Switched reluctance motor circuit drive system using adaptive nonlinear backstepping control with mended recurrent Romanovski polynomials neural network and mended particle swarm optimization

2019 ◽  
pp. e2629 ◽  
Author(s):  
Chih‐Hong Lin ◽  
Kuo‐Tsai Chang
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Switched Reluctance Motor ◽  
Drive System ◽  
Swarm Optimization ◽  
Motor Circuit ◽  
Switched Reluctance ◽  
Reluctance Motor ◽  
Nonlinear Backstepping Control

Adaptive nonlinear backstepping control using mended recurrent Romanovski polynomials neural network and mended particle swarm optimization for switched reluctance motor drive system

2019 ◽  
Vol 41 (14) ◽  
pp. 4114-4128 ◽  
Author(s):  
Chih-Hong Lin
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Switched Reluctance Motor ◽  
Drive System ◽  
Swarm Optimization ◽  
Switched Reluctance ◽  
Adaptive Law ◽  
Reluctance Motor ◽  
Nonlinear Backstepping Control

A switched reluctance motor (SRM) drive system has highly nonlinear uncertainties owing to a convex construction. It is hard for the linear control methods to achieve good performance for the SRM drive system. An adaptive nonlinear backstepping control system using the mended recurrent Romanovski polynomials neural network and mended PSO with an adaptive law and an error estimated law is proposed to estimate the lumped uncertainty and to compensate the estimated error in order to enhance the robustness of the SRM drive system. Additionally, in accordance with the Lyapunov stability theorem, the adaptive law in the mended recurrent Romanovski polynomials neural network and the error estimated law are established. Furthermore, to help improve convergence and to obtain better learning performance, the mended particle swarm optimization (PSO) algorithm is utilized for adjusting the two varied learning rates of the two parameters in the mended recurrent Romanovski polynomials neural network. Finally, some experimental results and a comparative analysis are verified that the proposed control scheme has better control performances for controlling the SRM drive system.

New Neural Network Inverse Control of Two-Motor Drive System

2013 ◽  
Vol 416-417 ◽  
pp. 447-453
Author(s):  
Mei Kang ◽  
Wen Xiang Zhao ◽  
Jing Hua Ji ◽  
Guo Hai Liu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Motor System ◽  
Rbf Neural Network ◽  
Particle Swarm ◽  
Drive System ◽  
Motor Drive ◽  
Swarm Optimization ◽  
Synchronous System ◽  
Motor Drive System

Two-motor drive system is a multi-variable, nonlinear and strongly coupled system. A new synchronous control strategy for two-motor system is proposed based on radial basis function (RBF) neural network inverse with particle swarm optimization. To enhance the system performance, the particle swarm optimization is adopted to optimize the RBF nerve center, an optimized RBF neural network inverse and a two-motor system is connected in series to form composite pseudo-linear system. This two-motor synchronous system can be decoupled into two independent linear subsystems for speed and tension. Then, the decoupled control is implemented by designing a linear closed-loop adjustor. The experimental results verify that the two-motor synchronous system can be decoupled well for speed and tension based on the proposed neural network inverse system. Also, the proposed system can deal with external disturbances with strong robustness.

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