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CLC number: TP13

On-line Access: 2024-07-05

Received: 2023-10-06

Revision Accepted: 2024-07-05

Crosschecked: 2024-01-21

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Yan Wei

https://orcid.org/0000-0002-9818-8034

Mingshuang HAO

https://orcid.org/0000-0002-9167-7388

Linlin OU

https://orcid.org/0000-0002-8589-9961

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Frontiers of Information Technology & Electronic Engineering  2024 Vol.25 No.6 P.887-902

http://doi.org/10.1631/FITEE.2300675


Asymmetric time-varying integral barrier Lyapunov function based adaptive optimal control for nonlinear systems with dynamic state constraints


Author(s):  Yan WEI, Mingshuang HAO, Xinyi YU, Linlin OU

Affiliation(s):  College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China

Corresponding email(s):   weiyanok@zjut.edu.cn, haoms@zjut.edu.cn, yuxy@zjut.edu.cn, linlinou@zjut.edu.cn

Key Words:  State constraints, Asymmetric time-varying integral barrier Lyapunov function (ATIBLF), Adaptive optimal control, Nonlinear systems


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Yan WEI, Mingshuang HAO, Xinyi YU, Linlin OU. Asymmetric time-varying integral barrier Lyapunov function based adaptive optimal control for nonlinear systems with dynamic state constraints[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(6): 887-902.

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publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2300675"
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Abstract: 
This paper investigates the issue of adaptive optimal tracking control for nonlinear systems with dynamic state constraints. An asymmetric time-varying integral barrier Lyapunov function (ATIBLF) based integral reinforcement learning (IRL) control algorithm with an actor–critic structure is first proposed. The ATIBLF items are appropriately arranged in every step of the optimized backstepping control design to ensure that the dynamic full-state constraints are never violated. Thus, optimal virtual/actual control in every backstepping subsystem is decomposed with ATIBLF items and also with an adaptive optimized item. Meanwhile, neural networks are used to approximate the gradient value functions. According to the Lyapunov stability theorem, the boundedness of all signals of the closed-loop system is proved, and the proposed control scheme ensures that the system states are within predefined compact sets. Finally, the effectiveness of the proposed control approach is validated by simulations.

基于非对称时变积分障碍李雅普诺夫函数的动态状态约束非线性系统自适应最优控制

魏岩,郝明爽,禹鑫燚,欧林林
浙江工业大学信息工程学院,中国杭州市,310023
摘要:本文研究具有动态状态约束的非线性系统自适应最优跟踪控制问题。首先提出一种基于非对称时变积分障碍李雅普诺夫函数(ATIBLF)的积分强化学习(IRL)控制算法。在最优反步控制设计的每一步中都引入ATIBLF,以确保系统始终满足动态变化的全状态约束。每个子系统中的最优虚拟/实际控制器均用ATIBLF和自适应最优项进行分解,同时利用神经网络来近似最优代价函数梯度。根据李雅普诺夫稳定性定理,证明了闭环系统所有信号的有界性。所提出的控制方案保证了系统状态在预定义的紧集内。最后,通过仿真实验验证了本文所提方法的有效性。

关键词:状态约束;非对称时变积分障碍李雅普诺夫函数(ATIBLF);自适应最优控制;非线性系统

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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