Adaptive learning control for a class of nonlinearly parameterized uncertain systems

Abstract

An adaptive learning control law is developed in this paper to attack a class of nonlinearly parameterized uncertain systems. Specifically, by assuming that the system uncertainty can be separated into an unknown parameter vector multiplying a periodic, yet unknown nonlinear function, we construct an adaptive learning controller to ensure global asymptotic tracking of the system state while compensate for the uncertainty associated with the system parameters and the unknown periodic functions simultaneously. Simulation results are included to demonstrate the efficacy of the proposed adaptive learning control law.