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Rong-jong Wai - One of the best experts on this subject based on the ideXlab platform.
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design of fuzzy neural network inherited backstepping control for Robot Manipulator including actuator dynamics
IEEE Transactions on Fuzzy Systems, 2014Co-Authors: Rong-jong Wai, Rajkumar MuthusamyAbstract:This study presents the design and analysis of an intelligent control system that inherits the systematic and recursive design methodology for an n-link Robot Manipulator, including actuator dynamics, in order to achieve a high-precision position tracking with a firm stability and robustness. First, the coupled higher order dynamic model of an n-link Robot Manipulator is introduced briefly. Then, a conventional backstepping control (BSC) scheme is developed for the joint position tracking of the Robot Manipulator. Moreover, a fuzzy-neural-network-inherited BSC (FNNIBSC) scheme is proposed to relax the requirement of detailed system information to improve the robustness of BSC and to deal with the serious chattering that is caused by the discontinuous function. In the FNNIBSC strategy, the FNN framework is designed to mimic the BSC law, and adaptive tuning algorithms for network parameters are derived in the sense of the projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. Numerical simulations and experimental results of a two-link Robot Manipulator that are actuated by dc servomotors are provided to justify the claims of the proposed FNNIBSC system, and the superiority of the proposed FNNIBSC scheme is also evaluated by quantitative comparison with previous intelligent control schemes.
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fuzzy neural network inherited sliding mode control for Robot Manipulator including actuator dynamics
IEEE Transactions on Neural Networks, 2013Co-Authors: Rong-jong Wai, Rajkumar MuthusamyAbstract:This paper presents the design and analysis of an intelligent control system that inherits the robust properties of sliding-mode control (SMC) for an n-link Robot Manipulator, including actuator dynamics in order to achieve a high-precision position tracking with a firm robustness. First, the coupled higher order dynamic model of an n-link Robot Manipulator is briefy introduced. Then, a conventional SMC scheme is developed for the joint position tracking of Robot Manipulators. Moreover, a fuzzy-neural-network inherited SMC (FNNISMC) scheme is proposed to relax the requirement of detailed system information and deal with chattering control efforts in the SMC system. In the FNNISMC strategy, the FNN framework is designed to mimic the SMC law, and adaptive tuning algorithms for network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. Numerical simulations and experimental results of a two-link Robot Manipulator actuated by DC servo motors are provided to justify the claims of the proposed FNNISMC system, and the superiority of the proposed FNNISMC scheme is also evaluated by quantitative comparison with previous intelligent control schemes.
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Adaptive fuzzy-neural-network velocity sensorless control for Robot Manipulator position tracking
Iet Control Theory and Applications, 2010Co-Authors: Rong-jong Wai, Zhi-wei Yang, Y.c. Huang, C.-y. ShihAbstract:This study focuses on the development of an adaptive fuzzy-neural-network velocity sensorless control (AFNNVSC) scheme for an n -link Robot Manipulator to achieve high-precision position tracking. In general, it is difficult to adopt a model-free design without the joint velocity/acceleration information to achieve this control objective owing to uncertainties in practical applications, such as friction forces, external disturbances and parameter variations. In order to cope with this problem, an AFNNVSC scheme including a non-linear observer and a fuzzy-neural-network (FNN) controller is investigated without the requirement of prior system information. This non-linear observer is used to estimate joint velocities of the Robot Manipulator. Then, a four-layer FNN is utilised for the major control role without auxiliary compensated control, and the adaptive tuning laws of network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the stable control performance. Experimental results of a two-link Robot Manipulator actuated by dc servomotors are given to verify the effectiveness and robustness of the proposed AFNNVSC methodology. In addition, the superiority of the proposed control scheme is indicated in comparison with the proportional-integral-differential control, computed torque control, Takagi-Sugeno-Kang-type fuzzy-neural-network control and robust-neural-fuzzy-network control systems.
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adaptive fuzzy neural network control design via a t s fuzzy model for a Robot Manipulator including actuator dynamics
Systems Man and Cybernetics, 2008Co-Authors: Rong-jong Wai, Zhi-wei YangAbstract:This paper focuses on the development of adaptive fuzzy neural network control (AFNNC), including indirect and direct frameworks for an n-link Robot Manipulator, to achieve high-precision position tracking. In general, it is difficult to adopt a model-based design to achieve this control objective due to the uncertainties in practical applications, such as friction forces, external disturbances, and parameter variations. In order to cope with this problem, an indirect AFNNC (IAFNNC) scheme and a direct AFNNC (DAFNNC) strategy are investigated without the requirement of prior system information. In these model-free control topologies, a continuous-time Takagi-Sugeno (T-S) dynamic fuzzy model with online learning ability is constructed to represent the system dynamics of an n-link Robot Manipulator. In the IAFNNC, an FNN estimator is designed to tune the nonlinear dynamic function vector in fuzzy local models, and then, the estimative vector is used to indirectly develop a stable IAFNNC law. In the DAFNNC, an FNN controller is directly designed to imitate a predetermined model-based stabilizing control law, and then, the stable control performance can be achieved by only using joint position information. All the IAFNNC and DAFNNC laws and the corresponding adaptive tuning algorithms for FNN weights are established in the sense of Lyapunov stability analyses to ensure the stable control performance. Numerical simulations and experimental results of a two-link Robot Manipulator actuated by dc servomotors are given to verify the effectiveness and robustness of the proposed methodologies. In addition, the superiority of the proposed control schemes is indicated in comparison with proportional-differential control, fuzzy-model-based control, T-S-type FNN control, and robust neural fuzzy network control systems.
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robust neural fuzzy network control for Robot Manipulator including actuator dynamics
IEEE Transactions on Industrial Electronics, 2006Co-Authors: Rong-jong Wai, Pochen ChenAbstract:This paper addresses the design and analysis of an intelligent control system for an n-link Robot Manipulator to achieve the high-precision position tracking. According to the concepts of mechanical geometry and motion dynamics, the dynamic model of an n-link Robot Manipulator including actuator dynamics is introduced initially. However, it is difficult to design a suitable model-based control scheme due to the uncertainties in practical applications, such as friction forces, external disturbances, and parameter variations. In order to deal with the mentioned difficulties, a robust neural-fuzzy-network control (RNFNC) system is investigated to the joint position control of an n-link Robot Manipulator for periodic motion. In this control scheme, a four-layer neural fuzzy network (NFN) is utilized for the major control role, and the adaptive tuning laws of network parameters are derived in the sense of a projection algorithm and the Lyapunov stability theorem to ensure network convergence as well as stable control performance. The merits of this model-free control scheme are that not only can the stable position tracking performance be guaranteed but also no prior system information and auxiliary control design are required in the control process. In addition, numerical simulations and experimental results of a two-link Robot Manipulator actuated by dc servo motors are provided to verify the effectiveness and robustness of the proposed RNFNC methodology
Mir Mohammad Ettefagh - One of the best experts on this subject based on the ideXlab platform.
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robust adaptive control of a bio inspired Robot Manipulator using bat algorithm
Expert Systems With Applications, 2016Co-Authors: Mehran Rahmani, Ahmad Ghanbari, Mir Mohammad EttefaghAbstract:A new combined control law (AFOPIDSMC) proposed for chattering reduction.We apply an adaptive controller for updating FOPID parameters.A bio-inspired bat algorithm used for tuning the proposed controller parameters.The stability of the proposed controller is proved by Lyapunov theory.The simulation results show the effectiveness of the proposed control. This paper proposes a novel adaptive fractional order PID sliding mode controller (AFOPIDSMC) using a Bat algorithm to control of a Caterpillar Robot Manipulator. A fractional order PID (FOPID) control is applied to improve both trajectory tracking and robustness. Sliding mode controller (SMC) is one of the control methods which provides high robustness and low tracking error. Using hybridization, a new combined control law is proposed for chattering reduction by means of FOPID controller and high trajectory tracking through using SMC. Then, an adaptive controller design motivated from the SMC is applied for updating FOPID parameters. A metaheuristic approach, the Bat search algorithm based on the echolocation behavior of bats is applied for optimal design of the Caterpillar Robot in order to tune the parameter AFOPIDSMC controllers (BA-AFOPIDSMC). To study the effectiveness of Bat algorithm, its performance is compared with five other controllers such as PID, FOPID, SMC, AFOPIDSMC and PSO-AFOPIDSMC. The stability of the AFOPIDSMC controller is proved by Lyapunov theory. Numerical simulation results completely indicate the advantage of BA-AFOPIDSMC for trajectory tracking and chattering reduction.
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Hybrid neural network fraction integral terminal sliding mode control of an Inchworm Robot Manipulator
Mechanical Systems and Signal Processing, 2016Co-Authors: Mostafa Rahmani, Mehran Rahmani, Amirhossein Ghanbari, Arash Ghanbari, Ahmad Ghanbari, Mir Mohammad EttefaghAbstract:This paper proposes a control scheme based on the fraction integral terminal sliding mode control and adaptive neural network. It deals with the system model uncertainties and the disturbances to improve the control performance of the Inchworm Robot Manipulator. A fraction integral terminal sliding mode control applies to the Inchworm Robot Manipulator to obtain the initial stability. Also, an adaptive neural network is designed to approximate the system uncertainties and unknown disturbances to reduce chattering phenomena. The weight matrix of the proposed adaptive neural network can be updated online, according to the current state error information. The stability of the proposed control method is proved by Lyapunov theory. The performance of the adaptive neural network fraction integral terminal sliding mode control is compared with three other conventional controllers such as sliding mode control, integral terminal sliding mode control and fraction integral terminal sliding mode control. Simulation results show the effectiveness of the proposed control method.
Mehran Rahmani - One of the best experts on this subject based on the ideXlab platform.
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Control of a caterpillar Robot Manipulator using hybrid control
Microsystem Technologies, 2019Co-Authors: Mehran RahmaniAbstract:In recent years, bio-inspired Robots have been applied in different fields such as inspecting of oil and gas pipes, medical devices and rescue issues. Designing an excellent control algorithm for the Caterpillar Robot Manipulator is so difficult due to the high nonlinearity and multi-input/multi-outputs features. In addition, a fast and robust response are the most important tasks in the Robot Manipulator control process. In this paper existing integral terminal sliding mode control (ITSMC) approach for systems is improved by a super-twisting control (STC). Therefore, the chattering phenomena can be reduced by using the STC. This proposed controller is robust because of the combination of two controls. The desired angle of the Robot has been obtained using the proposed controller. The proposed controller is compared with three other controllers such as sliding mode control (SMC), terminal sliding mode control (TSMC) and ITSMC. The numerical simulation results demonstrate that it can obtain better performance by using the proposed controller.
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robust adaptive control of a bio inspired Robot Manipulator using bat algorithm
Expert Systems With Applications, 2016Co-Authors: Mehran Rahmani, Ahmad Ghanbari, Mir Mohammad EttefaghAbstract:A new combined control law (AFOPIDSMC) proposed for chattering reduction.We apply an adaptive controller for updating FOPID parameters.A bio-inspired bat algorithm used for tuning the proposed controller parameters.The stability of the proposed controller is proved by Lyapunov theory.The simulation results show the effectiveness of the proposed control. This paper proposes a novel adaptive fractional order PID sliding mode controller (AFOPIDSMC) using a Bat algorithm to control of a Caterpillar Robot Manipulator. A fractional order PID (FOPID) control is applied to improve both trajectory tracking and robustness. Sliding mode controller (SMC) is one of the control methods which provides high robustness and low tracking error. Using hybridization, a new combined control law is proposed for chattering reduction by means of FOPID controller and high trajectory tracking through using SMC. Then, an adaptive controller design motivated from the SMC is applied for updating FOPID parameters. A metaheuristic approach, the Bat search algorithm based on the echolocation behavior of bats is applied for optimal design of the Caterpillar Robot in order to tune the parameter AFOPIDSMC controllers (BA-AFOPIDSMC). To study the effectiveness of Bat algorithm, its performance is compared with five other controllers such as PID, FOPID, SMC, AFOPIDSMC and PSO-AFOPIDSMC. The stability of the AFOPIDSMC controller is proved by Lyapunov theory. Numerical simulation results completely indicate the advantage of BA-AFOPIDSMC for trajectory tracking and chattering reduction.
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Hybrid neural network fraction integral terminal sliding mode control of an Inchworm Robot Manipulator
Mechanical Systems and Signal Processing, 2016Co-Authors: Mostafa Rahmani, Mehran Rahmani, Amirhossein Ghanbari, Arash Ghanbari, Ahmad Ghanbari, Mir Mohammad EttefaghAbstract:This paper proposes a control scheme based on the fraction integral terminal sliding mode control and adaptive neural network. It deals with the system model uncertainties and the disturbances to improve the control performance of the Inchworm Robot Manipulator. A fraction integral terminal sliding mode control applies to the Inchworm Robot Manipulator to obtain the initial stability. Also, an adaptive neural network is designed to approximate the system uncertainties and unknown disturbances to reduce chattering phenomena. The weight matrix of the proposed adaptive neural network can be updated online, according to the current state error information. The stability of the proposed control method is proved by Lyapunov theory. The performance of the adaptive neural network fraction integral terminal sliding mode control is compared with three other conventional controllers such as sliding mode control, integral terminal sliding mode control and fraction integral terminal sliding mode control. Simulation results show the effectiveness of the proposed control method.
Sebastian Trimpe - One of the best experts on this subject based on the ideXlab platform.
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safe and fast tracking on a Robot Manipulator robust mpc and neural network control
International Conference on Robotics and Automation, 2020Co-Authors: Julian Nubert, Johannes Kohler, Vincent Berenz, Frank Allgower, Sebastian TrimpeAbstract:Fast feedback control and safety guarantees are essential in modern Robotics. We present an approach that achieves both by combining novel robust model predictive control (MPC) with function approximation via (deep) neural networks (NNs). The result is a new approach for complex tasks with nonlinear, uncertain, and constrained dynamics as are common in Robotics. Specifically, we leverage recent results in MPC research to propose a new robust setpoint tracking MPC algorithm, which achieves reliable and safe tracking of a dynamic setpoint while guaranteeing stability and constraint satisfaction. The presented robust MPC scheme constitutes a one-layer approach that unifies the often separated planning and control layers, by directly computing the control command based on a reference and possibly obstacle positions. As a separate contribution, we show how the computation time of the MPC can be drastically reduced by approximating the MPC law with a NN controller. The NN is trained and validated from offline samples of the MPC, yielding statistical guarantees, and used in lieu thereof at run time. Our experiments on a state-of-the-art Robot Manipulator are the first to show that both the proposed robust and approximate MPC schemes scale to real-world Robotic systems.
Mohammad Hassan Khooban - One of the best experts on this subject based on the ideXlab platform.
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robust fuzzy sliding mode control for tracking the Robot Manipulator in joint space and in presence of uncertainties
Robotica, 2014Co-Authors: Mohammad Reza Soltanpour, Mohammad Hassan Khooban, Mahmoodreza SoltaniAbstract:This paper proposes a simple fuzzy sliding mode control to achieve the best trajectory tracking for the Robot Manipulator. In the core of the proposed method, by applying the feedback linearization technique, the known dynamics of the Robot's Manipulator is removed; then, in order to overcome the remaining uncertainties, a classic sliding mode control is designed. Afterward, by applying the TS fuzzy model, the classic sliding mode controller is converted to fuzzy sliding mode controller with very simple rule base. The mathematical analysis shows that the Robot Manipulator with the new proposed control in tracking the Robot Manipulator in presence of uncertainties has the globally asymptotic stability. Finally, to show the performance of the proposed method, the controller is simulated on a Robot Manipulator with two degrees of freedom as case study of the research. Simulation results demonstrate the superiority of the proposed control scheme in presence of the structured and unstructured uncertainties.
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a particle swarm optimization approach for fuzzy sliding mode control for tracking the Robot Manipulator
Nonlinear Dynamics, 2013Co-Authors: Mohammad Reza Soltanpour, Mohammad Hassan KhoobanAbstract:In this paper, an optimal fuzzy sliding mode controller is used for tracking the position of Robot Manipulator, is presented. In the proposed control, initially by using inverse dynamic method, the known sections of a Robot Manipulator’s dynamic are eliminated. This elimination is done due to reduction over structured and unstructured uncertainties boundaries. In order to overcome against existing uncertainties for the tracking position of a Robot Manipulator, a classic sliding mode control is designed. The mathematical proof shows the closed-loop system in the presence of this controller has the global asymptotic stability. Then, by applying the rules that are obtained from the design of classic sliding mode control and TS fuzzy model, a fuzzy sliding mode control is designed that is free of undesirable phenomena of chattering. Eventually, by applying the PSO optimization algorithm, the existing membership functions are adjusted in the way that the error tracking Robot Manipulator position is converged toward zero. In order to illustrate the performance of the proposed controller, a two degree-of-freedom Robot Manipulator is used as the case study. The simulation results confirm desirable performance of optimal fuzzy sliding mode control.
