Deep Learning-Based Trajectory Tracking Control forUnmanned Surface Vehicle

Trajectory tracking control based on waypoint behavior is a promising way for unmanned surface vehicle (USV) to achieve autonomous navigation. This study is aimed at the guidance progress in the kinematics; the artificial intelligence method of deep learning is adopted to improve the trajectory tracking level of USV. First, two deep neural network (DNN) models are constructed to evaluate navigation effects and to estimate guidance law parameters in real time, respectively. We then pretrain the DNN using a Gaussian–Bernoulli restricted Boltzmann machine to further improve the accuracy of predicting navigation effect. Finally, two DNNs are connected in parallel with the control loop of USV to provide predictive supervision and auxiliary decision making for traditional control methods. This kind of parallel way conforms to the ship manipulation of habit. Furthermore, we develop a new application on the basis of Mission Oriented Operating Suite Interval Programming named “pDeepLearning.” It can predict the navigation effect online by DNN and adjust the guidance law parameters according to the effect level. The experimental results show that, compared with the original waypoint behavior of USV, the prediction model proposed in this study reduces the trajectory tracking error by 19.0% and increases the waypoint behavior effect level.