Multimode trajectory tracking control of Unmanned Surface Vehicles based on LSTM assisted Model Predictive Control

Abstract

Modern intelligent ships are evolving into highly collaborative, complex, and tightly integrated systems. Diverse operation modes emerge as different mission requirements, which lead to increased demands for precision and reliability in control. In this paper, a Long Short-Term Memory (LSTM) assisted Model Predictive Control (MPC) method is proposed to fit each operating condition of trajectory tracking for Unmanned Surface Vehicles (USVs). Firstly, the LSTM network is utilized to predict the behaviors of the controlled system, inspired by the robust representational capabilities of deep learning. Based on this mechanism, the different operation modes of USVs could be automatically matched without the switching strategy or building multiple models for each mode. Then, additional collision avoidance constraints are added to the MPC to enhance safety measures and address the optimization problem. Finally, a case study including three different trajectory tracking scenarios is conducted to validate the proposed method. The simulation results show that the model is proven to have the high control accuracy and effective collision avoidance response within the confines of obstacles under the disturbed environment. The model could be used to provide invaluable guidance to decision making such as the remote control of human-computer interaction and maritime rescue.