Full Text:   <628>

Summary:  <305>

Suppl. Mater.: 

CLC number: TP13

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2024-01-21

Cited: 0

Clicked: 1208

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

-   Go to

Article info.
Open peer comments

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


Share this article to: More <<< Previous Article|

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.

@article{title="Asymmetric time-varying integral barrier Lyapunov function 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",
volume="25",
number="6",
pages="887-902",
year="2024",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2300675"
}

%0 Journal Article
%T Asymmetric time-varying integral barrier Lyapunov function 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
%V 25
%N 6
%P 887-902
%@ 2095-9184
%D 2024
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2300675

TY - JOUR
T1 - Asymmetric time-varying integral barrier Lyapunov function based adaptive optimal control for nonlinear systems with dynamic state constraints
A1 - Yan WEI
A1 - Mingshuang HAO
A1 - Xinyi YU
A1 - Linlin OU
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 25
IS - 6
SP - 887
EP - 902
%@ 2095-9184
Y1 - 2024
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2300675


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

Reference

[1]Bhasin S, Kamalapurkar R, Johnson M, et al., 2013. A novel actor–critic–identifier architecture for approximate optimal control of uncertain nonlinear systems. Automatica, 49(1):82-92.

[2]Chen B, Liu XP, Liu KF, et al., 2009. Direct adaptive fuzzy control of nonlinear strict-feedback systems. Automatica, 45(6):1530-1535.

[3]Jin X, Xu JX, 2014. A barrier composite energy function approach for robot manipulators under alignment condition with position constraints. Int J Robust Nonl Contr, 24(17):2840-2851.

[4]Li DY, Ge SS, Lee TH, 2021. Simultaneous arrival to origin convergence: sliding-mode control through the norm-normalized sign function. IEEE Trans Automat Contr, 67(4):1966-1972.

[5]Li DY, Yu HY, Tee KP, et al., 2022. On time-synchronized stability and control. IEEE Trans Syst Man Cybern Syst, 52(4):2450-2463.

[6]Li Y, Qiang S, Zhuang X, et al., 2004. Robust and adaptive backstepping control for nonlinear systems using RBF neural networks. IEEE Trans Neur Netw, 15(3):693-701.

[7]Li YM, Fan YL, Li KW, et al., 2022a. Adaptive optimized backstepping control-based RL algorithm for stochastic nonlinear systems with state constraints and its application. IEEE Trans Cybern, 52(10):10542-10555.

[8]Li YM, Zhang JX, Liu W, et al., 2022b. Observer-based adaptive optimized control for stochastic nonlinear systems with input and state constraints. IEEE Trans Neur Netw Learn Syst, 33(12):7791-7805.

[9]Liu BJ, Hou MS, Ni JK, et al., 2020. Asymmetric integral barrier Lyapunov function-based adaptive tracking control considering full-state with input magnitude and rate constraint. J Franklin Inst, 357(14):9709-9732.

[10]Liu L, Gao TT, Liu YJ, et al., 2021. Time-varying IBLFs-based adaptive control of uncertain nonlinear systems with full state constraints. Automatica, 129:109595.

[11]Liu L, Chen AQ, Liu YJ, 2022. Adaptive fuzzy output-feedback control for switched uncertain nonlinear systems with full-state constraints. IEEE Trans Cybern, 52(8):7340-7351.

[12]Liu YC, Zhu QD, Wen GX, 2022. Adaptive tracking control for perturbed strict-feedback nonlinear systems based on optimized backstepping technique. IEEE Trans Neur Netw Learn Syst, 33(2):853-865.

[13]Liu YJ, Ma L, Liu L, et al., 2020. Adaptive neural network learning controller design for a class of nonlinear systems with time-varying state constraints. IEEE Trans Neur Netw Learn Syst, 31(1):66-75.

[14]Luo X, Mu DR, Wang Z, et al., 2023. Adaptive full-state constrained tracking control for mobile robotic system with unknown dead-zone input. Neurocomputing, 524:31-42.

[15]Mei KQ, Ding SH, Chen CC, 2022. Fixed-time stabilization for a class of output-constrained nonlinear systems. IEEE Trans Syst Man Cybern Syst, 52(10):6498-6510.

[16]Mohammadi M, Arefi MM, Setoodeh P, et al., 2021. Optimal tracking control based on reinforcement learning value iteration algorithm for time-delayed nonlinear systems with external disturbances and input constraints. Inform Sci, 554:84-98.

[17]Shen LY, Wang HQ, Yue HX, 2022. Prescribed performance adaptive fuzzy control for affine nonlinear systems with state constraints. IEEE Trans Fuzzy Syst, 30(12):5351-5360.

[18]Su QY, Wan M, 2020. Adaptive neural dynamic surface output feedback control for nonlinear full states constrained systems. IEEE Access, 8:131590-131600.

[19]Tee KP, Ge SS, 2012. Control of state-constrained nonlinear systems using integral barrier Lyapunov functionals. Proc IEEE 51st IEEE Conf on Decision and Control, p.3239-3244.

[20]Vamvoudakis KG, Lewis FL, 2010. Online actor-critic algorithm to solve the continuous-time infinite horizon optimal control problem. Automatica, 46(5):878-888.

[21]Wang N, Fu ZM, Song SZ, et al., 2022a. Barrier-Lyapunov-based adaptive fuzzy finite-time tracking of pure-feedback nonlinear systems with constraints. IEEE Trans Fuzzy Syst, 30(4):1139-1148.

[22]Wang N, Gao Y, Liu YJ, et al., 2022b. Self-learning-based optimal tracking control of an unmanned surface vehicle with pose and velocity constraints. Int J Robust Nonl Contr, 32(5):2950-2968.

[23]Wei QL, Song RZ, Yan PF, 2016. Data-driven zero-sum neuro-optimal control for a class of continuous-time unknown nonlinear systems with disturbance using ADP. IEEE Trans Neur Netw Learn Syst, 27(2):444-458.

[24]Wei Y, Wang YY, Ahn CK, et al., 2021. IBLF-based finite-time adaptive fuzzy output-feedback control for uncertain MIMO nonlinear state-constrained systems. IEEE Trans Fuzzy Syst, 29(11):3389-3400.

[25]Wei Y, Hao M, Yu X, et al., 2023. Adaptive neural optimal control of a robot manipulator with time-varying state constraints. Int Annual Conf on Complex Systems and Intelligent Science, p.294-301.

[26]Wen GX, Chen CLP, Li WN, 2020. Simplified optimized control using reinforcement learning algorithm for a class of stochastic nonlinear systems. Inform Sci, 517:230-243.

[27]Wen GX, Chen CLP, Ge SS, 2021. Simplified optimized backstepping control for a class of nonlinear strict-feedback systems with unknown dynamic functions. IEEE Trans Cybern, 51(9):4567-4580.

[28]Xu ZB, Sun CB, Liu QY, 2023. Output-feedback prescribed performance control for the full-state constrained nonlinear systems and its application to DC motor system. IEEE Trans Syst Man Cybern Syst, 53(7):3898-3907.

[29]Zhang HG, Liu Y, Wang YC, 2021. Observer-based finite-time adaptive fuzzy control for nontriangular nonlinear systems with full-state constraints. IEEE Trans Cybern, 51(3):1110-1120.

[30]Zhang LL, Zhu LC, Hua CC, et al., 2023. Adaptive decentralized control for interconnected time-delay uncertain nonlinear systems with different unknown control directions and deferred full-state constraints. IEEE Trans Neur Netw Learn Syst, 34(12):10789-10801.

[31]Zhang YX, Liang XL, Li DY, et al., 2024a. Adaptive safe reinforcement learning with full-state constraints and constrained adaptation for autonomous vehicles. IEEE Trans Cybern, 54(3):1907-1920.

[32]Zhang YX, Liang XL, Li DY, et al., 2024b. Barrier Lyapunov function-based safe reinforcement learning for autonomous vehicles with optimized backstepping. IEEE Trans Neur Netw Learn Syst, 35(2):2066-2080.

[33]Zhao K, Song YD, Chen CLP, et al., 2020. Control of nonlinear systems under dynamic constraints: a unified barrier function-based approach. Automatica, 119:109102.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE