scholarly journals An Adaptive Neural Network Controller Based on PSO and Gradient Descent Method for PMSM Speed Drive

2018 ◽  
Vol 9 (3) ◽  
pp. 1412
Author(s):  
Zribi Ali ◽  
Zaineb Frijet ◽  
Mohamed Chtourou
Keyword(s):  
Neural Network ◽  
Gradient Descent ◽  
Control Method ◽  
Speed Control ◽  
Pso Algorithm ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Adaptive Neural Network ◽  
Neural Network Controller ◽  
Network Controller

In this paper, based on the combination of particle swarm optimization (PSO) algorithm and neural network (NN), a new adaptive speed control method for a permanent magnet synchronous motor (PMSM) is proposed. Firstly, PSO algorithm is adopted to get the best set of weights of neural network controller (NNC) for accelerating the convergent speed and preventing the problems of trapping in local minimum. Then, to achieve high-performance speed tracking despite of the existence of varying parameters in the control system, gradient descent method is used to adjust the NNC parameters. The stability of the proposed controller is analyzed and guaranteed from Lyapunov theorem. The robustness and good dynamic performance of the proposed adaptive neural network speed control scheme are verified through computer simulations.

scholarly journals A New PID Neural Network Controller Design for Nonlinear Processes

2017 ◽  
Vol 27 (04) ◽  
pp. 1850065 ◽  
Author(s):  
Ali Zribi ◽  
Mohamed Chtourou ◽  
Mohamed Djemel
Keyword(s):  
Neural Network ◽  
Controller Design ◽  
Nonlinear Process ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Tuning Method ◽  
Neural Network Controller ◽  
Network Controller ◽  
Pid Neural Network ◽  
Variation Simulation

In this paper, a novel adaptive tuning method of PID neural network (PIDNN) controller for nonlinear process is proposed. The presented method utilizes an improved gradient descent method to adjust PIDNN parameters where the margin stability will be employed to get high tracking performance and robustness with regard to external load disturbance and parameter variation. Simulation results show the effectiveness of the proposed algorithm compared with other well-known learning methods.

scholarly journals Adaptive Neural Network Control with Backstepping for Surface Ships with Input Dead-Zone

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Guoqing Xia ◽  
Xingchao Shao ◽  
Ang Zhao ◽  
Huiyong Wu
Keyword(s):  
Neural Network ◽  
Explicit Knowledge ◽  
Dead Zone ◽  
Nonlinear Function ◽  
Lyapunov Theory ◽  
Adaptive Neural Network ◽  
Neural Network Controller ◽  
Network Controller ◽  
Adaptive Neural Network Control ◽  
Fuzzy Logic Technique

This paper addresses the problem of adaptive neural network controller with backstepping technique for fully actuated surface vessels with input dead-zone. The combination of approximation-based adaptive technique and neural network system is used for approximating the nonlinear function of the ship plant. Through backstepping and Lyapunov theory synthesis, an indirect adaptive network controller is derived for dynamic positioning ships without dead-zone property. In order to improve the control effect, a dead-zone compensator is derived using fuzzy logic technique to handle the dead-zone nonlinearity. The main advantage of the proposed controller is that it can be designed without explicit knowledge about the ship motion model, and dead-zone nonlinearity is well compensated. A set of simulations is carried out to verify the performance of the proposed controller.

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