JZUS - Journal of Zhejiang University SCIENCE

Frontiers of Information Technology & Electronic Engineering

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ATIBLF-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; ATIBLF; Adaptive optimal control; Nonlinear systems

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Yan WEI, Mingshuang HAO, Xinyi YU, Linlin OU. ATIBLF-based adaptive optimal control for nonlinear systems with dynamic state constraints[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2300675

@article{title="ATIBLF-based adaptive optimal control for nonlinear systems with dynamic state constraints",
author="Yan WEI, Mingshuang HAO, Xinyi YU, Linlin OU",
journal="Frontiers of Information Technology & Electronic Engineering",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/FITEE.2300675"
}

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%T ATIBLF-based adaptive optimal control for nonlinear systems with dynamic state constraints
%A Yan WEI
%A Mingshuang HAO
%A Xinyi YU
%A Linlin OU
%J Frontiers of Information Technology & Electronic Engineering
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%@ 2095-9184
%D in press
%I Zhejiang University Press & Springer
doi="https://doi.org/10.1631/FITEE.2300675"

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T1 - ATIBLF-based adaptive optimal control for nonlinear systems with dynamic state constraints
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A1 - Mingshuang HAO
A1 - Xinyi YU
A1 - Linlin OU
J0 - Frontiers of Information Technology & Electronic Engineering
SP -
EP -
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Y1 - in press
PB - Zhejiang University Press & Springer
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doi="https://doi.org/10.1631/FITEE.2300675"

Abstract
Chinese Summary
Academic Network
Reviewer Comment

Abstract: This article 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 simulation.

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