Two-Step Event-Triggered Data Driven Model Predictive Control for Trajectory Tracking of Unmanned Surface Vessel Under Environmental Disturbances

Data driven model predictive control (DMPC) has become an effective trajectory tracking approach for unmanned surface vessel (USV). However, its offline-trained prediction models and event-triggered (ET) mechanisms result in reduced control accuracy and efficiency under time-varying ocean disturbances. To effectively improve the control accuracy and efficiency of the DMPC method under environmental influences, a two-step ET DMPC (TETDMPC) algorithm is proposed. Traditional mechanism prediction models fail to effectively account for time-varying USV dynamics, which can degrade the accuracy of control algorithms. To enhance the control accuracy of the algorithm, a v-support vector regression (vSVR)-based data driven model is introduced into the model predictive control algorithm to replace mechanism prediction model. However, the rolling horizon calculations of the DMPC and the online updates of the vSVR suffer from extensive computation. To improve the control efficiency and enable vSVR model online updates, a two-step ET mechanism is designed. The performance of the online vSVR model depends on the quality of the training data. Therefore, a spatiotemporal data updating algorithm is also developed to construct the model training dataset online. The trajectory tracking performance of the proposed TETDMPC algorithm is validated based on the CyberShip II and Dolphin 1 USVs, respectively. The results demonstrate that the TETDMPC algorithm has desirable control performance. Note to Practitioners—Our research has achieved a breakthrough. A two-step event-triggered data driven model predictive control (TETDMPC) algorithm has been specifically designed to improve the control efficiency, accuracy and reliability of the unmanned surface vehicle (USV) systems under environmental disturbances. This innovation is of great significance, especially for the trajectory tracking of USVs under environmental disturbances. Since traditional model predictive control (MPC) has difficulty taking into account the decline in control performance caused by inaccurate models, and data driven MPC (DMPC) methods are unable to maintain good control efficiency under environmental disturbances, the need for effectively saving computational resources and maintaining good control accuracy has emerged for DMPC. TETDMPC has demonstrated its ability to solve these problems in this paper, making it a powerful tool for improving control performance. However, TETDMPC does not consider how to perform effective obstacle avoidance operations. In future research, we will address the integration issue of the TETDMPC and obstacle avoidance algorithms.