FORCE RIPPLE SUPPRESSION TECHNOLOGY FOR LINEAR MOTORS BASED ON BACK PROPAGATION NEURAL NETWORK

作     者：ZHANG Dailin CHEN Youping AI Wu ZHOU Zude KONG Ching Tom 

作者机构：School of Mechanical EngineeringHuazhong University of Science and Technology Wuhan 430074 China Institute of Precision EngTneeringThe Chinese University of Hong Kong Hong Kong China 

出 版 物：《Chinese Journal of Mechanical Engineering》 (中国机械工程学报（英文版）)

年 卷 期：2008年第21卷第2期

页      面：13-16页

核心收录：

学科分类：0817[工学-化学工程与技术] 08[工学] 0807[工学-动力工程及工程热物理] 080203[工学-机械设计及理论] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0801[工学-力学（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China(No. 60474021) 

主　　题：Linear motor (LM) Back propagation(BP) algorithm Neural network Anti-disturbance technology 

摘      要：Various force disturbances influence the thrust force of linear motors when a linear motor （LM） is running. Among all of force disturbances, the force ripple is the dominant while a linear motor runs in low speed. In order to suppress the force ripple, back propagation（BP） neural network is proposed to learn the function of the force ripple of linear motors, and the acquisition method of training samples is proposed based on a disturbance observer. An off-line BP neural network is used mainly because of its high running efficiency and the real-time requirement of the servo control system of a linear motor. By using the function, the force ripple is on-line compensated according to the position of the LM. The experimental results show that the force ripple is effectively suppressed by the compensation of the BP neural network.