【作者】 姜万录； 刘伟； 张瑞娟； 陈海军；

【Author】 JIANG Wan-lu LIU Wei ZHANG Rui-juan(Yanshan University,Qinhuangdao,066004,China)

【机构】 燕山大学机械工程学院；

【摘要】 本文提出了一种基于蚁群优化、BP网络学习和RBF网络辨识的智能PID控制策略,有效地克服了BP算法训练过程中收敛慢和易陷入局部极小值的缺陷。利用该控制策略,对PID参数进行在线调整,并应用于材料试验机电液位置伺服系统。仿真结果表明,与各种传统PID控制策略相比,采用该智能PID控制器控制的系统具有更好的动态响应特性和抗扰动能力。更多还原

【Abstract】 A new intelligent PID control method based on Ant Colony Optimization(ACO)、BP neural network and RBF neural network is proposed in this paper,and solved the systemic error immersed in a local minimum and accelerate the network’s convergence during the training of BP neural network effectively.This method can automatically adjust PID parameters to obtain satisfied performance,and was applied in the position server electro-hydraulic material testing system.The simulation result show that the intelligent PID controller possessed better dynamic characteristics and robust performance than traditional one.更多还原