Enhanced P-Type Control: Indirect Adaptive Learning From Set-Point Updates

• 成果类型: Article
• 署名作者: Chi, Ronghu; Li, Huaying; Shen, Dong; Hou, Zhongsheng; Huang, Biao
• 署名单位: Qingdao University of Science & Technology; Renmin University of China; Qingdao University; University of Alberta
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2022.3154347
• 发表日期: 2023
• 页码: 1600-1613
• 关键词: convergence; uncertainty; Analytical models; Adaptive systems; Linear systems; data models; Tuning; Adaptive iterative learning control; convergence analysis; P-type controller; set-point updating method

摘要

In this article, an indirect adaptive iterative learning control (iAILC) scheme is proposed for both linear and nonlinear systems to enhance the P-type controller by learning from set points. An adaptive mechanism is included in the iAILC method to regulate the learning gain using input-output measurements in real time. An iAILC method is first designed for linear systems to improve control performance by fully utilizing model information if such a linear model is known exactly. Then, an iterative dynamic linearization (IDL)-based iAILC is proposed for a nonlinear nonaffine system, whose model is completely unknown. The IDL technique is employed to deal with the strong nonlinearity and nonaffine structure of the systems such that a linear data model can be attained consequently for the algorithm design and performance analysis. The convergence of the developed iAILC schemes is proved rigorously, where contraction mapping, two-dimensional (2-D) Roesser's system theory, and mathematical induction are employed as the basic analysis tools. Simulation studies are provided to verify the developed theoretical results.