ADAPTIVE-CONTROL OF DISCRETE-TIME NONLINEAR-SYSTEMS USING RECURRENT NEURAL NETWORKS

被引：59
|
作者：
JIN, L
NIKIFORUK, PN
GUPTA, MM
机构：
[1] Univ of Saskatchewan, Saskatchewan
来源：
IEE PROCEEDINGS-CONTROL THEORY AND APPLICATIONS | 1994年 / 141卷 / 03期
关键词：
NONLINEAR SYSTEMS; ADAPTIVE CONTROL;
D O I：
10.1049/ip-cta:19949976
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A learning and adaptive control scheme for a general class of unknown MIMO discrete-time nonlinear systems using multilayered recurrent neural networks (MRNNs) is presented. A novel MRNN structure is proposed to approximate the unknown nonlinear input-output relationship, using a dynamic back propagation (DBP) learning algorithm. Based on the dynamic neural model, an extension of the concept of the input-output linearisation of discrete-time nonlinear systems is used to synthesise a control technique for model reference control purposes. A dynamic learning control architecture is developed with simultaneous online identification and control. The potentials of the proposed methods are demonstrated by simulation studies.
引用
收藏
页码：169 / 176
页数：8
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