Generalized Norm Optimal Iterative Learning Control: Constraint Handling

被引：6

作者：

Chen, Yiyang ^{[1
]}

Chu, Bing ^{[1
]}

Freeman, Christopher T. ^{[1
]}

机构：

[1] Univ Southampton, Dept Elect & Comp Sci, Southampton SO17 1BJ, Hants, England

来源：

IFAC PAPERSONLINE | 2017年 / 50卷 / 01期

关键词：

iterative learning control; constraint handling; TO-POINT TRACKING; SYSTEMS;

D O I：

10.1016/j.ifacol.2017.08.2275

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper proposes a novel control methodology to incorporate constraint handling within generalized iterative learning control (ILC), an overarching methodology which includes intermediate point and sub-interval tracking as special cases. The constrained generalized ILC design objective is first described, and then the design problem is formulated into a successive projection framework. This framework yields a constrained generalized ILC algorithm which embeds system input and output constraints. Convergence analysis of the algorithm is performed and supported by rigorous proofs. The algorithm is verified using a gantry robot experimental platform, whose results reveal its practical efficacy and robustness against plant uncertainty. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

引用

页码：13396 / 13401

页数：6

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