving spider, 𝑖, ( 𝑊
𝑐 >
𝑊𝑖) contributing to vibration 𝑉𝑐,𝑖.
The situation where the transmitting spider, 𝑆𝑏 is the best heaviest one in the web which contributing in
vibration 𝑉𝑏,𝑖 , ( 𝑊𝑏= max 𝑏𝑒𝑠𝑡 (𝑓𝑖𝑡𝑛𝑒𝑠𝑠𝑖)).
The situation where the transmitting spider, 𝑆𝑓 is a female neighbor contributing in vibration 𝑉𝑓,𝑖.
The location of the female and male spiders is modified at each iteration of the optimization
algorithm using the formula is being as [27]:
𝑓𝑖
𝑘+1
= {
𝑓𝑖
𝑘
+ 𝛼 𝑉𝑐,𝑖( 𝑆𝑐 − 𝑓𝑖
𝑘
) + 𝛽 𝑉𝑏,𝑖 (𝑆𝑏 − 𝑓𝑖
𝑘
) + 𝛾 (𝑟𝑎𝑛𝑑 −
1
2
) 𝑖𝑓 𝑟𝑚 < 𝑃𝐹
𝑓𝑖
𝑘
− 𝛼 𝑉𝑐,𝑖( 𝑆𝑐 − 𝑓𝑖
𝑘
) − 𝛽 𝑉𝑏,𝑖 (𝑆𝑏 − 𝑓𝑖
𝑘
) + 𝛾 (𝑟𝑎𝑛𝑑 −
1
2
) 𝑖𝑓 𝑟𝑚 ≥ 𝑃𝐹
(37)
where: 𝛼, 𝛽, 𝛾, 𝑎𝑛𝑑 𝑟𝑚 are consider as random numbers, that are between [0 or 1]. 𝑃𝐹 is regarded as the
threshold of probability factor that is matched with all the randomly produced numbers while the spiders will
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travelled in the space. So, the transition is randomly regulated by the 𝑃𝐹 factor of likelihood and the motion
is generated in relation with the other spiders depending on their vibration through the space of search. In the
optimization procedure the male spider 𝑚𝑖
𝑘+1
, will operated is being as [26]:
𝑚𝑖
𝑘+1
= {
𝑚𝑖
𝑘
+ 𝛼 𝑉𝑐,𝑖( 𝑆𝑓 − 𝑚𝑖
𝑘
) + 𝛾 (𝑟𝑎𝑛𝑑 −
1
2
) 𝑖𝑓 𝑊𝑁𝑓+𝑖
> 𝑊𝑁𝑓+𝑚
𝑚𝑖
𝑘
+ 𝛼 (
∑ 𝑚ℎ
𝑘
𝑊𝑁𝑓+ℎ𝑖
𝑁𝑚
ℎ𝑖=1
∑ 𝑊𝑁𝑓+ℎ𝑖
𝑁𝑚
ℎ𝑖=1
− 𝑚𝑖
𝑘
) 𝑖𝑓 𝑊𝑁𝑓+𝑖
≤ 𝑊𝑁𝑓+𝑚
(38)
where: (
∑ 𝑚ℎ
𝑘
𝑊𝑁𝑓+ℎ𝑖
𝑁𝑚
ℎ𝑖=1
∑ 𝑊𝑁𝑓+ℎ𝑖
𝑁𝑚
ℎ𝑖=1
− 𝑚𝑖
𝑘
) is considered as the weighted average of the male spider in the web
population. The dominant males, as described earlier, will mate with the females in the mating range, which
is given is being as:
𝑟 =
∑ (𝑝𝑗
ℎ𝑖𝑔ℎ
−𝑝𝑗
𝑙𝑜𝑤
)
𝑛
𝑗=1
2𝑛
(39)
where: 𝑝𝑗
ℎ𝑖𝑔ℎ
𝑎𝑛𝑑 𝑝𝑗
𝑙𝑜𝑤
are assumed to be as the higher and lower initial limits. Table 2 illustrates the pseudo
code for SSO algorithm:
Table 2. The pseudo code for SSO algorithm
Step No. Description
1 Assign the initial parameters value for the SSO algorithm
2 Create the population of spiders and assign memory for them
3 Initial the first position for both male spider and female spider
4 Counting the k iterations in the population ( 𝑁𝑘)
5 While 𝑖 ≤ 𝑁𝑘
6 Calculate the mating radius for both female and male spiders as in (39)
7 Calculating the spiders’ weight as in (35)
8 Calculate the passage of female and male spider’s dependent on mutual female and male
operators as shown in (37) and (38)
9 Conduct mating between dominant males and females
10 Update solutions if there are heavier spider progenies
11 End the loop
7. RESULTS AND DISCUSSION
In this work, the Matlab/ Simulink structure version R2019b and M-File were used to apply the
design strategy of the controller in order to evaluate the performance of the proposed controller design. The
sampling time fixed on ∆t = 0.001 s. PSO and SSO algorithms are used to inspect and refine the parameters
of the controller (K, μ). The algorithms will take the (Χ𝑑𝑒𝑠𝑖𝑟𝑒) as a reference and check each particle in
(Χ𝑎𝑐𝑡𝑢𝑎𝑙) by using the mean square error between them until it reaches the best fitness function with
smallest mean square error, is being as:
𝑀𝑠𝑒 =
1
𝑁
∑ (𝑋𝑑 − 𝑋𝑎)2
𝑁
𝑖=1 (40)
where: N: is number of random samples that were used 𝑋𝑑: is for desire position. 𝑋𝑎: is for actual position.
PSO algorithm is initialized with the following parameters: N=20 birds, Iteration=100, 𝑐1=𝑐2=2, Φ=0.5, and
the k (dimension vector) for SMC and ISMC=2 parameters.
For SSO algorithm the initialized is with the following parameters: Spider=20, Iteration=100, all the
random number [𝛼, 𝛽, 𝛾, 𝑟𝑚] = 1. The upper female percent=0.9, lower female percent=0.65. The total time
for implementation is 100 Sec. Only the first three links of PUMA robot were being considered in this work.
Figure 5 shows the step response for the first, second and third links for the robot, from which it can
be seen that ISMC/SSO has the best response with nearly zero maximum overshoot and stable steady state,
minimum settling time, and near zero mean square error comparing with the ISMC/PSO. Figure 6 shows the
final torque in the third links with ISMC/SSO, and ISMC/PSO techniques, it can be noticed that the torque
response becomes more stable besides eliminating the chattering phenomena. Table 3 shows the
configuration of the range for the initial values of the parameters (K, μ) for ISMC, the values after
optimization with PSO, and the values after optimization with SSO.
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3952
(a) (b)
(c)
Figure 5. The step response for the first, second and third links (a, b, c) for the robot
(a) (b)
(c)
Figure 6. The final torque in the first three links (a, b, c) with ISMC/SSO, and ISMC/PSO techniques
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Table 3. The configuration of the basic values for the parameters range of ISMC
The Parameters Initial Value Optimization with PSO Optimization with SSO
𝐾 30 489.2526 70.9456
𝜇 2 4.4726 9.3382
7.1. Robustness test
In order to test the robustness of the proposed techniques, a white Gaussian noise predefined of 40%
power from the input signal was introduced to the system between 5 s to 10 s. Figure 7 shows the effect of
the disturbance noise on the response signal for the three links, we can notice that a slight oscillations occur
in the trajectory response of the links. Figure 8 shows the iteration of SSO and PSO algorithms with mean
square error and objective function, from which it can be seen that SSO successively iterates until it reaches
the optimal value for parameters, with best fitness=4.4876 𝑒−6
. Besides PSO has best fitness=3.4948 𝑒−4
. In
order to evaluate the performance of the proposed techniques, a comparison were made with some existing
methods. Table 4 shows a comparison with existing approaches specifically used to operate with robot
manipulator in order to verify the validation of the proposed controller techniques in terms of robustness and
stability, these techniques are shown in [11], [12], [17].
(a) (b)
(c)
Figure 7. The effect of disturbance of a white Gaussian noise for the first three links (a, b, c)
Figure 8. Iteration and objective function for SSO and PSO algorithm
12. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 11, No. 5, October 2021 : 3943 - 3955
3954
Table 4. Comparison between proposed techniques and existing methods
Category The Techniques Mean Square Error
1 Proposed method ISMC /SSO 4.4876 𝑒−6
2 Proposed method ISMC /PSO 3.4948 𝑒−4
3 Adaptive fuzzy SMC 0.0016
4 Fuzzy SMC 0.0001
5 Estimated SMC 0.00172
6 Super-twisting of sliding mode controller STSMC 0.00085
7 Impedance controller /PSO 0.00208
7.2. Discussion
The results demonstrate significant response with almost zero maximum overshoot and stable steady
state with the ISMC and SSO optimization algorithm compared with the ISMC/PSO, the oscillations are
dampened faster, besides reduce the chattering phenomenon. The torque reaction becomes more stable. The
comparison with the existing techniques shows that ISMC/SSO has the best fitness.
8. CONCLUSION
This work introduces an optimal technique and robust control strategy based on conventional SMC,
and integral sliding mode control (ISMC) with two novel swarm intelligent algorithms, SSO and PSO, to
control the first three links of PUMA robot manipulators. Therefore, the evolutionary algorithms SSO and
PSO are used to determine the optimal values for the parameters of the proposed controllers such that the
suggested techniques trajectory response is ensure the stability and robustness for variable conditions that
affect the end effector of the robot manipulator. Thus, the developed method effectiveness was observed by
simulations, where a comparison with the conventional SMC has been done. The simulation results
demonstrated that the proposed ISMC/SSO offering high performance in term of step response, stability, and
smoothness of control signal despite disturbance noise, with minimum mean square error and best fitness
compared to the other existing techniques.
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BIOGRAPHIES OF AUTHORS
Suhad Haddad received her BSc in Electronic and Communication Engineering from
Department of Electrical and Electronic Engineering at University of Technology.She received
her Higher Deploma in Computer Enginnering at University of Technology in2000 .She received
her Master’s degree from the Electrical and Electronics Engineering Department at the
University of Technology in2015.Ph. D student in Electronic and communication Department at
the University of Technology, Baghdad, Iraq.Her research activites in ANN, image processing,
electronic circuits, robotic system.Having 15 years of teaching experience.
Hanan A. R. Akkar received her Bachelor’s Degree from the Electrical and Electronics
Engineering Department at the University of Technology in 1988. She received her Master’s
degree and Ph.D. degree from the Electrical and Electronics Engineering Department at the
University of Technology in 1994 and 1998, respectively. She has been Professor in the
Department of Electrical Engineering at the University of Technology in the filled of ANN, FL,
GA and swarm intelligent based on FPGA and electronic circuits. Currently she is Head of
scientific Upgrade committee in the University of Technology.