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LMI-based robust iterative learning controller design for discrete linear uncertain systems. (English) Zbl 1122.93346
Summary: 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 γ-suboptimal H 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 results demonstrate the effectiveness of the proposed method.
93B51Design techniques in systems theory
68T05Learning and adaptive systems
93C55Discrete-time control systems
93D09Robust stability of control systems
93C41Control problems with incomplete information