Prescribed performance UAV tracking control under disturbance using reinforcement learning-based backstepping

IF 4.6 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Meiying Yang , Hai Zhu , Xiaozhou Zhu , Zhe Liu , Wen Yao , Xiaoqian Chen
{"title":"Prescribed performance UAV tracking control under disturbance using reinforcement learning-based backstepping","authors":"Meiying Yang ,&nbsp;Hai Zhu ,&nbsp;Xiaozhou Zhu ,&nbsp;Zhe Liu ,&nbsp;Wen Yao ,&nbsp;Xiaoqian Chen","doi":"10.1016/j.conengprac.2025.106393","DOIUrl":null,"url":null,"abstract":"<div><div>For the tracking control problem of unmanned aerial vehicle (UAVs) with nonlinear and strongly coupled dynamics, a reinforcement learning (RL) optimization control method with prescribed performance under disturbances is proposed based on the backstepping framework. This method employs RL to solve the Hamilton–Jacobi–Bellman (HJB) equation in the optimization problem, which involves tracking errors and control inputs. Among them, the actor network is used in the controller to ensure system stability, while the critic network is employed to evaluate system performance through performance index functions. Additionally, a reduced-order extended state observer is designed to estimate external disturbances, and the estimated results are applied to the controller to compensate for the impact of external disturbances on the UAV. During controller design, first-order filtering helps resolve the complex differentiation issues inherent in the backstepping method. For the prescribed performance issues, particularly the tracking error constraint, a performance index function guarantees that the tracking error stays within the desired range. Next, the stability performance of the UAV system is proven using Lyapunov theory. Finally, the effectiveness of the proposed control method is further validated through numerical simulations and physical experiments.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106393"},"PeriodicalIF":4.6000,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Control Engineering Practice","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S096706612500156X","RegionNum":2,"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

For the tracking control problem of unmanned aerial vehicle (UAVs) with nonlinear and strongly coupled dynamics, a reinforcement learning (RL) optimization control method with prescribed performance under disturbances is proposed based on the backstepping framework. This method employs RL to solve the Hamilton–Jacobi–Bellman (HJB) equation in the optimization problem, which involves tracking errors and control inputs. Among them, the actor network is used in the controller to ensure system stability, while the critic network is employed to evaluate system performance through performance index functions. Additionally, a reduced-order extended state observer is designed to estimate external disturbances, and the estimated results are applied to the controller to compensate for the impact of external disturbances on the UAV. During controller design, first-order filtering helps resolve the complex differentiation issues inherent in the backstepping method. For the prescribed performance issues, particularly the tracking error constraint, a performance index function guarantees that the tracking error stays within the desired range. Next, the stability performance of the UAV system is proven using Lyapunov theory. Finally, the effectiveness of the proposed control method is further validated through numerical simulations and physical experiments.
基于强化学习的干扰下无人机预定性能跟踪控制
针对具有非线性强耦合动力学特性的无人机跟踪控制问题,提出了一种基于反步框架的具有预定性能的扰动强化学习(RL)优化控制方法。该方法采用强化学习方法求解包含跟踪误差和控制输入的优化问题中的Hamilton-Jacobi-Bellman (HJB)方程。其中,actor网络用于控制器以保证系统的稳定性,而critic网络通过性能指标函数来评价系统的性能。此外,设计了一个降阶扩展状态观测器来估计外部干扰,并将估计结果应用于控制器以补偿外部干扰对无人机的影响。在控制器设计过程中,一阶滤波有助于解决反推法固有的复杂微分问题。对于规定的性能问题,特别是跟踪误差约束,性能指标函数保证跟踪误差保持在期望的范围内。其次,利用李亚普诺夫理论对无人机系统的稳定性进行了验证。最后,通过数值仿真和物理实验进一步验证了所提控制方法的有效性。
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
Control Engineering Practice
Control Engineering Practice 工程技术-工程:电子与电气
CiteScore
9.20
自引率
12.20%
发文量
183
审稿时长
44 days
期刊介绍: Control Engineering Practice strives to meet the needs of industrial practitioners and industrially related academics and researchers. It publishes papers which illustrate the direct application of control theory and its supporting tools in all possible areas of automation. As a result, the journal only contains papers which can be considered to have made significant contributions to the application of advanced control techniques. It is normally expected that practical results should be included, but where simulation only studies are available, it is necessary to demonstrate that the simulation model is representative of a genuine application. Strictly theoretical papers will find a more appropriate home in Control Engineering Practice''s sister publication, Automatica. It is also expected that papers are innovative with respect to the state of the art and are sufficiently detailed for a reader to be able to duplicate the main results of the paper (supplementary material, including datasets, tables, code and any relevant interactive material can be made available and downloaded from the website). The benefits of the presented methods must be made very clear and the new techniques must be compared and contrasted with results obtained using existing methods. Moreover, a thorough analysis of failures that may happen in the design process and implementation can also be part of the paper. The scope of Control Engineering Practice matches the activities of IFAC. Papers demonstrating the contribution of automation and control in improving the performance, quality, productivity, sustainability, resource and energy efficiency, and the manageability of systems and processes for the benefit of mankind and are relevant to industrial practitioners are most welcome.
×
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