Adaptive quantized output feedback DSC of uncertain systems with output constraints and unmodeled dynamics based on reduced-order K-filters

Abstract

Abstract This paper is concerned with adaptive output feedback tracking control for a class of uncertain nonlinear systems with quantized input and unmodeled dynamics as well as output constraints. Quantized reduced-order K-filters are designed to observe parts of unmeasured states. The considered system is of two types of uncertainties. To deal with these uncertainties, we adopts global exponential stability technique of the state unmodeled dynamics with a Lyapunov description and the fuzzy approximation approach to those uncertain functions. To avoid the chattering of the quantized control signal, a hysteretic quantizer is introduced, and by designing a novel control law based on dynamic surface control (DSC) method, many assumptions of the quantized system in early literatures are removed. Combining quantized DSC with an one to one mapping from output error to the first dynamic surface, the robust quantized adaptive controller is constructed to guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB), and the tracking error is restricted within the prescribed output constraints. A numerical simulation example shows the effectiveness of the proposed approach.