LMI-based robust iterative learning controller design for discrete linear uncertain systems

This paper addresses the design problem of robust iterative learning controllers for a class of linear discrete-time systems with norm-bounded parameter uncertainties. An iterative learning algorithm with current cycle feedback is proposed to achieve both robust convergence and robust stability. The synthesis problem of the proposed iterative learning control (ILC) system is reformulated as a y-suboptimal H-infinity control problem via the linear fractional transformation (LFT) .A sufficient condition for the convergence of the ILC algorithm is presented in terms of linear matrix inequalities (LMIs) . Furthermore, the linear transfer operators of the ILC algorithm with high convergence speed are obtained by using existing convex optimization techniques. The simulation results demonstrate the effectiveness of the proposed method.