基于模糊RBF神经网络的双电机驱动伺服系统研究  

Dual-motor driving servo system based on fuzzy RBF neural network

作　　者：赵海波[1] 周向红[2] 

机构地区：[1]铜陵学院电气工程系光电子研究中心,安徽铜陵244000 [2]中国电子科技集团公司第43研究所,安徽合肥230088

出　　处：《安徽工程科技学院学报（自然科学版）》2010年第1期57-61,共5页Journal of Anhui University of Technology and Science

基　　金：光电子应用安徽省工程技术研究中心培育基地基金资助项目

摘　　要：双电机驱动伺服系统中存在齿隙和摩擦等非线性,常规PID控制不能满足其控制要求.针对常规PID控制器参数固定而导致在控制中效果差的缺陷,提出一种基于模糊RBF神经网络整定的PID控制方法.该方法结合了模糊控制的推理能力强与神经网络学习能力强的特点,可以在线调整得到一组适合于控制对象的PID控制参数.最后在双电机驱动伺服系统中进行仿真试验结果表明所提出的控制策略是有效的.Due to the existence of backlash and friction in dual-motor driving servo system, conventional PID control can＇t satisfy its control requirement. In view of the defect that it is difficult to achieve good control effect by using conventional PID control because of the fixed parameter, a kind of PID control based on fuzzy RBF neural network is proposed. This method combines the reasoning ability of fuzzy control and the learning ability of neural network. The PID parameter which suits the plant can be adjusted online. Finally the simulation testing was carried out in dual-motor driving servo system.Simulation illustrates the effectiveness of the proposed control strategy.

关 键 词：齿隙非线性 模糊控制 RBF神经网络 双电机驱动 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]