Adaptive Learning and Control for MIMO System Based on Adaptive Dynamic Programming

被引:78
作者
Fu, Jian [1 ]
He, Haibo
Zhou, Xinmin [1 ]
机构
[1] Wuhan Univ Technol, Sch Automat, Wuhan 430070, Hubei, Peoples R China
来源
IEEE TRANSACTIONS ON NEURAL NETWORKS | 2011年 / 22卷 / 07期
基金
美国国家科学基金会;
关键词
Adaptive dynamic programming; Levenberg-Marquardt method; looper system; multiple-input-multiple-output; online learning and control; TIME NONLINEAR-SYSTEMS; NEURAL-NETWORKS; TENSION CONTROL; LOOPER; STRATEGIES; MILLS;
D O I
10.1109/TNN.2011.2147797
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Adaptive dynamic programming (ADP) is a promising research field for design of intelligent controllers, which can both learn on-the-fly and exhibit optimal behavior. Over the past decades, several generations of ADP design have been proposed in the literature, which have demonstrated many successful applications in various benchmarks and industrial applications. While many of the existing researches focus on multiple-inputs-single-output system with steepest descent search, in this paper we investigate a generalized multiple-input-multiple-output (GMIMO) ADP design for online learning and control, which is more applicable to a wide range of practical real-world applications. Furthermore, an improved weight-updating algorithm based on recursive Levenberg-Marquardt methods is presented and embodied in the GMIMO approach to improve its performance. Finally, we test the performance of this approach based on a practical complex system, namely, the learning and control of the tension and height of the looper system in a hot strip mill. Experimental results demonstrate that the proposed approach can achieve effective and robust performance.
引用
收藏
页码:1133 / 1148
页数:16
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