Policy-Iteration-Based Active Disturbance Rejection Control for Uncertain Nonlinear Systems With Unknown Relative Degree

IF 10.5 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Sesun You;Kwankyun Byeon;Jiwon Seo;Wonhee Kim;Masayoshi Tomizuka
{"title":"Policy-Iteration-Based Active Disturbance Rejection Control for Uncertain Nonlinear Systems With Unknown Relative Degree","authors":"Sesun You;Kwankyun Byeon;Jiwon Seo;Wonhee Kim;Masayoshi Tomizuka","doi":"10.1109/TCYB.2025.3532518","DOIUrl":null,"url":null,"abstract":"In this article, a policy-iteration-based active disturbance rejection control (ADRC) is proposed for uncertain nonlinear systems to achieve real-time output tracking performance, regardless of the specific relative degree of the system. The approach integrates a partial control input generator with a policy-iteration-based reinforcement learning (RL) agent for degree weight adjustment. The partial control input generator includes each ith order partial control input, which is constructed following the ADRC design framework for an ith order system. The RL agent adjusts the degree weights (its actions) to enhance the dominance of the partial control input corresponding to the unknown relative degree through iterative policy refinement. The RL agent is designed to minimize the quadratic reward as the performance index function while enhancing the influence of the partial control input associated with the correct relative degree via the policy iteration procedure. All signals in the closed-loop system (including the time-varying degree weights) ensure semi-global uniformly ultimately boundness using the Lyapunov stability theorem and the affinely quadratically stable property. Consequently, the degree weight adjustments by the RL agent do not affect the closed-loop stability. The proposed method does not require system dynamics, specific relative degree, external disturbances, and other state variable sensing beyond output sensing. The performance of the proposed method was validated via simulations for two different-order uncertain nonlinear systems and experiments using a permanent magnet synchronous motor testbed.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 3","pages":"1347-1358"},"PeriodicalIF":10.5000,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Cybernetics","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10879124/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

In this article, a policy-iteration-based active disturbance rejection control (ADRC) is proposed for uncertain nonlinear systems to achieve real-time output tracking performance, regardless of the specific relative degree of the system. The approach integrates a partial control input generator with a policy-iteration-based reinforcement learning (RL) agent for degree weight adjustment. The partial control input generator includes each ith order partial control input, which is constructed following the ADRC design framework for an ith order system. The RL agent adjusts the degree weights (its actions) to enhance the dominance of the partial control input corresponding to the unknown relative degree through iterative policy refinement. The RL agent is designed to minimize the quadratic reward as the performance index function while enhancing the influence of the partial control input associated with the correct relative degree via the policy iteration procedure. All signals in the closed-loop system (including the time-varying degree weights) ensure semi-global uniformly ultimately boundness using the Lyapunov stability theorem and the affinely quadratically stable property. Consequently, the degree weight adjustments by the RL agent do not affect the closed-loop stability. The proposed method does not require system dynamics, specific relative degree, external disturbances, and other state variable sensing beyond output sensing. The performance of the proposed method was validated via simulations for two different-order uncertain nonlinear systems and experiments using a permanent magnet synchronous motor testbed.
未知关联度不确定非线性系统的策略迭代自抗扰控制
本文针对不确定非线性系统,提出了一种基于策略迭代的自抗扰控制(ADRC),无论系统的具体相对程度如何,都能实现实时输出跟踪性能。该方法将部分控制输入生成器与基于策略迭代的强化学习(RL)代理集成在一起,用于度权调整。部分控制输入发生器包括每个i阶部分控制输入,该部分控制输入遵循i阶系统的自抗扰设计框架构造。RL agent通过迭代策略细化调整度权重(其行为),增强未知相对度对应的部分控制输入的支配地位。RL智能体的设计是最小化二次奖励作为性能指标函数,同时通过策略迭代过程增强与正确相对程度相关的部分控制输入的影响。利用Lyapunov稳定性定理和仿射二次稳定性质,闭环系统中的所有信号(包括时变度权重)都保证了半全局一致最终有界。因此,RL剂的度权调整不影响闭环稳定性。除了输出感知之外,该方法不需要系统动力学、特定相对度、外部干扰和其他状态变量感知。通过对两个不同阶不确定非线性系统的仿真和永磁同步电机试验台的实验,验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
IEEE Transactions on Cybernetics
IEEE Transactions on Cybernetics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, CYBERNETICS
CiteScore
25.40
自引率
11.00%
发文量
1869
期刊介绍: The scope of the IEEE Transactions on Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or machine, human, and organizations. The scope includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, cognitive systems, decision making, and robotics, to the extent that they contribute to the theme of cybernetics or demonstrate an application of cybernetics principles.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信