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On the Algorithms of Adaptive Neural Network-Based Speed Control of Switched Reluctance Machines

On the Algorithms of Adaptive Neural Network-Based Speed Control of Switched Reluctance Machines
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摘要 <Abstract>A switched reluctance machine(SRM)drive is a time-varying,strongly nonlinear system.High performance control can no longer be achieved by using linear techniques.This paper describes the back-propagation (BP)neural network-based proportional-integral-derivative(PID)speed control of the SRM.It’s the interest of this paper to explore the utilization of the prior empirical knowledge as guidance in the initializing and training of the neural networks.The purpose is to make the networks less sensitive on the initial weights.Two modified algorithms are presented and simulation experiments show some interesting findings about their control effects and their corresponding sensitivity on the initial weights of the networks. A switched reluctance machine (SRM) drive is a time-varying, strongly nonlinear system. High performance control can no longer be achieved by using linear techniques. This paper describes the back-propagation (BP) neural network-based proportional-integral-derivative (PID) speed control of the SRM. It's the interest of this paper to explore the utilization of the prior empirical knowledge as guidance in the initializing and training of the neural networks. The purpose is to make the networks less sensitive on the initial weights. Two modified algorithms are presented and simulation experiments show some interesting findings about their control effects and their corresponding sensitivity on the initial weights of the networks.
作者 陈琼忠 孟光 曾水生 CHEN Qiong-zhong , MENG Guang, ZENG Shui-sheng (State Key Laboratory of Mechanical System and Vibration, Shanghai Jiaotong University, Shanghai 200240, China)
出处 《上海交通大学学报:英文版》 EI 2010年第4期484-491,共8页 Journal of Shanghai Jiaotong university
基金 the Programme of Introducing Talents of Discipline to Universities (No. B06012)
switched reluctance machine (SRM). nonlinear control, neural network, learnin algorithms
作者简介 E-mail gmeng@sjtu.edu.cn
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