改进的神经网络观测器在非线性系统中的应用  

Improved Neural Network State Observer Designed for Nonlinear System

作　　者：姜寅令[1] 李艳辉[1] 王海星[1] 

机构地区：[1]东北石油大学电气信息工程学院,黑龙江大庆163318

出　　处：《吉林大学学报（信息科学版）》2015年第4期471-475,共5页Journal of Jilin University（Information Science Edition）

基　　金：黑龙江省科学基金资助项目(QC2013C066);黑龙江省普通高等学校青年学术骨干支持计划基金资助项目(1254G004)

摘　　要：为降低非线性观测器对模型精度的依赖性,提出一种非传统的神经网络观测器设计方法。该神经网络为三层前馈网络,采用带修正项的误差反传算法进行训练,以保证控制的精度和权值有界,利用神经网络识别系统的非线性部分,并结合传统的龙伯格观测器重构系统状态;利用Lyapunov直接法保证基于权值误差的非观测器的稳定性,并将该观测器应用于机器人轨迹跟踪控制中。仿真结果表明,该方法解决了模型不确定系统状态观测问题,适用于模型精度较低的非线性系统。For reducing the dependence of nonlinear observer on the precision model, a non conventional NN （Neural Network） observer for nonlinear system is proposed. The neuro-observer is a three-layer feedforward neural network, which is trained extensively with the error backpropagation learning algorithm including a correction term to guarantee good tracking and bounded NN weights. Designing using artificial neural network to identify the nonlinear parts of the system and the neural network observer is using a Luenberger observer to reconstruct the states of the system. The Lyapunov direct method is used in order to ensure the stability of the proposed non-conventional observer. The proposed observer is applied to 2 degrees of freedom horizontal manipulator to evaluate its performance. The simulation results show that the state observation of uncertain systems can be solved by the method and it is suitable for the low precision model of the nonlinear system.

关 键 词：神经网络观测器 非线性系统 机器人 

分 类 号：TH868[机械工程—仪器科学与技术]