{"title":"Adaptive Dynamic Programming-Based Fixed-Time Optimal Control for Wheeled Mobile Robot","authors":"Chen Wang;Haoran Zhan;Qing Guo;Tieshan Li","doi":"10.1109/LRA.2024.3504314","DOIUrl":null,"url":null,"abstract":"In this study, the adaptive dynamic programming (ADP)-based fixed-time optimal trajectory tracking control is investigated for wheeled mobile robots. An ADP-based fixed-time optimal tracking controller is developed based on the critic-only neural network ADP technique, which guarantees the robot track the desired trajectory in fixed time. Firstly, to address the solution difficulty of the Hamilton-Jacobi-Bellman (HJB) equation, a critic neural network is used to estimate the cost function. Meanwhile, a weight update law is designed by using the adaptive control technique, which not only removes the persistent or finite excitation condition, but also enables the fixed-time convergence of the weight estimation error. By using the proposed controller, all error variables can converge to a neighborhood of zero in fixed time. Finally, both simulations and physical experiments indicate that the proposed ADP-based fixed-time optimal controller has a faster convergence rate compared to the two comparison controllers.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 1","pages":"176-183"},"PeriodicalIF":4.6000,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Robotics and Automation Letters","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10759734/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ROBOTICS","Score":null,"Total":0}
引用次数: 0
Abstract
In this study, the adaptive dynamic programming (ADP)-based fixed-time optimal trajectory tracking control is investigated for wheeled mobile robots. An ADP-based fixed-time optimal tracking controller is developed based on the critic-only neural network ADP technique, which guarantees the robot track the desired trajectory in fixed time. Firstly, to address the solution difficulty of the Hamilton-Jacobi-Bellman (HJB) equation, a critic neural network is used to estimate the cost function. Meanwhile, a weight update law is designed by using the adaptive control technique, which not only removes the persistent or finite excitation condition, but also enables the fixed-time convergence of the weight estimation error. By using the proposed controller, all error variables can converge to a neighborhood of zero in fixed time. Finally, both simulations and physical experiments indicate that the proposed ADP-based fixed-time optimal controller has a faster convergence rate compared to the two comparison controllers.
期刊介绍:
The scope of this journal is to publish peer-reviewed articles that provide a timely and concise account of innovative research ideas and application results, reporting significant theoretical findings and application case studies in areas of robotics and automation.