Adaptive quantized DSC of output-constrained uncertain nonlinear systems with quantized input and input unmodeled dynamics

In this paper, an adaptive quantized fuzzy dynamic surface control (DSC) stratergy is investigated for a class of nonlinear systems with input unmodeled dynamics and output constraints. A challenge lies in that the input-quantized actuator is considered to possess both unknown control gain and nonlinear input unmodeled dynamics. By quantized controller design and DSC technique, combining normalized signal, integral Lyapunov functions, Nussbaum functions and the adaptive laws, the obstacle caused by quantization and multiple uncertainties is effectively overcome. The designed novel quantizer has the advantages of both the existing uniform quantizer and hysteresis quantizer, which can avoid the chattering and reduce the quantization error no matter the control volume is large or small. By defining a group of transformation based on a logarithmic one to one mapping, time-varying output constraints is satisfied. It is shown that all the signals are bounded, and the output signal is constrained within the preset range. (C) 2020 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.