Convergence Analysis of Sampled-Data ILC for Locally Lipschitz Continuous Nonlinear Nonaffine Systems With Nonrepetitive Uncertainties

• 成果类型: Article
• 署名作者: Chi, Ronghu; Hui, Yu; Chien, Chiang-Ju; Huang, Biao; Hou, Zhongsheng
• 署名单位: Qingdao University of Science & Technology; Huafan University; University of Alberta; Qingdao University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.3020803
• 发表日期: 2021
• 页码: 3347-3354
• 关键词: convergence; uncertainty; Nonlinear systems; trajectory; Analytical models; tools; Robustness; Locally Lipschitz continuous (LLC); nonaffine nonlinear continuous-time systems; robust convergence analysis; sampled-data iterative learning control (ILC)

摘要

This article investigates a challenging open problem of convergence analysis of sampled-data iterative learning control for locally Lipschitz continuous (LLC) nonlinear nonaffine continuous-time systems. The restrictive repetitive conditions are relaxed by taking the LLC nonlinear nonaffine systems with iteratively variant initial states, reference trajectories, and exogenous disturbances into consideration. A new convergence analysis method is proposed by incorporating the mathematical induction with the contraction mapping principle (MI-CMP) to prove the boundedness of system variables, and bounded convergence of tracking error for a finite iteration as the first step. Then, the mathematical proof by contradiction is utilized along with the MI-CMP to further prove the robust convergence of the tracking error in any iteration to an error bound related to the bounds of the iteration-varying uncertainties and the sampling period. It is also shown that the tracking error converges to a smaller bound if the iteration-varying uncertainties disappear, even to zero if the sampling period approaches zero. In addition, an algorithm is provided to optimize the learning operator under the convergence condition. Theoretical results are further verified by simulations.