Adaptive iterative learning controller with input learning technique for a class of uncertain MIMO nonlinear systems

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• 作者：Minsung Kim ; Tae-Yong Kuc ; Hyosin Kim…
• 关键词：Adaptive control ; iterative learning control ; multi ; input multi ; output systems ; nonlinear systems ; robot manipulators ; uncertain systems
• 刊名：International Journal of Control, Automation and Systems
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：15
• 期：1
• 页码：315-328
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Control, Robotics, Mechatronics;
• 出版者：Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers
• ISSN：2005-4092
• 卷排序：15

文摘

In this paper, an adaptive iterative learning controller (AILC) with input learning technique is presented for uncertain multi-input multi-output (MIMO) nonlinear systems in the normal form. The proposed AILC learns the internal parameter of the state equation as well as the input gain parameter, and also estimates the desired input using an input learning rule to track the whole history of command trajectory. The features of the proposed control scheme can be briefly summarized as follows: 1) To the best of authors’ knowledge, the AILC with input learning is first developed for uncertain MIMO nonlinear systems in the normal form; 2) The convergence of learning input error is ensured; 3) The input learning rule is simple; therefore, it can be easily implemented in industrial applications. With the proposed AILC scheme, the tracking error and desired input error converge to zero as the repetition of the learning operation increases. Single-link and two-link manipulators are presented as simulation examples to confirm the feasibility and performance of the proposed AILC.