A Novel Tracker for a Class of Sampled-data Nonlinear Systems

Abstract

In this paper, a novel observer/Kalman filter identification (OKID) based iterative learning control (ILC) for a class sample-data nonlinear system is proposed and supplies a good tracking performance in both the transient and steady-state phase. The proposed observer-based digital redesign tracker can suppress the uncertainties and the nonlinear perturbations. First, even without resetting the identical initial condition the optimal linear model of the analog nonlinear system is constructed at the operating point. The operating point is generated due to the analog observer updated by the well-designed analog OKID-ILC nonlinear system. Thereafter, the linear quadratic regulator design technique with a high-gain property is applied to design an analog observer-based tracker of the optimal linear model. Finally, the proposed approach for the analog system is then extended to the case for a class of sampled-data nonlinear systems.