电液位置伺服系统神经网络辨识的实验研究  

Experimental Research on Neural Network Identification of Electro-hydraulic Position Servo System

作　　者：韩桂华[1] 于凤丽[1] 李建英[1] 

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150000

出　　处：《机床与液压》2016年第5期104-108,共5页Machine Tool & Hydraulics

摘　　要：针对电液伺服系统非线性建模问题,研究了电液位置伺服系统神经网络辨识模型的基本结构。分析伺服系统动态模型的输入、输出关系,依据遗传算法优化神经网络权值和阈值,建立神经网络辨识模型的基本结构。利用xPC技术建立阀控缸实时电液伺服实验台,以实验台的阶跃输出信号作为改进BP神经网络系统辨识信号,以实验台正弦输出信号作为验证信号。结果表明:该神经网络辨识模型的基本结构可达到较高的辨识精度,其可信性得以验证,适用于非线性系统模型辨识。The identification model construction on electro-hydraulic position servo system based on neural networks was studied to use in nonlinear model of the system.The relationship of dynamic model input and output was analyzed and the neural network weights and threshold were optimized using the genetic algorithm,and a basic structure of neural network identification model was presented.A real-time electro-hydraulic servo test bench was built with the xPC 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 high precision is gained and the credibility is verified on neural network identification model structure,and which is applied in nonlinear system model identification.

关 键 词：遗传算法 改进BP神经网络 电液位置伺服系统 系统辨识 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]