A Novel Dynamic Periodic Event-Triggered Prescribed Performance Control of Uncertain Semi-Strict Feedback Systems with Application

In this paper, a novel dynamic periodic event-triggered adaptive global prescribed performance control algorithm is proposed for a class of uncertain semi-strict feedback nonlinear systems. The proposed dynamic periodic event-triggering mechanism incorporates both signal amplitude and rate of change as triggering criteria, which effectively addressing two critical challenges: it eliminates the need for continuous monitoring of event-triggering conditions while preventing the Zeno phenomenon, and achieves minimal triggering frequency without compromising closed-loop system performance. Through the integration of backstepping methodology with time-varying scaling transformation and nonlinear transformation techniques, an event-based adaptive global prescribed performance controller is designed, which ensures that all closed-loop system signals are bounded and the tracking error remains within predefined performance bounds, regardless of initial conditions. Finally, the effectiveness of the control algorithm is validated through a numerical simulation of the networked spacecraft simulator system.