Neural Network Observer Based Adaptive Trajectory Tracking Control Strategy of Unmanned Surface Vehicle With Event-Triggered Mechanisms and Signal Quantization
Jun Ning, Yu Wang, C. L. Philip Chen, Tieshan Li
Abstract
This paper concerned with the network observer based adaptive trajectory tracking control strategy of Unmanned Surface Vehicle with event-triggered mechanisms and signal quantization. In expound upon input quantization, this paper introduces a linear analytical model enabling controller design without necessitating prior knowledge of the input quantization parameters. Meanwhile, the quantized state variables are estimated through the neural network-based observer. As a result, the quantized feedback controller is designed to use the observer's estimation results, through a combination of backstepping, dynamic surface techniques, and event-triggered mechanisms. The stability of the formulated closed-loop system is demonstrated through the application of Lyapunov stability theory principles. Ultimately, the effectiveness of the proposed control strategy is substantiated through simulation experiments.