Robust iterative learning preview control for LPV/LFR discrete-time systems

Abstract

This study proposes a novel observer-based iterative learning preview control (ILPC) scheme that employs a linear fractional representation (LFR) for discrete-time linear parameter-varying (LPV) systems. A tracking controller design involving a state observer and preview actions is adopted to enhance tracking performance. First, the state observer method and the Lyapunov function approach are employed to develop a state estimation methodology for the LPV/LFR system with external interferences. Next, an equivalent augmented model is established using the obtained state estimation information and the error system approach. This model includes future information on reference signals, thereby transforming the ILPC problem into a stabilization challenge. A differential-type (D-type) iterative learning control (ILC) scheme based on two-dimensional (2D) system theory is proposed to achieve tracking for the augmented model. The stability and convergence of the closed-loop system are analyzed. Finally, two examples are presented to demonstrate the effectiveness of the proposed control scheme.