Multi-objective iterative learning control using convex optimization

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• 作者：Tong Duy Son ; ^{Tong.DuySon@kuleuven.be} ; Goele Pipeleers ^{Goele.Pipeleers@kuleuven.be} ; Jan Swevers ^{Jan.Swevers@kuleuven.be}
• 关键词：Iterative learning control ; H&infin ; H&infin ; control ; Multi-objective control ; Convex optimization
• 刊名：European Journal of Control
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：33
• 期：Complete
• 页码：35-42
• 全文大小：1329 K
• 卷排序：33

文摘

This paper presents a multi-objective iterative learning control (ILC) design approach that realizes an optimal trade-off between robust convergence, converged tracking performance, convergence speed, and input constraints. Linear time-invariant single-input single-output systems which are represented by both parametric and nonparametric models are considered. The noncausal filter Q(q)Q(q) and learning function L(q)L(q) are simultaneously optimized by solving a convex optimization problem. The proposed method is applied to a non-minimal phase system and compared with a model-inversion based ILC design. Using the developed ILC design the underlying trade-off between tracking performance and convergence speed is thoroughly/quantitatively analyzed.