电液伺服系统神经网络辨识及控制器设计  

Neural Network Identification and Controller on Electro-hydraulic Servo System

作　　者：韩桂华[1] 王鹏飞[1] 张此军 

机构地区：[1]哈尔滨理工大学机械动力工程学院,黑龙江哈尔滨150080

出　　处：《哈尔滨理工大学学报》2017年第5期18-23,共6页Journal of Harbin University of Science and Technology

基　　金：国家自然科学基金青年科学基金(51405113);国家国际科技合作专项资助合作项目(2012DFR70840)

摘　　要：针对阀控缸电液位置伺服系统非线性建模问题,采用神经网络进行系统模型辨识。采用LM遗传算法对三层BP神经网络的权值和阈值进行修正,通过训练系统的输入/输出数据建立非线性系统辨识模型。基于此模型,设计模糊PI控制器,利用智能权函数在线自动调整和修改模糊控制器的规则。利用x PC技术建立阀控缸伺服实验台,以实验台阶跃输出信号作为改进BP神经网络辨识信号,以实验台正弦输出信号作为验证信号。实验表明:该神经网络辨识模型的可信性得以验证;通过对比智能权函数模糊PI控制器和模糊控制器的实验曲线,表明前者控制效果更好。The neural networks system identification was used in nonlinear model on valve-control-cylinder electro-hydraulic position servo system. The three layers BP neural network weights and threshold were optimized using LM genetic algorithm, the relationship of system input and output was analyzed and neural network identification model was presented. A kind of fuzzy PI controller was designed based on the model,which can automatically adjust and modify the rules of fuzzy controller by using the intelligent weight function. A real-time electro-hydraulic servo test bench was built with the x PC technique. The test bench step output was used to identify in the improved BP neural network and the sinusoidal output was used to verify in experiment. Experiment results show that the credibility is verified on neural network identification model; and that the control effect of the intelligent weight function fuzzy PI controller is better than the fuzzy controller.

关 键 词：改进BP神经网络 系统辨识 智能权函数 模糊控制 电液位置伺服系统 遗传算法 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP273[自动化与计算机技术—控制科学与工程]