Dynamic Event-Triggered Adaptive Command Filter Control for Nonlinear Systems with Full State Constraints

Abstract

This paper proposes a unified adaptive control strategy for strict-feedback nonlinear systems that have full state constraints. The strategy combines command filtering with a dynamic event-triggered framework. It handles two key problems in modern control systems: the high computational cost of conventional backstepping and the limited communication bandwidth in networked implementations. To avoid the well-known complexity explosion, command filters generate bounded approximations of virtual control signals, so repeated analytical differentiation is not required. At the same time, barrier Lyapunov functions are incorporated to make sure that all prescribed state constraints are strictly respected throughout the operation. In addition, a new dynamic event-triggering rule is designed to significantly reduce control signal transmissions, which strikes a reasonable balance between communication efficiency and control performance. This rule strictly excludes Zeno behavior and ensures that the tracking error is uniformly ultimately bounded. A complete theoretical analysis establishes that all closed-loop signals remain uniformly ultimately bounded, all state constraints are strictly satisfied, and the output tracking error converges to a bounded compact set. Simulation studies performed on a representative nonlinear system confirm the effectiveness and practical benefits of the proposed approach, showing accurate trajectory tracking and constraint adherence.