Dynamic Gain-Driven Adaptive Quantized Output Feedback Control for Nonlinear Systems Governed by Parameter Criteria.

Abstract

This article focuses on stabilizing uncertain nonlinear systems with limited communication resources. Traditional approaches relying on static quantizers or fixed-gain observers face significant limitations. To solve this, an adaptive observer-based quantized output feedback control framework is proposed. A dynamic-gain state observer is developed, with observer gains adjusted by a differential equation to handle nonlinearities and quantization effects. A criterion for choosing quantization parameters is established, linking them to control gains, observer dynamics, and bounded uncertainties. This confines quantization errors and ensures global asymptotic stability of the closed-loop system. Simulations on a robotic manipulator system validate the superiority of the proposed method. The work integrates dynamic observer adaptation and quantizer design, promoting resource-efficient control in bandwidth and resource-constrained applications.