{"title":"Neuro-Adaptive-Based Fixed-Time Composite Learning Control for Manipulators With Given Transient Performance.","authors":"Yanli Fan, Chenguang Yang, Bin Li, Yongming Li","doi":"10.1109/TCYB.2024.3414186","DOIUrl":null,"url":null,"abstract":"<p><p>This article investigates an adaptive neural network (NN) control technique with fixed-time tracking capabilities, employing composite learning, for manipulators under constrained position error. The first step involves integrating the composite learning method into the NN to address the dynamic uncertainties that inevitably arise in manipulators. A composite adaptive updating law of NN weights is formulated, requiring adherence solely to the relaxed interval excitation (IE) conditions. In addition, for the output error, instead of knowing the initial conditions, this article integrates the error transfer function and asymmetric barrier function to achieve the specific performance for position error in both steady and transient states. Furthermore, the fixed-time control methodology and Lyapunov stability criterion are synergistically employed in order to guarantee the convergence of all signals in the manipulators to a compact neighborhood around the origin within a fixed-time. Finally, numerical simulation and experiments with the Baxter robot results both determine the capability of the NN composite learning technique and fixed-time control strategy.</p>","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":null,"pages":null},"PeriodicalIF":9.4000,"publicationDate":"2024-07-04","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://doi.org/10.1109/TCYB.2024.3414186","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
This article investigates an adaptive neural network (NN) control technique with fixed-time tracking capabilities, employing composite learning, for manipulators under constrained position error. The first step involves integrating the composite learning method into the NN to address the dynamic uncertainties that inevitably arise in manipulators. A composite adaptive updating law of NN weights is formulated, requiring adherence solely to the relaxed interval excitation (IE) conditions. In addition, for the output error, instead of knowing the initial conditions, this article integrates the error transfer function and asymmetric barrier function to achieve the specific performance for position error in both steady and transient states. Furthermore, the fixed-time control methodology and Lyapunov stability criterion are synergistically employed in order to guarantee the convergence of all signals in the manipulators to a compact neighborhood around the origin within a fixed-time. Finally, numerical simulation and experiments with the Baxter robot results both determine the capability of the NN composite learning technique and fixed-time control strategy.
本文研究了一种具有固定时间跟踪能力的自适应神经网络 (NN) 控制技术,该技术采用了复合学习方法,适用于位置误差受限的机械手。第一步是将复合学习方法集成到 NN 中,以解决操纵器中不可避免出现的动态不确定性。我们制定了 NN 权重的复合自适应更新法,只要求遵守宽松的区间激励 (IE) 条件。此外,对于输出误差,本文不需要知道初始条件,而是整合了误差传递函数和非对称障碍函数,以实现位置误差在稳定和瞬态下的特定性能。此外,本文还采用了固定时间控制方法和 Lyapunov 稳定性准则,以保证在固定时间内机械手的所有信号都收敛到原点周围的紧凑邻域。最后,数值模拟和巴克斯特机器人的实验结果都确定了 NN 复合学习技术和固定时间控制策略的能力。
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.