An Adaptive Characteristic Model-Based Event-Triggered Sigmoid Prescribed Performance Control Approach for Tracking the Trajectory of Autonomous Underwater Vehicles

Abstract

This paper introduces an event-triggered sigmoid prescribed performance control method, enhanced by an adaptive characteristic model, for tracking the trajectory of autonomous underwater vehicles (AUVs). The AUV model is simplified into a function reliant solely on second-order parameter information through the use of characteristic modeling and a compression algorithm, which is then approximated by a neural network. We propose integrating prescribed performance control into event-triggered sliding mode control to accelerate convergence in AUV trajectory tracking. A novel prescribed performance function is employed in this integration, creating an event-triggered, non-singular terminal sliding mode control strategy. The stability of this controller is rigorously proven. This control strategy is not only robust against model uncertainties but also mitigates the jitter commonly associated with sliding mode control and the singularities from preset performance control due to sudden random disturbances. Comparative simulation experiments demonstrate that the proposed control method achieves superior control accuracy and a quicker response.