LVS guidance principle and adaptive neural fault-tolerant formation control for underactuated vehicles with the event-triggered input

Abstract

This paper presents a novel adaptive neural event-triggered formation control strategy for underactuated surface vehicles (USVs) in the presence of marine practical constraints, i.e. the waypoints-based planned path and actuator failures. The proposed strategy is composed of the guidance model and the control one. For the guidance part, the logic virtual ship (LVS) guidance principle is developed to implement the reference path programming for the formation pattern. For merit of the adaptive virtual ship, the smooth reference can be produced for followers without using the velocity information of leader. Furthermore, the proposed adaptive neural fault-tolerant control algorithm is to guarantee that the followers can maintain their pattern and converge to the corresponding reference path. In the algorithm, the neural networks (NNs)-based observer is designed to estimate the unmeasurable velocities of followers online. With the input event-triggered mechanism, two adaptive parameters are derived to compensate for the gain uncertainty and actuator failure. And that could effectively reduce the communication burden for the channel from controller to actuator. The derived closed-loop system has been proved to be with the semi-global uniform ultimate bounded (SGUUB) stability. Finally, the superiority of the proposed strategy can be verified by the simulation experiments.