Noisy-Output-Based Direct Learning Tracking Control With Markov Nonuniform Trial Lengths Using Adaptive Gains

• 成果类型: Article
• 署名作者: Shen, Dong; Saab, Samer S.
• 署名单位: Renmin University of China; Lebanese American University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2021.3106860
• 发表日期: 2022
• 页码: 4123-4130
• 关键词: Markov processes; Noise measurement; CONVERGENCE; transient analysis; Time-varying systems; Task analysis; Stochastic systems; Adaptive gains; Iterative learning control (ILC); Markov chain; nonuniform trial length

摘要

In this article, a noisy-output-based direct learning tracking control is proposed for stochastic linear systems with nonuniform trial lengths. The iteration-varying trial length is modeled using a Markov chain for demonstration of the iteration dependence. The effect of the noisy output is asymptotically eliminated using a prior given decreasing gain sequence in the learning algorithm. Two alternative adaptive gains are presented for improving the tracking performance and the convergence speed. Both the mean-square and almost-sure convergence are provided. Numerical simulations on a four-degree-of-freedom robot arm are presented to illustrate the effectiveness of the proposed scheme.