International Journal of Power Electronics and Drive Systems (IJPEDS)
In order to improve the control accuracy of the robot manipulator, the sliding mode control combi... more In order to improve the control accuracy of the robot manipulator, the sliding mode control combined with the adaptive neural network (ANNSMC) is proposed. Sliding mode control (SMC) is a nonlinear control recognized for its efficiency, easy tuning and implementation, accuracy and robustness. However, higher amplitude of chattering is produced due to the higher switching gain to handle the large uncertainties. For the purpose of reducing this gain, the uncertain parts of the system are estimated using neural network (NN) with on-line training using back propagation (BP) technique. The results of the online interconnection weights between the input and the hidden layers and between the hidden and the output layers are injected offline in order to improve the network performance in term of the convergence speed. In order to reduce the response time caused by the online training, the obtained output and input weights are updated using the adaptive laws derived from the Lyapunov stabili...
The optimum neural network combined with sliding mode control (ONNSMC) introduces the approach as... more The optimum neural network combined with sliding mode control (ONNSMC) introduces the approach as a means of developing a strong controller for a robot system with two links. Sliding mode control is a strong control method that has found widespread use in a variety of disciplines and recognized for its efficiency and easy tuning to solve a wide variety of control issues using nonlinear dynamics. Nevertheless, the uncertainties in complex nonlinear systems are huge, the higher switching gain leads to an increase of the chattering amplitude. To mitigate this gain, a neural network (NN) is utilized to predict the uncertain sections of the system plant with on-line training using the backpropagation (BP) technique. The learning rate is a hyperparameter of BP algorithm which has an important effect on the results. This parameter controls how much the weights of the network are updated during each training iteration. Typically, the learning rate is set to a value ranging from 0.1 to 1. In...
This work presents the neural network combined with the sliding mode control (NNSMC) to design a ... more This work presents the neural network combined with the sliding mode control (NNSMC) to design a robust controller for the two-links robot system. Sliding mode control (SMC) is well known for its robustness and efficiency to deal with a wide range of control problems with nonlinear dynamics. However, for complex nonlinear systems, the uncertainties are large and produce higher amplitude of chattering due to the higher switching gain. In order to reduce this gain, neural network (NN) is used to estimate the uncertain parts of the system plant with on-line training using backpropagation (BP) algorithm. The learning rate is one of the parameters of BP algorithm which have a significant influence on results. Particle swarm optimization (PSO) algorithm with global search capabilities is used in this study to optimize this parameter in order to improve the network performance in term of the speed of convergence. The performance of the proposed approach is investigated in simulations and t...
An optimal ∞ tracking-based indirect adaptive fuzzy controller for a class of perturbed uncertain... more An optimal ∞ tracking-based indirect adaptive fuzzy controller for a class of perturbed uncertain affine nonlinear systems without reaching phase is being developed in this paper. First a practical Interval Type-2 (IT2) fuzzy system is used in an adaptive scheme to approximate the system using a nonlinear model and to determine the optimal value of the ∞ gain control. Secondly, to eliminate the trade-off between ∞ tracking performance and high gain at the control input, a modified output tracking error has been used. The stability is ensured through Lyapunov synthesis and the effectiveness of the proposed method is proved and the simulation is also given to illustrate the superiority of the proposed approach.
International Journal of Power Electronics and Drive Systems (IJPEDS)
In order to improve the control accuracy of the robot manipulator, the sliding mode control combi... more In order to improve the control accuracy of the robot manipulator, the sliding mode control combined with the adaptive neural network (ANNSMC) is proposed. Sliding mode control (SMC) is a nonlinear control recognized for its efficiency, easy tuning and implementation, accuracy and robustness. However, higher amplitude of chattering is produced due to the higher switching gain to handle the large uncertainties. For the purpose of reducing this gain, the uncertain parts of the system are estimated using neural network (NN) with on-line training using back propagation (BP) technique. The results of the online interconnection weights between the input and the hidden layers and between the hidden and the output layers are injected offline in order to improve the network performance in term of the convergence speed. In order to reduce the response time caused by the online training, the obtained output and input weights are updated using the adaptive laws derived from the Lyapunov stabili...
The optimum neural network combined with sliding mode control (ONNSMC) introduces the approach as... more The optimum neural network combined with sliding mode control (ONNSMC) introduces the approach as a means of developing a strong controller for a robot system with two links. Sliding mode control is a strong control method that has found widespread use in a variety of disciplines and recognized for its efficiency and easy tuning to solve a wide variety of control issues using nonlinear dynamics. Nevertheless, the uncertainties in complex nonlinear systems are huge, the higher switching gain leads to an increase of the chattering amplitude. To mitigate this gain, a neural network (NN) is utilized to predict the uncertain sections of the system plant with on-line training using the backpropagation (BP) technique. The learning rate is a hyperparameter of BP algorithm which has an important effect on the results. This parameter controls how much the weights of the network are updated during each training iteration. Typically, the learning rate is set to a value ranging from 0.1 to 1. In...
This work presents the neural network combined with the sliding mode control (NNSMC) to design a ... more This work presents the neural network combined with the sliding mode control (NNSMC) to design a robust controller for the two-links robot system. Sliding mode control (SMC) is well known for its robustness and efficiency to deal with a wide range of control problems with nonlinear dynamics. However, for complex nonlinear systems, the uncertainties are large and produce higher amplitude of chattering due to the higher switching gain. In order to reduce this gain, neural network (NN) is used to estimate the uncertain parts of the system plant with on-line training using backpropagation (BP) algorithm. The learning rate is one of the parameters of BP algorithm which have a significant influence on results. Particle swarm optimization (PSO) algorithm with global search capabilities is used in this study to optimize this parameter in order to improve the network performance in term of the speed of convergence. The performance of the proposed approach is investigated in simulations and t...
An optimal ∞ tracking-based indirect adaptive fuzzy controller for a class of perturbed uncertain... more An optimal ∞ tracking-based indirect adaptive fuzzy controller for a class of perturbed uncertain affine nonlinear systems without reaching phase is being developed in this paper. First a practical Interval Type-2 (IT2) fuzzy system is used in an adaptive scheme to approximate the system using a nonlinear model and to determine the optimal value of the ∞ gain control. Secondly, to eliminate the trade-off between ∞ tracking performance and high gain at the control input, a modified output tracking error has been used. The stability is ensured through Lyapunov synthesis and the effectiveness of the proposed method is proved and the simulation is also given to illustrate the superiority of the proposed approach.
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Papers by siham massou