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Abstract This study proposes an innovative nonlinear model predictive control (NMPC) algorithm developed for obstacle avoidance in trajectory tracking of autonomous surface vessels (ASV). The proposed algorithm extends the prediction horizon to enhance situational awareness and enable the controller to calculate the best control actions. The novelty of the proposed algorithm is that it modifies the duration of the prediction by dynamically varying the prediction sampling time. The controller scans along the reference trajectory for potential obstacles and adjusts the prediction sampling time based on the vessel speed and the obstacle size. The simulation results show that the proposed algorithm improves the consistency of the execution time compared to the conventional NMPC and exhibits improved trajectory tracking with less speed variations.
Fernando et al. (Sun,) studied this question.