Decentralized Adaptive Neural Inverse Optimal Control of Nonlinear Interconnected Systems

被引：14

作者：

Lu, Kaixin ^{[1
,2
]}

Liu, Zhi ^{[1
,3
]}

Yu, Haoyong ^{[4
]}

Chen, C. L. Philip ^{[5
]}

Zhang, Yun ^{[1
,3
]}

机构：

[1] Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Peoples R China

[2] Natl Univ Singapore, Dept Biomed Engn, Singapore 117583, Singapore

[3] Guangdong Univ Technol, Guangdong HongKong Macao Joint Lab Smart Discrete, Guangzhou 510006, Peoples R China

[4] Natl Univ Singapore, Dept Biomed Engn, Singapore, Singapore

[5] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou, Peoples R China

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2023年 / 34卷 / 11期

关键词：

Optimal control; Interconnected systems; Adaptive systems; Costs; Control systems; Large-scale systems; Artificial neural networks; Adaptive neural control; backstepping; decentralized control; interconnected systems; inverse optimal control; OUTPUT-FEEDBACK CONTROL; TRACKING CONTROL; EVOLUTION SYSTEMS; STABILIZATION;

D O I：

10.1109/TNNLS.2022.3153360

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Existing methods on decentralized optimal control of continuous-time nonlinear interconnected systems require a complicated and time-consuming iteration on finding the solution of Hamilton-Jacobi-Bellman (HJB) equations. In order to overcome this limitation, in this article, a decentralized adaptive neural inverse approach is proposed, which ensures the optimized performance but avoids solving HJB equations. Specifically, a new criterion of inverse optimal practical stabilization is proposed, based on which a new direct adaptive neural strategy and a modified tuning functions method are proposed to design a decentralized inverse optimal controller. It is proven that all the closed-loop signals are bounded and the goal of inverse optimality with respect to the cost functional is achieved. Illustrative examples validate the performance of the methods presented.

引用

页码：8840 / 8851

页数：12

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