Robust iterative learning control for linear systems with time-invariant parametric uncertainties

This paper presents a novel algorithm of the robust iterative learning control for linear systems subject to time-invariant parametric uncertainties. The design problem is formulated as a min-max problem with a quadratic performance criterion. Then, we derive an upper-bound of the worst-case performance. Applying Lagrange duality to the minimization problem leads to a dual problem which can be reformulated as an optimization problem over linear matrix inequalities. An algorithm is given afterward and its convergence properties are proved. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.