Control of a class of nonlinear discrete-time systems using multilayer neural networks

被引：58

作者：

Jagannathan, S ^{[1
]}

机构：

[1] Univ Texas, Dept Elect & Comp Engn, San Antonio, TX 78249 USA

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2001年 / 12卷 / 05期

基金：

美国国家科学基金会;

关键词：

backstepping; discrete-time control; neural-network (NN) control; on-line training; stability;

D O I：

10.1109/72.950140

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A multilayer neural-network (NN) controller is designed to deliver a desired tracking performance for the control of a class of unknown nonlinear systems in discrete time where the system nonlinearities do not satisfy a matching condition. Using the Lyapunov approach, the uniform ultimate boundedness (UUB) of the tracking error and the NN weight estimates are shown by using a novel weight updates. Further, a rigorous procedure is provided from this analysis to select the NN controller parameters. The resulting structure consists of several NN function approximation inner loops and an outer proportional derivative (PD) tracking loop. Simulation results are then carried out to justify the theoretical conclusions. The net result is the design and development of an NN controller for strict-feedback class of nonlinear discrete-time systems.

引用

页码：1113 / 1120

页数：8

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