Adaptive Prescribed-Time Event-Triggered Control of Nonlinear Networked Systems Under Dynamic Quantization

Abstract

This article addresses the issue of adaptive event-triggered and quantized control for a category of uncertain nonlinear systems, utilizing a prescribed-time (PT) control framework. We begin by introducing a dynamic event-triggering mechanism and a dynamic event-driven quantizer to develop a discrete control framework, without assuming the constraint of input-to-state stability (ISS). The aperiodic discrete control method can effectively improve the data transmission efficiency of the networked control system. Then, according to the adaptive parameter estimation, a novel PT event-triggered adaptive controller and a PT sampled and quantized adaptive controller are proposed. Compared with the backstepping control method, the designed "one-step-controller" decreases the computational loads of the virtual controllers. Moreover, the global PT stability of the nonlinear system is assured, and the Zeno phenomenon of the event-triggered sampling does not happen. Finally, the practicability and availability of the designed control method are validated via a numerical system and a manipulator system.