Abstract
An iterative learning control scheme is applied to a class of linear discrete-time switched systems with arbitrary switching rules. The application is based on the assumption that the switched system repetitively operates over a finite time interval. By taking advantage of the super vector approach, convergence is discussed when noise is free and robustness is analyzed when the controlled system is disturbed by bounded noise. The analytical results manifest that the iterative learning control algorithm is feasible and effective for the linear switched system. To support the theoretical analysis, numerical simulations are made.
Highlights
A switched system consists of a family of subsystems described by differential equations or difference equations, whose switching rules are usually considered to be arbitrary
The sufficient condition of convergence is given and significantly, the robustness of the algorithm is analyzed when the control switched system is interfered by bounded measurement noise
The super vector approach employed in the paper is a tool to theoretically analyze the learning performance of the addressed ILC algorithm
Summary
A switched system consists of a family of subsystems described by differential equations or difference equations, whose switching rules are usually considered to be arbitrary. In engineering practice, the dynamics of the plant model is usually unknown and some uncertain disturbance is unavoidable These adverse effects together with random switching rules increase challenge of designing an efficacious controller for a tracking performance of a switched system. Motivated by the drawbacks of the literatures [9, 11, 12], the paper discusses the convergence and the robustness of a Ptype ILC algorithm for a kind of discrete linear discrete timeinvariant switched systems with a fixed arbitrary switching rule. The sufficient condition of convergence is given and significantly, the robustness of the algorithm is analyzed when the control switched system is interfered by bounded measurement noise. In order to show the feasibility and effectiveness of the theoretical results, numerical simulations are given in Section 4 and the conclusion is drawn in the last section
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