Robust Feedforward Control of Discrete Uncertain Systems Over a Finite Frequency Range

Abstract

In this paper, a novel robust iterative learning control strategy for linear discrete systems in a finite frequency domain is proposed. The strategy first converts the iterative learning control process into a discrete linear repeating system model by means of the equivalence transformation technique, and employs a convex bounded uncertainty domain to define the uncertainty range of the system. Subsequently, the control law design problem in discrete linear repetitive systems is solved by utilizing the generalized KYP lemma, which is transformed into the problem of solving linear matrix inequalities (LMIs). The designed control law is able to satisfy the robustness requirement in the direction of the iteration axis and ensure the monotonic convergence of the dynamic error between each experiment, so as to realize the expected control objective step by step. Finally, the advantages and practicality of the method are experimentally verified, and its potential limitations and constraints are analyzed and summarized.

Keywords

• Uncertainties
• robust convergence
• Kalman-Yakubovich-Popov
• linear discrete system