Dynamic Gain-Driven Adaptive Quantized Output Feedback Control for Nonlinear Systems Governed by Parameter Criteria
针对通信资源受限的不确定非线性系统，提出一种动态增益自适应观测器与量化器协同设计的输出反馈控制方法，通过微分方程调节观测器增益并建立量化参数选择准则，实现全局渐近稳定，仿真验证了在机器人操作臂上的优越性。
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.