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http://dx.doi.org/10.5916/jkosme.2013.37.7.743

Optimal iterative learning control with model uncertainty  

Le, Dang Khanh (Department of Marine Engineering, Mokpo Maritime University)
Nam, Taek-Kun (Division of Marine Engineering, Mokpo Maritime University)
Publication Information
Journal of Advanced Marine Engineering and Technology / v.37, no.7, 2013 , pp. 743-751 More about this Journal
Abstract
In this paper, an approach to deal with model uncertainty using norm-optimal iterative learning control (ILC) is mentioned. Model uncertainty generally degrades the convergence and performance of conventional learning algorithms. To deal with model uncertainty, a worst-case norm-optimal ILC is introduced. The problem is then reformulated as a convex minimization problem, which can be solved efficiently to generate the control signal. The paper also investigates the relationship between the proposed approach and conventional norm-optimal ILC; where it is found that the suggested design method is equivalent to conventional norm-optimal ILC with trial-varying parameters. Finally, simulation results of the presented technique are given.
Keywords
ILC; Optimal control; Model uncertainty; Convex minimization problem; Trial-varying parameters; worst-case norm-optimal ILC;
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