Neural dynamics and sliding mode integration for the guidance of unmanned surface vehicles

Abstract

The purpose of this work is the development of a hybrid guidance and control system for the trajectory tracking task of an underactuated Unmanned Surface Vehicle (USV). The novelty of the guidance system is based on neural dynamics and sliding mode integration. The biological neural dynamics model is designed to smooth the outputs of the sliding mode control, avoid sharp speed jumps and satisfy the thruster control constraint. The outputs of the sliding mode controller are used such as the reference inputs of an internal backstepping control loop for low level dynamics regulation. The hybrid control is focused towards real robot exploitation in such a way to damp sudden changes of the torque commands and consequently, mechanical stress onto the thrust motors. The performance of the proposed control strategy and the comparison between sliding mode guidance, and sliding mode control embeds the neural dynamics model are described by a number of simulative tests. Moreover, measurement noises and a model of the sea currents variation, in direction and velocity, are counted in the simulated trials in order to demonstrate the control robustness with respect to measurement and environmental disturbances that unavoidably affect maritime operations.