Robust Adaptive Learning Control for a Class of Nonaffine Nonlinear Systems

• 成果类型: Article
• 署名作者: Gao, Shuai; Shen, Dong; Tayebi, Abdelhamid
• 署名单位: Renmin University of China; Lakehead University; Shandong University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2026.3656105
• 发表日期: 2026
• 关键词: tracking; feedback; consensus; networks; robots; point; ILC

摘要

We address the tracking problem for a class of uncertain nonaffine nonlinear systems with high relative degrees, performing nonrepetitive tasks. We propose a rigorously proven, robust adaptive learning control scheme that relies on a gradient descent parameter adaptation law to handle the unknown time-varying parameters of the system, along with a state estimator that estimates the unmeasurable state variables. Furthermore, despite the inherently complex nature of the nonaffine system, we provide an explicit iterative computation method to facilitate the implementation of the proposed control scheme. This article includes a thorough analysis of the performance of the proposed control strategy, and simulation results are presented to demonstrate the effectiveness of the approach.