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In this paper we present an approach to deal with model uncertainty in norm-optimal iterative learning control (ILC). Model uncertainty generally degrades the convergence and performance of conventional learning algorithms. To deal with model uncertainty, a robust 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 our design method is equivalent to conventional norm-optimal ILC with trial-varying learning gains. Finally, simulation results of the presented technique are given.