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.
Speed Control of PMSM by Sliding Mode Control and PI ControlIJMTST Journal
In order to optimize the speed-control performance of the permanent-magnet synchronous motor (PMSM)
system with different disturbances and uncertainties, a nonlinear speed-control algorithm for the PMSM servo
systems using sliding-mode control and disturbance compensation techniques is developed in this paper.
First, a sliding-mode control and PI control method based on one novel which allows chattering reduction on
control input while maintaining high tracking performance of the controller. Then, an PI control extended
sliding-mode disturbance observer is proposed to estimate lumped uncertainties directly, to compensate
strong disturbances and achieve high servo precisions. Simulation results PI control better than the SMC
control both show the validity of the proposed control approach.
Simulation of Linear Induction Motor Using Sliding Mode Controller TechniqueIJMERJOURNAL
ABSTRACT:In the present paper, the mover speed control of a linear induction motor (LIM) using a sliding mode control design is proposed. First, the indirect field-oriented control LIM is derived. The sliding mode control design is then investigated to achieve speed- and flux-tracking under load thrust force disturbance. The numerical simulation results of the proposed scheme present good performances in comparison to that of the classical sliding mode control.In the present paper, a sliding mode controller based onindirect field orientation is proposed for LIM speed controlwhile considering end effects. The proposed controller isapplied to achieve a speed- and flux-tracking objectiveunder parameter uncertainties and disturbance of loadthrust force.
In this paper, the design of a speed control scheme based on a total sliding mode control for Indirect Field Oriented of a three phase induction motor (IM) is proposed. Firstly, the indirect field oriented control is derived. Then, sliding mode control design is investigated to achieve a speed tracking objective under different load torque disturbance. Finally a dSPACE DS1104 R&D board is used to implement the proposed scheme. The experimental results released on 0.25 kW slip-ring IM show a high dynamic performance, fast transient response without overshot as well as a good load disturbances rejection response.
Sliding Mode Methodology Vs. Computed Torque Methodology Using MATLAB/SIMULIN...CSCJournals
Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of the most important challenging works. This paper focuses on the design, implementation and analysis of a chattering free sliding mode controller for highly nonlinear dynamic PUMA robot manipulator and compare to computed torque controller, in presence of uncertainties. In order to provide high performance nonlinear methodology, sliding mode controller and computed torque controller are selected. Pure sliding mode controller and computed torque controller can be used to control of partly known nonlinear dynamic parameters of robot manipulator. Conversely, pure sliding mode controller is used in many applications; it has an important drawback namely; chattering phenomenon which it can causes some problems such as saturation and heat the mechanical parts of robot manipulators or drivers. In order to reduce the chattering this research is used the linear saturation function boundary layer method instead of switching function method in pure sliding mode controller. These simulation models are developed as a part of a software laboratory to support and enhance graduate/undergraduate robotics courses, nonlinear control courses and MATLAB/SIMULINK courses at research and development company (SSP Co.) research center, Shiraz, Iran.
This work treats the modeling and simulation of non-linear system behavior of an induction motor using backstepping sliding mode control (BACK- SMC). First, the direct field oriented control IM is derived. Then, a sliding for direct field oriented control is proposed to compensate the uncertainties, which occur in the control. Finally, the study of Backstepping sliding controls strategy of the induction motor drive. Our non linear system is simulated in MATLAB SIMULINK environment, the results obtained illustrate the efficiency of the proposed control with no overshoot, and the rising time is improved with good disturbances rejections comparing with the classical control law.
Speed Control of PMSM by Sliding Mode Control and PI ControlIJMTST Journal
In order to optimize the speed-control performance of the permanent-magnet synchronous motor (PMSM)
system with different disturbances and uncertainties, a nonlinear speed-control algorithm for the PMSM servo
systems using sliding-mode control and disturbance compensation techniques is developed in this paper.
First, a sliding-mode control and PI control method based on one novel which allows chattering reduction on
control input while maintaining high tracking performance of the controller. Then, an PI control extended
sliding-mode disturbance observer is proposed to estimate lumped uncertainties directly, to compensate
strong disturbances and achieve high servo precisions. Simulation results PI control better than the SMC
control both show the validity of the proposed control approach.
Simulation of Linear Induction Motor Using Sliding Mode Controller TechniqueIJMERJOURNAL
ABSTRACT:In the present paper, the mover speed control of a linear induction motor (LIM) using a sliding mode control design is proposed. First, the indirect field-oriented control LIM is derived. The sliding mode control design is then investigated to achieve speed- and flux-tracking under load thrust force disturbance. The numerical simulation results of the proposed scheme present good performances in comparison to that of the classical sliding mode control.In the present paper, a sliding mode controller based onindirect field orientation is proposed for LIM speed controlwhile considering end effects. The proposed controller isapplied to achieve a speed- and flux-tracking objectiveunder parameter uncertainties and disturbance of loadthrust force.
In this paper, the design of a speed control scheme based on a total sliding mode control for Indirect Field Oriented of a three phase induction motor (IM) is proposed. Firstly, the indirect field oriented control is derived. Then, sliding mode control design is investigated to achieve a speed tracking objective under different load torque disturbance. Finally a dSPACE DS1104 R&D board is used to implement the proposed scheme. The experimental results released on 0.25 kW slip-ring IM show a high dynamic performance, fast transient response without overshot as well as a good load disturbances rejection response.
Sliding Mode Methodology Vs. Computed Torque Methodology Using MATLAB/SIMULIN...CSCJournals
Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of the most important challenging works. This paper focuses on the design, implementation and analysis of a chattering free sliding mode controller for highly nonlinear dynamic PUMA robot manipulator and compare to computed torque controller, in presence of uncertainties. In order to provide high performance nonlinear methodology, sliding mode controller and computed torque controller are selected. Pure sliding mode controller and computed torque controller can be used to control of partly known nonlinear dynamic parameters of robot manipulator. Conversely, pure sliding mode controller is used in many applications; it has an important drawback namely; chattering phenomenon which it can causes some problems such as saturation and heat the mechanical parts of robot manipulators or drivers. In order to reduce the chattering this research is used the linear saturation function boundary layer method instead of switching function method in pure sliding mode controller. These simulation models are developed as a part of a software laboratory to support and enhance graduate/undergraduate robotics courses, nonlinear control courses and MATLAB/SIMULINK courses at research and development company (SSP Co.) research center, Shiraz, Iran.
This work treats the modeling and simulation of non-linear system behavior of an induction motor using backstepping sliding mode control (BACK- SMC). First, the direct field oriented control IM is derived. Then, a sliding for direct field oriented control is proposed to compensate the uncertainties, which occur in the control. Finally, the study of Backstepping sliding controls strategy of the induction motor drive. Our non linear system is simulated in MATLAB SIMULINK environment, the results obtained illustrate the efficiency of the proposed control with no overshoot, and the rising time is improved with good disturbances rejections comparing with the classical control law.
The main objective of this paper is to continue the development of activities of basic and applied research related to wind energy and to develop methods of optimal control to improve the performance and production of electrical energy from wind. A new control technique of Double fed induction generator for wind turbine is undertaken through a robust approach tagged nonlinear sliding mode control (SMC) with exponential reaching law control (ERL). The SMC with ERL proves to be capable of reducing the system chattering phenomenon as well as accelerating the approaching process. A nonlinear case numerical simulation test is employed to verify the superior performance of the ERL method over traditional power rate reaching strategy. Results obtained in Matlab/Simulink environment show that the SMC with ERL is more robust, prove excellent performance for the control unit by improving power quality and stability of wind turbine.
In this paper, a novel adaptive control approach for Unmanned Aerial Manipulators (UAMs) is proposed. The UAMs are a new configuration of the Unmanned Arial Vehicles (UAVs) which are characterized by several inhered nonlinearities, uncertainties and coupling. The studied UAM is a Quadrotor endowed with two degrees of freedom robotic arm. The main objectives of our contribution are to achieve both a tracking error convergence by avoiding any singularity problem and also the chattering amplitude attenuation in the presence of perturbations. Therefore, the proposed Adaptive Nonsingular Terminal Super-Twisting controller (ANTSTW) consists of the hybridization of a Nonsingular Terminal Sliding Mode Control and an Adaptive Super Twisting. The adaptive law, which adjust the Super-Twisting’s parameters, is obtained by using stability Lyapunov theorem. Simulation experiments in trajectory tracking mode were realized and compared with Nonsingular Terminal Super-twisting control to prove the superiority and the effectiveness of the proposed approach.
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.
The sliding mode control approach is recognized as one of the
efficient tools to design robust controllers for complex high-order non-linear dynamic plant operating under uncertainty conditions.
Design and Implementation of Sliding Mode Algorithm: Applied to Robot Manipul...Waqas Tariq
Refer to the research, review of sliding mode controller is introduced and application to robot manipulator has proposed in order to design high performance nonlinear controller in the presence of uncertainties. Regarding to the positive points in sliding mode controller, fuzzy logic controller and adaptive method, the output in most of research have improved. Each method by adding to the previous algorithm has covered negative points. Obviously robot manipulator is nonlinear, and a number of parameters are uncertain, this research focuses on comparison between sliding mode algorithm which analyzed by many researcher. Sliding mode controller (SMC) is one of the nonlinear robust controllers which it can be used in uncertainty nonlinear dynamic systems. This nonlinear controller has two challenges namely nonlinear dynamic equivalent part and chattering phenomenon. A review of sliding mode controller for robot manipulator will be investigated in this research.
Novel Artificial Control of Nonlinear Uncertain System: Design a Novel Modifi...Waqas Tariq
This research is focused on novel particle swarm optimization (PSO) SISO Lyapunov based fuzzy estimator sliding mode algorithms derived in the Lyapunov sense. The stability of the closed-loop system is proved mathematically based on the Lyapunov method. PSO SISO fuzzy compensate sliding mode method design a SISO fuzzy system to compensate for the dynamic model uncertainties of the nonlinear dynamic system and chattering also solved by nonlinear fuzzy saturation like method. Adjust the sliding function is played important role to reduce the chattering phenomenon and also design acceptable estimator applied to nonlinear classical controller so PSO method is used to off-line tuning. 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 nonlinear (fuzzy) boundary like 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 applied fuzzy inference system into sliding mode algorithm to design and estimate model-free nonlinear dynamic equivalent part. 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 PSO method to a fuzzy sliding mode controller to tune the parameters of the fuzzy rules in use will ensure a moderate computational load. The PSO method in this algorithm is designed based on the PSO stability theorem. Asymptotic stability of the closed loop system is also proved in the sense of Lyapunov.
We focus a modern methodology in this paper for adding the fuzzy logic control as well as sliding model control. This combination can enhance the MLS position control robustness and enhanced performance of it.In the start, for an application in an area to control the loops placement and position for the synchronous motor what has permanent magnetic linearity we tend to control the fuzzy sliding mode control. To resolve the chattering issues a designed controller is investigated and, in this way, steady state motion in sliding with higher accuracy is obtained. In this case, method of online tuning with the help of fuzzy logic is used in order to adjust the thickness of boundary layer and switching gains.For the suggested scheme technique, the outcomes of simulation suggest that with the classical SMC the accurate state and good dynamic performance is compared due to force chattering resistance, response by quick dynamic force and external disturbance elements and robustness against them.
MODELING AND DESIGN OF CRUISE CONTROL SYSTEM WITH FEEDFORWARD FOR ALL TERRIAN...csandit
This paper presents PID controller with feed-forward control. The cruise control system is one
of the most enduringly popular and important models for control system engineering. The
system is widely used because it is very simple to understand and yet the control techniques
cover many important classical and modern design methods. In this paper, the mathematical
modeling for PID with feed-forward controller is proposed for nonlinear model with
disturbance effect. Feed-forward controller is proposed in this study in order to eliminate the
gravitational and wind disturbance effect. Simulation will be carried out . Finally, a C++
program written and feed to the microcontroller type AMR on our robot
This paper present a speed hybrid fuzzy-sliding mode control (HFSMC) of a permanent magnet synchronous motor (PMSM) to ensure the traction of an electric vehicle; at the first we applied the sliding mode control (SMC) with three surfaces on the PMSM by taking into account the dynamics of the vehicle; And afterwards we applied the fuzzy-sliding mode in which the surface of the speed is replaced by a Fuzzy-PI controller; Simulation under Matlab/Simulink has been carried out to evaluate the efficiency and robustness of the proposed control on a system drive. It should be noted that the reference speed is the European urban driving schedule ECE-15 cycle.
Real Time Implementation of Fuzzy Adaptive PI-sliding Mode Controller for Ind...IJECEIAES
In this work, a fuzzy adaptive PI-sliding mode control is proposed for Induction Motor speed control. First, an adaptive PI-sliding mode controller with a proportional plus integral equivalent control action is investigated, in which a simple adaptive algorithm is utilized for generalized soft-switching parameters. The proposed control design uses a fuzzy inference system to overcome the drawbacks of the sliding mode control in terms of high control gains and chattering to form a fuzzy sliding mode controller. The proposed controller has implemented for a 1.5kW three-Phase IM are completely carried out using a dSPACE DS1104 digital signal processor based real-time data acquisition control system, and MATLAB/Simulink environment. Digital experimental results show that the proposed controller can not only attenuate the chattering extent of the adaptive PI-sliding mode controller but can provide high-performance dynamic characteristics with regard to plant external load disturbance and reference variations.
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.
Speed and Torque Control Challenge of PMSMIJMTST Journal
This paper presents modeling and implementation Challenge of speed toqrue rotor field oriented control of
permanent magnet synchronous machine (PMSM)drive. An experimental setup consisting of IGBT inverters
and a -TMS320LF240 DSP based digital controller is developed in the laboratory in IIT Kharagpur to
implement the control algorithms. A voltage model based flux observer is used for estimating the speed and
position of PMSM. In order to get good starting characteristics a rotor initial position algorithm is also
implemented in the control algorithm. For control purpose PMSM is consider like dc motor. The torque and
speed in the dc motor can be controlled independently by controlling armature current and field current
respectively ensures that dc motor has good dynamic performance.
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.
Comparative Analysis of Multiple Controllers for Semi-Active Suspension SystemPrashantkumar R
International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2014, held on 01-02 Aug 2014 in Nitte Meenakhsi Institute of Technology, Bangalore, India
Paper ID: 600
The main objective of this paper is to continue the development of activities of basic and applied research related to wind energy and to develop methods of optimal control to improve the performance and production of electrical energy from wind. A new control technique of Double fed induction generator for wind turbine is undertaken through a robust approach tagged nonlinear sliding mode control (SMC) with exponential reaching law control (ERL). The SMC with ERL proves to be capable of reducing the system chattering phenomenon as well as accelerating the approaching process. A nonlinear case numerical simulation test is employed to verify the superior performance of the ERL method over traditional power rate reaching strategy. Results obtained in Matlab/Simulink environment show that the SMC with ERL is more robust, prove excellent performance for the control unit by improving power quality and stability of wind turbine.
In this paper, a novel adaptive control approach for Unmanned Aerial Manipulators (UAMs) is proposed. The UAMs are a new configuration of the Unmanned Arial Vehicles (UAVs) which are characterized by several inhered nonlinearities, uncertainties and coupling. The studied UAM is a Quadrotor endowed with two degrees of freedom robotic arm. The main objectives of our contribution are to achieve both a tracking error convergence by avoiding any singularity problem and also the chattering amplitude attenuation in the presence of perturbations. Therefore, the proposed Adaptive Nonsingular Terminal Super-Twisting controller (ANTSTW) consists of the hybridization of a Nonsingular Terminal Sliding Mode Control and an Adaptive Super Twisting. The adaptive law, which adjust the Super-Twisting’s parameters, is obtained by using stability Lyapunov theorem. Simulation experiments in trajectory tracking mode were realized and compared with Nonsingular Terminal Super-twisting control to prove the superiority and the effectiveness of the proposed approach.
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.
The sliding mode control approach is recognized as one of the
efficient tools to design robust controllers for complex high-order non-linear dynamic plant operating under uncertainty conditions.
Design and Implementation of Sliding Mode Algorithm: Applied to Robot Manipul...Waqas Tariq
Refer to the research, review of sliding mode controller is introduced and application to robot manipulator has proposed in order to design high performance nonlinear controller in the presence of uncertainties. Regarding to the positive points in sliding mode controller, fuzzy logic controller and adaptive method, the output in most of research have improved. Each method by adding to the previous algorithm has covered negative points. Obviously robot manipulator is nonlinear, and a number of parameters are uncertain, this research focuses on comparison between sliding mode algorithm which analyzed by many researcher. Sliding mode controller (SMC) is one of the nonlinear robust controllers which it can be used in uncertainty nonlinear dynamic systems. This nonlinear controller has two challenges namely nonlinear dynamic equivalent part and chattering phenomenon. A review of sliding mode controller for robot manipulator will be investigated in this research.
Novel Artificial Control of Nonlinear Uncertain System: Design a Novel Modifi...Waqas Tariq
This research is focused on novel particle swarm optimization (PSO) SISO Lyapunov based fuzzy estimator sliding mode algorithms derived in the Lyapunov sense. The stability of the closed-loop system is proved mathematically based on the Lyapunov method. PSO SISO fuzzy compensate sliding mode method design a SISO fuzzy system to compensate for the dynamic model uncertainties of the nonlinear dynamic system and chattering also solved by nonlinear fuzzy saturation like method. Adjust the sliding function is played important role to reduce the chattering phenomenon and also design acceptable estimator applied to nonlinear classical controller so PSO method is used to off-line tuning. 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 nonlinear (fuzzy) boundary like 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 applied fuzzy inference system into sliding mode algorithm to design and estimate model-free nonlinear dynamic equivalent part. 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 PSO method to a fuzzy sliding mode controller to tune the parameters of the fuzzy rules in use will ensure a moderate computational load. The PSO method in this algorithm is designed based on the PSO stability theorem. Asymptotic stability of the closed loop system is also proved in the sense of Lyapunov.
We focus a modern methodology in this paper for adding the fuzzy logic control as well as sliding model control. This combination can enhance the MLS position control robustness and enhanced performance of it.In the start, for an application in an area to control the loops placement and position for the synchronous motor what has permanent magnetic linearity we tend to control the fuzzy sliding mode control. To resolve the chattering issues a designed controller is investigated and, in this way, steady state motion in sliding with higher accuracy is obtained. In this case, method of online tuning with the help of fuzzy logic is used in order to adjust the thickness of boundary layer and switching gains.For the suggested scheme technique, the outcomes of simulation suggest that with the classical SMC the accurate state and good dynamic performance is compared due to force chattering resistance, response by quick dynamic force and external disturbance elements and robustness against them.
MODELING AND DESIGN OF CRUISE CONTROL SYSTEM WITH FEEDFORWARD FOR ALL TERRIAN...csandit
This paper presents PID controller with feed-forward control. The cruise control system is one
of the most enduringly popular and important models for control system engineering. The
system is widely used because it is very simple to understand and yet the control techniques
cover many important classical and modern design methods. In this paper, the mathematical
modeling for PID with feed-forward controller is proposed for nonlinear model with
disturbance effect. Feed-forward controller is proposed in this study in order to eliminate the
gravitational and wind disturbance effect. Simulation will be carried out . Finally, a C++
program written and feed to the microcontroller type AMR on our robot
This paper present a speed hybrid fuzzy-sliding mode control (HFSMC) of a permanent magnet synchronous motor (PMSM) to ensure the traction of an electric vehicle; at the first we applied the sliding mode control (SMC) with three surfaces on the PMSM by taking into account the dynamics of the vehicle; And afterwards we applied the fuzzy-sliding mode in which the surface of the speed is replaced by a Fuzzy-PI controller; Simulation under Matlab/Simulink has been carried out to evaluate the efficiency and robustness of the proposed control on a system drive. It should be noted that the reference speed is the European urban driving schedule ECE-15 cycle.
Real Time Implementation of Fuzzy Adaptive PI-sliding Mode Controller for Ind...IJECEIAES
In this work, a fuzzy adaptive PI-sliding mode control is proposed for Induction Motor speed control. First, an adaptive PI-sliding mode controller with a proportional plus integral equivalent control action is investigated, in which a simple adaptive algorithm is utilized for generalized soft-switching parameters. The proposed control design uses a fuzzy inference system to overcome the drawbacks of the sliding mode control in terms of high control gains and chattering to form a fuzzy sliding mode controller. The proposed controller has implemented for a 1.5kW three-Phase IM are completely carried out using a dSPACE DS1104 digital signal processor based real-time data acquisition control system, and MATLAB/Simulink environment. Digital experimental results show that the proposed controller can not only attenuate the chattering extent of the adaptive PI-sliding mode controller but can provide high-performance dynamic characteristics with regard to plant external load disturbance and reference variations.
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.
Speed and Torque Control Challenge of PMSMIJMTST Journal
This paper presents modeling and implementation Challenge of speed toqrue rotor field oriented control of
permanent magnet synchronous machine (PMSM)drive. An experimental setup consisting of IGBT inverters
and a -TMS320LF240 DSP based digital controller is developed in the laboratory in IIT Kharagpur to
implement the control algorithms. A voltage model based flux observer is used for estimating the speed and
position of PMSM. In order to get good starting characteristics a rotor initial position algorithm is also
implemented in the control algorithm. For control purpose PMSM is consider like dc motor. The torque and
speed in the dc motor can be controlled independently by controlling armature current and field current
respectively ensures that dc motor has good dynamic performance.
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.
Comparative Analysis of Multiple Controllers for Semi-Active Suspension SystemPrashantkumar R
International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2014, held on 01-02 Aug 2014 in Nitte Meenakhsi Institute of Technology, Bangalore, India
Paper ID: 600
Evaluation of the stability enhancement of the conventional sliding mode cont...nooriasukmaningtyas
The proposed work is an attempt to investigate the stability of the nonlinear
system by using a whale optimization algorithm as of one of the metaheuristic optimization methods, and this investigation was conducted on a
single inverted pendulum as a study model. The evaluation measures which
were used in this article values of gain and sliding surface of the
conventional sliding mode controller to illustrate the extent of the system`s
stability. Furthermore, control action, the relationship between error and its
derivative, desired, and actual position in addition to sliding response
graphically showed the feasibility of the proposed solution. The attained
results illustrated considerable improvement in the settling time and
minimizing the impact of chattering behavior.
Optimization of Modified Sliding Mode Controller for an Electro-hydraulic Act...IJECEIAES
This paper presents the design of the modified sliding mode controller (MSMC) for the purpose of tracking the nonlinear system with mismatched disturbance. Provided that the performance of the designed controller depends on the value of control parameters, gravitational search algorithm (GSA), and particle swarm optimization (PSO) techniques are used to optimize these parameters in order to achieve a predefined system’s performance. In respect of system’s performance, it is evaluated based on the tracking error present between reference inputs transferred to the system and the system output. This is followed by verification of the efficiency of the designed controller in simulation environment under various values, with and without the inclusion of external disturbance. It can be seen from the simulation results that the MSMC with PSO exhibits a better performance in comparison to the performance of the similar controller with GSA in terms of output response and tracking error.
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).
Synthesis of the Decentralized Control System for Robot-Manipulator with Inpu...ITIIIndustries
It is discussed the problem of synthesis of the decentralized adaptive-periodic control system for two degrees of freedom robotic manipulator which have an input limitations. The solution of the problem is based on the use of hyperstability criterion, L-dissipativity conditions and dynamic filter-corrector.
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.
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.
Intelligent swarm algorithms for optimizing nonlinear sliding mode controller...IJECEIAES
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 푒 −6 and (PSO)=3.4948 푒 −4 .
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In this study, in a computer the dynamic motion modelling of manipulator and control of
trajectory with an algorithm this has been tested. First after dynamic motion simulation of manipulator
has been made MPC Control. The result in this study we can observe that computed torque method gives
better results than MPC methods. So in trajectory control it is approved of using computed torque
method. In last part of this study the results are estimated forward development are exemined and
suggested. The model predictive control (MPC) technique for an articulated robot with n joints is
introduced in this paper. The proposed MPC control action is conceptually different with the trajectory
robot control methods in that the control action is determined by optimising a performance index over
the time horizon. A neural network (NN) is used in this paper as the predictive model.
In this paper, the tracking control scheme is presented using the framework of finite-time sliding mode control (SMC) law and high-gain observer for disturbed/uncertain multi-motor driving systems under the consideration multi-output systems. The convergence time of sliding mode control is estimated in connection with linear matrix inequalities (LMIs). The input state stability (ISS) of proposed controller was analyzed by Lyapunov stability theory. Finally, the extensive simulation results are given to validate the advantages of proposed control design.
Induction motor harmonic reduction using space vector modulation algorithmjournalBEEI
The vector control was proposed as an alternative to the scalar control for AC machines control. Vector control provide high operation performance in steady state and transient operation. However, the variable switching frequency of vector control causes high flux and torque ripples which lead to an acoustical noise and degrade the performance of the control scheme. The insertion of the space vector modulation was a very useful solution to reduce the high ripples level inspite of its complexity. Numerical simulation results obtained in MATLAB/Simulink show the good dynamic performance of the proposed vector control technique and the effectiveness of the proposed sensorless strategy in the presence of the sudden load torque basing on the integral backstepping approach capabilities on instant perturbation rejection.
Keywords
On line Tuning Premise and Consequence FIS: Design Fuzzy Adaptive Fuzzy Slidi...Waqas Tariq
One of the most active research areas in the field of robotics is robot manipulators control, because these systems are multi-input multi-output (MIMO), nonlinear, and uncertainty. At present, robot manipulators is used in unknown and unstructured situation and caused to provide complicated systems, consequently strong mathematical tools are used in new control methodologies to design nonlinear robust controller with satisfactory performance (e.g., minimum error, good trajectory, disturbance rejection). Robotic systems controlling is vital due to the wide range of application. Obviously stability and robustness are the most minimum requirements in control systems; even though the proof of stability and robustness is more important especially in the case of nonlinear systems. One of the best nonlinear robust controllers which can be used in uncertainty nonlinear systems is sliding mode controller (SMC). Chattering phenomenon is the most important challenge in this controller. Most of nonlinear controllers need real time mobility operation; one of the most important devices which can be used to solve this challenge is Field Programmable Gate Array (FPGA). FPGA can be used to design a controller in a single chip Integrated Circuit (IC). In this research the SMC is designed using VHDL language for implementation on FPGA device (XA3S1600E-Spartan-3E), with minimum chattering and high processing speed (63.29 MHz).
Design Auto Adjust Sliding Surface Slope: Applied to Robot ManipulatorWaqas Tariq
The main target in this paper is to present the nonlinear methods in order to control the robot manipulators and also the related results. Also the important role of sliding surface slope in sliding mode fuzzy control of robot manipulator should be considered. Sliding mode controller (SMC) is a significant nonlinear controller in certain and uncertain dynamic parameters systems. To solve the chattering phenomenon, this paper complicated two methods to each other; boundary layer method and applied fuzzy logic in sliding mode methodology. To remove the chattering sliding surface slope also played important role so this paper focused on the auto tuning this important coefficient to have the best results by applied mathematical model free methodology. Auto tuning methodology has acceptable performance in presence of uncertainty (e.g., overshoot=0%, rise time=0.8 s, steady state error = 1e-9 and RMS error=0.0001632).
Design Artificial Nonlinear Robust Controller Based on CTLC and FSMC with Tun...Waqas Tariq
One of the most active research areas in the field of robotics is robot manipulators control, because these systems are multi-input multi-output (MIMO), nonlinear, time variant and uncertainty. An artificial non linear robust controller design is major subject in this work. At present, robot manipulators are used in unknown and unstructured situation and caused to provide complicated systems, consequently nonlinear classical controllers are used in artificial intelligence control methodologies to design nonlinear robust controller with satisfactory performance (e.g., minimum error, good trajectory, disturbance rejection). Sliding mode controller (SMC) and computed torque controller (CTC) are the best nonlinear robust controllers which can be used in uncertainty nonlinear. Sliding mode controller has two most important challenges in uncertain systems: chattering phenomenon and nonlinear dynamic equivalent part. Computed torque controller works very well when all nonlinear dynamic parameters are known. This research is focused on the applied non-classical method (e.g., Fuzzy Logic) in robust classical method (e.g., Sliding Mode Controller and computed torque controller) in the presence of uncertainties and external disturbance to reduce the limitations. Applying the Mamdani’s error based fuzzy logic controller with minimum rules is the first goal that causes the elimination of the mathematical nonlinear dynamic in SMC and CTC. Second target focuses on the elimination of chattering phenomenon with regard to the variety of uncertainty and external disturbance in fuzzy sliding mode controller and computed torque like controller by optimization the tunable gain. Therefore fuzzy sliding mode controller with tunable gain (GTFSMC) and computed torque like controller with tunable gain (GTCTLC) will be presented in this paper.
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).
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Optimal Design of Super Twisting Control with PSO Algorithm for Robotic Manipulator
1. Azza Ibrahim & Sawsan Gharghory
International Journal of Intelligent Systems and Applications in Robotics (IJRA), Volume (9) : Issue (1) :
2018 1
Optimal Design of Super Twisting Control with PSO Algorithm
for Robotic Manipulator
Azza Ibrahim azza@eri.sci.eg
Engineering/ Computers and Systems Department
Electronics Research Institute
Giza, 002, Egypt
Sawsan Gharghory sawsan@eri.sci.eg
email@address.com
Engineering/ Computers and Systems Department
Electronics Research Institute
Giza, 002, Egypt
Abstract
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.
Keywords: Super Twisting Control, Sliding Mode Control, Particle Swarm Optimization Method,
Robotic Manipulator, Robust Optimal Control.
1. INTRODUCTION
Robotic manipulators have being used for many purposes in industrial automation systems,
research and mining fields, medical applications, in laboratories. Because of high challenges in
the dynamic model of robotic manipulator [1], its performance has largely depended on the
control technology that is used which has a direct impact on the overall performance of system.
Furthermore, robotic manipulator suffers from different kind of disturbances such as joint frictions,
unknown payloads, and non-model dynamics. Therefore, the accurate and high precision control
systems become a persistent need. Consequently, the development of robust, nonlinear, and
adaptive control strategy is contentiously required. The inverse dynamics, computed torque
techniques, and the feedback linearization were among the controller methods that were used
earlier in literatures [2-3]. The feedback linearization methods were based on the cancellation of
nonlinearities, so the robustness to the variables uncertainty is appeared. Control method was
used in [4-5], but it is barely sufficient for this problem due to the continual nature of the
disturbances. On the other hand, robust control systems such as sliding modes and Lyapunov
methods were also applied to face the problems of robot manipulators.
2. Azza Ibrahim & Sawsan Gharghory
International Journal of Intelligent Systems and Applications in Robotics (IJRA), Volume (9) : Issue (1) :
2018 2
Sliding Mode technique (SMC) is an effective tool in control non-linear systems due to its
robustness features. It is composed of two stages. The first one is to select a sliding surface in
the state space that the state trajectories are forced to remain along it (sliding phase). The
second is the reaching phase; a discontinuous state-feedback is designed to force the system
trajectory to reach the sliding surface in a finite time [6]. A demonstration of its good performance
in control systems can be found in detail by Utkin et al. in [7]. The sliding mode dynamics can be
stabilized with a proper choice of the sliding surface.
The SMC controller has many advantages such as: system order reduction, finite time
convergence, and its robustness against disturbances, while the trajectories are not robust
against perturbations during reaching phase. The main drawback of the SMC was the appearing
of small oscillations of finite frequency with the output signal which is known as chattering
phenomena caused by the switching frequency of the control [8]. This phenomenon is harmful
because it decreases the control accuracy; the moving mechanical parts suffer from high wear,
and high heat is lost in power circuits [9].
Several approaches were proposed to reduce the chattering problems. The most common one is
the boundary layer approach; the states are forced to remain within a small boundary layer about
the surface by using saturation function rather than the sign discontinuous function [10]. A
boundary layer is employed using fuzzy logic to reduce chattering [11]. The chattering problem
can also be overcome by intelligent systems such as GA, which can be utilized in choosing the
sliding surface slope and thickness of the boundary layer [12-13]. Also, the theory of higher-order
sliding mode (HOSM) was introduced to overcome the above mentioned problems; its concept
was illustrated in details [14]. HOSM provides an effective technique for the reduction or even
practical elimination of the chattering problem without compromising the benefits of the standard
sliding mode. A sliding surface in HOSM is designed in order to make the sliding variable and its
high order time derivatives reach it in finite time [15]. There are several types of HOSM control
algorithms, among them the sub-optimal controller, the terminal sliding mode controllers, the
twisting controller and the super-twisting controller [16]. The super-twisting algorithm is
nowadays preferable over the classical siding mode, since it eliminates the chattering
phenomenon [17-18]. The appropriate choice to the parameters of STC has a big effect on the
overall performance and the accuracy of the controller.
PSO is one of the modern smart heuristic algorithms developed through the simulation of
simplified social behavior to solve nonlinear optimization problems [19]. Since its appearance, it
has been a hotspot of research and promising technique for real world optimization problems [20-
22]. Due to its simple concept, easy implementation as well as its quick convergence, nowadays
PSO has gained much attention and has wide applications in different fields [23]. The successful
applications of this robust optimization algorithm have been reported not only in the area of
control theory but also in a wide variety field of research. For instance, in [24], a new particle
swarm optimization algorithm was applied to optimum design of the armored vehicle scheme. In
[25], multi-objective optimization of vehicle crashworthiness was performed using a new particle
swarm based approach. In [26], a probability matrix based particle swarm optimization was
applied for the capacitated vehicle routing problem. In [27], the particle swarm optimization
algorithm was used for a vehicle routing problem with heterogeneous fleet, mixed backhauls, and
time windows. Also, power optimization of gas pipelines was performed with aid of an improved
particle swarm optimization algorithm in [28].
This work discusses and compares two strategies for creating robust control in an environment of
dynamic disturbances. The first strategy utilizes the conventional sliding mode control SMC while
the second proposes the super-twisting control with an intelligent gains tuned by PSO. Also, a
comparative study was done to the transient response of the non-linear robot manipulator in
terms of settling time and rise time with the proposed strategies. The proposed controller is
designed and simulated using MATLAB/ Simulink software.
3. Azza Ibrahim & Sawsan Gharghory
International Journal of Intelligent Systems and Applications in Robotics (IJRA), Volume (9) : Issue (1) :
2018 3
The remaining of this paper is organized as follows. Section 2 reviews the mathematical model of
two links robot manipulator. The conventional slide mode control along with the proposed supper
twisting controller and the design of control law for robotic manipulator are described in sections 3
and 4 respectively. A brief presentation of PSO algorithm and its application for the proposed
optimal design of STC are outlined in section 5. A simulation study of the suggested controller
along with the conventional controller is carried on in section 6 and finally, the concluding
comments are presented in section 6.
2. SYSTEM DESCRIPTION
The structure of the two links rigid manipulator system are shown in Fig. 1. It composed of two
revolute joints to support the angular motion between the links. The link is moved by controlling
torque applied by the actuator at the joint of manipulator. The system parameters are recorded in
table1.
Parameter Symbol Value
Mass of link1 m1 1 Kg.
Mass of link2 m2 1 Kg.
Length of link 1 l1 1m
Length of link 2 l2 1m
Gravity acc. G 9.8 m/s^2
torque to joint τ N⋅m
Ang. Pos. of link1 Rad
Ang. Pos. of link2 Rad
TABLE 1: The Manipulator Parameters.
FIGURE 1: The Two Links Manipulator Schematic Diagram.
2.1 System Dynamics
The dynamic equations of 2DOF rigid manipulator are deduced according to Lagrange Euler
Formulation [29-30]. The final form of dynamic equation of manipulator is described in (1, 2, and
3).
(1)
(2)
l 1
l 2
Y
x
target
g
`
Base
Gripper
q2
q1
m2
m1
4. Azza Ibrahim & Sawsan Gharghory
International Journal of Intelligent Systems and Applications in Robotics (IJRA), Volume (9) : Issue (1) :
2018 4
(3)
Where, is the symmetric positive definite manipulator inertia matrix is the
Coriolis and Centrifugal matrix, and G (q) is the vector of gravitational torque. q Is the joint
position, is the velocity and is the acceleration, all of them are vectors belonging to .
Symbolizes the vector of external disturbances and non-modelled dynamics such that the
upper bound of the disturbance is , is a certain positive value. Let
, hence the relation (1) can be rewritten as the following form:
(4)
The controller objective is to get the suitable input torque τ such that the joints angular positions
can accurately reach the desired values and in finite time in spite of existing an external
disturbances, . The error and the first time derivative of error are defined in the general form as
depicted in (5-7).
(5)
(6)
(7)
Hence, the objective is to drive and . To solve this problem and achieve the
required target, both of the conventional sliding mode control technique and the super-twisting
controller are suggested.
3. SLIDING MODE CONTROLLER DESIGN
The concept of both the sliding mode and the sliding surface was explained in [31]. The sliding
mode control is an attractive technique to control systems subject to bounded external
disturbances and model uncertainty. In the conventional sliding mode control, the control signal
consists of two parts. One is called the “equivalent control” and the other is the “switching
control”. The equivalent control deals with the dynamics of the nominal system and the sliding
surface, while the switching signal is used to keep the system trajectories onto the selected
sliding surface in spite of existing disturbances. The sliding surface is linear with a constant slope,
“c” which determines the system performance. For small values of c, the reaching time is short,
while the system dynamics are slow [32]. The design procedure for the control system for the
robot manipulator is outlined in the following equations. The sliding function is choosing as
follows,
(8)
And the control signal is represented as in (9)
(9)
The equivalent control is computed by putting, , keeping , [33].
(10)
Considering the dynamic equation of the system in (4) without external disturbances and the
substitution in (10), (11) can be obtained and then the equivalent control signal can be computed
by putting (11) equals zero as follows.
5. Azza Ibrahim & Sawsan Gharghory
International Journal of Intelligent Systems and Applications in Robotics (IJRA), Volume (9) : Issue (1) :
2018 5
(11)
Where: . Consequently, the equivalent control is deduced in (12)
as follows:
(12)
After adding the switching control signal to the above equivalent control, the input control using
SMC is which is computed as described in (13).
(13)
The term ( ) represents the switching part of the control signal, . It is used to
make the system trajectories go towards the sliding surface in the reaching mode or used to
reject the disturbances. Also, "k" is a positive definite gain vector. However, a serious high-
frequency oscillations will appear in any real system whose input is supposed to switch infinitely
fast by using this function and resulting in actuators damage. This drawback is called chattering
phenomena.
To reduce the chattering, the boundary layer is the most famous method. The basic idea of this
method is to replace the sign function by a saturation function, , where ∈ is the thickness
of the boundary layer [34-35]. The system is stabilized by driving the state to a small region
containing the state space origin. The boundary layer control is a continuous function of state,
therefore it is smooth and chattering free. However, when the state measurements are corrupted
with noise whose level is significant with respect to the boundary layer width, the boundary layer
design can no longer avoid chattering. Therefore, super twisting controller is suggested to face
the chattering problem and explained in detail in the following section.
4. SUPER TWISTING CONTROL LAW
n order to avoid chattering, the super-twisting algorithms are developed to control systems with
relative degree one which means s is continuous and s is discontinuous [36-37]. In this kind of
controllers, the system trajectories are characterized by twisting around the origin in phase plane
( vs. plane) as shown in Fig. 2.
FIGURE 1: Phase Trajectory of Super-twisting Algorithm.
In super twisting algorithm, the control signal consists of three terms. They are: the
equivalent control, the continuous function of the sliding variable, and the discontinuous part with
integrator as formulated in (14) and (15) [9].
6. Azza Ibrahim & Sawsan Gharghory
International Journal of Intelligent Systems and Applications in Robotics (IJRA), Volume (9) : Issue (1) :
2018 6
(14)
(15)
Where the equivalent control term, is defined as in equation (12), and C and W are certain
positive gains, the designed parameter ρ is chosen as ρ=0.5. The value of W is selected such
that greater than the upper bound of disturbance, δ in order to ensure the stability. Also the
sufficient conditions for a finite time convergence to the sliding surface [9], [37] are:
and , such that if there are positive boundary
constants values, , and are defined from the inequities (16).
, and (16)
Because of the complexity of the aforementioned computations which are time consuming, it is
hardly to determine the values of these boundaries and consequently the values of the controller
parameters cannot be estimated. Instead of this mathematical calculation for STC parameters,
PSO is proposed to optimally tune the controller parameters by using integral absolute error (IAE)
and integral square error (ISE) indices. Consequently, the controlled system response can be
improved by the optimal value of the control torque and the manipulator reaches rapidly to the
desired position. This control law guarantees the continuity of the control signal and allows
suppression of the chattering. Lyapunov stability criterion was used to confirm the controller
stability as demonstrated extensively in [38]. An overview of the standard PSO and a brief
description of its concept are presented in the following section.
5. OPTIMAL STC WITH PSO
The first pioneer introducing the particle swarm optimization was Kennedy and Eberhart in 1995
[19], the idea of PSO algorithm was inspired from the living creature social behavior such as birds
and others. The first PSO generation is started randomly with a set of particles (solutions to the
problem) and after that updating the PSO algorithm through new generations to find the best
solution. Each particle in the swarm memorizes the best solution track that has yet accomplished
in the search space and the best solution value is named pbest. Also, the location of the global
best value found so far through any particle in the swarm is called the gbest. The location of each
particle in the swarm is affected by two parameters values which are its best position reached
and the best position of its neighbouring particles. The particle is accelerated toward pbest and
gbest locations by generating a random numbers. Both of the particle location and its velocity is
updated in each iteration using the following equations:
(17)
)1()()1( kvkxkx iii (18)
Where: v(k + 1), v(k) are the velocity of a particle in iteration k + 1 and k respectively, while x(k
+ 1) and x(k) are the particle positions in the following iterations k + 1 and k. Both of and
are random numbers generated from (0 to 1). The particle self-confidence is and usually
given value in range from (1.5 to 2.0), while is called the swarm confidence [22] and usually
takes value in the range (2.0–2.5). The inertia weight (ω) is used to fulfil the balancing between
the search space exploration and its exploitation and plays very vital role in the convergence
behavior of PSO. In each generation, the inertia weight is reduced dynamically from 1.0 to near 0
using the following equation.
(19)
7. Azza Ibrahim & Sawsan Gharghory
International Journal of Intelligent Systems and Applications in Robotics (IJRA), Volume (9) : Issue (1) :
2018 7
Where: is the number of maximum iterations, and iter is the current iteration number.
, and are the maximum and minimum values of inertia weight. PSO algorithm in this
paper is proposed for tuning STC parameters and our proposed PSO parameters are: = 1.5,
= 2, also, the number of iterations N = 10 and the swarm size = 20. The range of STC
parameters for W and C vectors are obtained. Both of W1 and W2 are: (0.1 to 2), while the range
of C1 and C2 are: (0.1 to 3) and (0.1 to 4) respectively. The optimal STC parameters by PSO are
adjusted by minimizing the error between the desired and the actual output. Two objective
functions based on error indices are used and each function defines the specific error
performance criterion being implemented to optimize the performance of STC controlled system.
The fitness of each particle in the swarm is evaluated in each iteration depending on the following
objective functions: Integral of Square of the Error (ISE),
(20)
And integral of Absolute Magnitude of the Error (IAE),
(21)
Where: e1 and e2 are the trajectories errors between system input and output calculated over a
time interval t equals 10 sec. for both the first and second link of robot manipulator respectively,
and n is the number of samples. Selection of the range of the STC control parameters using PSO
is low complexity compared to the mathematical method which is depicted from (14) to (15).
6. SIMULATION RESULTS AND DISCUSSION
To verify the accuracy and the efficiency of the proposed control algorithms, the simulation of the
two links rigid manipulator have been carried out using MATLAB/SIMULINK Software (v. 2016a).
The simulator is verified by comparing its input/output with simulator derived in other published
work [39]. Both the conventional SMC and the STC algorithms are simulated and coupled with
the system as shown in Fig. 3.
FIGURE 3: The Block Diagram of The Controlled System.
Firstly, the behavior of the controlled system with the computed control torque using the
conventional SMC without and with different types of external disturbance are shown in figures
8. Azza Ibrahim & Sawsan Gharghory
International Journal of Intelligent Systems and Applications in Robotics (IJRA), Volume (9) : Issue (1) :
2018 8
(4-6). The designed SMC parameters are tuned manually and selected as c [1 1], k = [0.5, 0.6],
and = [0.001 0.001]. Both constant and sinusoidal external disturbances, ,
is added in time interval and are used to verify the robust characteristic of
the controller.
Secondly, the performance of the system is tested by using the super twisting controller. Both of
the super twisting controllers either the conventional or that tuned by PSO prove their superiority
in chattering suppression as shown in figures (7-9) comparing to the results of SMC shown in
previous figures (4-6).
Finally, the performance of the controlled system via STC whose parameters are tuned by the
proposed PSO is evaluated in terms of settling time and rise time and through reducing the error
between the desired and actual outputs using IAE and ISE indices. The time responses results of
robotic manipulator with the designed STC whose parameters are tuned manually or by PSO in
presence of different types of disturbance such as constant and sine wave disturbances are
shown in figures (7-9). Also, the cost functions of PSO performance in tuning the parameters of
STC using IAE and ISE criterions are depicted in figures (10-11). Moreover, the transient
response characteristics of robotic manipulator controlled by STC whose parameters are
manually adjusted or tuned by PSO using IAE and ISE performance indices in terms of rise time
and settling time for step input are described in tables 2-3. Also, the values of IAE and ISE are
recorded in the same table. The initial conditions for the manipulator in simulation process are
chosen as follows: , and .
FIGURE 4: The responses of the two joints angles, the control signal and the s-functons of robot
manipultor using SMC without disturbance.
9. Azza Ibrahim & Sawsan Gharghory
International Journal of Intelligent Systems and Applications in Robotics (IJRA), Volume (9) : Issue (1) :
2018 9
FIGURE 5: The responses of the two joints angles, the control signal and the s-functons of robot
manipultor using SMC with constant disturbance.
FIGURE 6: The responses of the two joints angles, the control signal and the s-functons of robot
manipultor using SMC with sinsoidal disturbance.
10. Azza Ibrahim & Sawsan Gharghory
International Journal of Intelligent Systems and Applications in Robotics (IJRA), Volume (9) : Issue (1) :
2018 10
FIGURE 7: The responses of the two joints angles, the control signal and the s-functons of robot
manipultor using STC tuned manually or by PSO without disturbance.
FIGURE 8: The responses of the two joints angles, the control signal and the s-functons of robot
manipultor using STC tuned manually or by PSO with constant disturbance.
11. Azza Ibrahim & Sawsan Gharghory
International Journal of Intelligent Systems and Applications in Robotics (IJRA), Volume (9) : Issue (1) :
2018 11
FIGURE 9: The responses of the two joints angles, the control signal and the s-functons of robot
manipultor using STC tuned manually or by PSO with sinsoidal disturbance.
FIGURE 10: Cost function of manipulator with
STC tuned by PSO using ISE Indices.
FIGURE 11:. Cost function of manipulator
with STC tuned by PSO using IAE Indices.
With Sine DisturbanceWith Constant DisturbanceNominal ModelModel Parameters
PSOWithout PSOPSOWithout PSOPSOWithout PSO
0.0620.08090.0620.08090.0620.080Rise Time
44.109444.144.1Settling Time
000000Over shoot
12.9213.9312.9113.7212.9514ISE
345378.8345.8360347.2378.8IAE
TABLE 2: Transient response characteristics of the first link (q1) with step input.
12. Azza Ibrahim & Sawsan Gharghory
International Journal of Intelligent Systems and Applications in Robotics (IJRA), Volume (9) : Issue (1) :
2018 12
With Sine DisturbanceWith Constant DisturbanceNominal ModelModel Parameters
PSOWithout PSOPSOWithout PSOPSOWithout PSO
0.0660.09280.0660.09280.0660.0928Rise Time
3.974.263344.495044.13Settling Time
000000Over shoot
12.9213.9312.9113.7212.9514ISE
345378.8345.8360347.2378.8IAE
TABLE 3: Transient response characteristics of the second link (q2) with step input.
The simulation results prove the ability of the designed STC whose parameters are tuned using
PSO to achieve the demand output and converge to its desired value in a short rise time and
settled in a small time if compared to the results of STC in which its parameters adjusted
manually as shown in figures (7-9). Besides, the designed control input to the robot manipulator
using the STC is efficient, robust to any type of disturbances and without the chattering problem
comparing to the control input of the conventional SMC whose results are shown in figures (4-6).
It is noticed that the control signal suffers from chattering, and the second joint angle has large
overshoot in case of existing constant disturbance and the system move away and return around
the sliding surface before settle in presence of sinusoidal disturbance.
Also, PSO technique proves its simplicity as heuristic algorithm and its ability to tune the STC
parameters in less time without the needs for knowledge about the boundary values of external
disturbance if comparing with the manual adjusting to the parameters of STC. In addition, the
transient response characteristics of system with STC whose parameters tuned by PSO are
faster comparing to those with the manually adjusted of STC. It is found that the rise time of the
controlled system using STC tuned by PSO ranges from 0.0062 to 0.0066 for the first and second
link respectively, and the settling time for both of the two links and in presence of external
disturbance equals 4 sec and 3.97 sec. respectively, and the outputs with no overshoots as
depicted in Table 2 and Table 3. The global best values found for STC parameters using the
iterative PSO algorithm are depicted in Table 4.
STC parameters Values
2
2
C1 2.9
C2 4
TABLE 4: The Parameters of STC Tuned by PSO.
7. CONCLUSION
This work contributes to the control problem of the two links robotic manipulator by utilizing two
robust controllers' methodology. The first one is the sliding mode controllers that ensure system
insensitivity to external disturbance. However, they exhibit a serious drawback which is high
frequency oscillations named chattering. The second methodology is the super-twisting sliding
mode control. The simulation results of STC controller to robotic manipulator prove its robustness
and ability to suppress the chattering problem associated with the controlled system by the
conventional SMC. Also, STC guarantees high accuracy in presence of external disturbance and
can tolerate any type of disturbance.
The main contribution of this work is the utilizing of intelligent optimization technique to tune the
parameters of STC algorithm, Instead of the manually adjusted to the parameters of STC which is
time consuming and a burden process. To the best of the authors’ knowledge, the integration of
the Particle Swarm Optimization with the Super Twisting Control is a novel approach. PSO is
more suitable than the other optimization heuristic techniques due to its simplicity, its quick
convergence and consume a small time in tuning the STC. The results prove the efficiency of the
13. Azza Ibrahim & Sawsan Gharghory
International Journal of Intelligent Systems and Applications in Robotics (IJRA), Volume (9) : Issue (1) :
2018 13
proposed STC controller tuned by PSO and its ability in convergence to be around the desired
position value in small rise time and settling time compared to the STC whose parameters
adjusted manually, even in the presence of noise. The transient response of system with STC
tuned by PSO is faster and its rise time is almost around 0.0066sec., while the settling time for
both of the two links in presence of external disturbance is approximately around 4 sec., and the
outputs have no overshoots.
8. FUTURE WORK
The integration of the particle swarm optimization with the super twisting control algorithm will be
used for robot has more degree of freedom. Using adaptive super twisting technique to get
adaptive gains which is important point to design the controller with reduced knowledge on
uncertainties and perturbations. Try to use other optimization techniques for comparison with
PSO.
9. REFERENCES
[1] Hasanifard, Goran, Habib Nejad Korayem, Moharam, and Nikoobin, Amin, " Robust Nonlinear
Control of Two Links Robot manipulator and Computing Maximum Load ", World Academy of Science,
Engineering and Technology 50 2009.
[2] B.R. Markiewicz. “Analysis of the Computed Torque Drive Method and Comparison with Conventional
Position Servo for a Computer-Controlled Manipulator,” NASA-JPL Technical Memo, 33-61, Mar.
1973.
[3] T. J. Tarn, A. K. Bejczy, A. sidori, and Y. Chen. “Nonlinear feedback in robot arm control,” in Proc.
IEEE Conference on Decision and Control, Las Vegas, 1984, pp. 736-751 ‘ORC D: Connecting
research and researchers’, http://orcid.org/, accessed 3 December 2014
[4] tzhak Levi, Nadav Berman and Amit Ailon, “Robust Adaptive Nonlinear H∞ Control for Robot
Manipulators”, proceedings of 15th Mediterranean Conference on Control & Automation, July 27-29,
2007, Athens – Greece.
[5] Rincy Koshy, Jayasree P R, “Comparative Study of H-infinity and Sliding Mode Control for a
Manipulator with Oscillatory-Base”, 2017 nternational Conference on circuits Power and Computing
Technologies [ICCPCT].
[6] Azza El-sayed brahim “Wheeled Mobile Robot Trajectory Tracking using Sliding Mode Control “,
Journal of Computer Sciences 2016, 12 (1): 48.55, DOI: 10.3844/jcssp.2016.48.55.
[7] K. David Young, Vadim . Utkin, “A Control Engineer’s Guide to Sliding Mode Control”, EEE
transactions on control systems technology, vol. 7, no. 3, may 1999.
[8] W. Perruquetti, J. P. Barbot. "Sliding Mode Control in Engineering", FL, USA: CRC Press, 2002.
CrossRef Google Scholar.
[9] A. Levant, "Chattering analysis", IEEE Transactions on Automatic Control, vol. 55, no. 6, pp. 1380–
1389, 2010.MathSciNetCrossRefGoogle Scholar
[10] Nasri A, Hazzab A, Bousserhane IK, Hadjeri S, Sicard P. Two wheel speed robust sliding mode control
for electric vehicle drive. Serb J Elect Eng 2008; 5: 199-216.
[11] Senol I, Demirtas M, Rustemov S, Gumus B. "Position control of induction motor a new-bounded fuzzy
sliding mode controller". COMPEL 2005; 24: 145-157.
[12] Demirtas M. "DSP-based sliding mode speed control of induction motor using neuro-genetic structure".
Int J Exp Sys Appl 2009; 36: 5533-5540.
[13] Dastranj MR, Moghaddas M, Ghezi Y, Rouhani M. "Robust control of inverted pendulum using fuzzy
sliding mode control and genetic algorithm". Int J Info Elect Eng 2012; 2: 773-776.