Improvement of Bacterial Foraging Algorithm for Heading Control of Unmanned Surface Vessel

To address the problem of heading control for Unmanned Surface Vessel (USV) in autonomous navigation, this paper proposes an optimization algorithm based on an Improved Bacterial Foraging Algorithm(IBFA), which can optimize the heading control parameters of USV and improve the control performance. The original BFA has fixed search step size, many useless swimming steps, and rigid migration operation, which leads to long computation time and easy to fall into local optimum. To address these issues, this study designed an improved multi-dimensional adaptive step length strategy to adjust the algorithm step length automatically. Additionally, prior exploration techniques were utilized to optimize bacterial random swimming steps, enhancing the algorithm efficiency. An adaptive migration probability was used to make the algorithm’s dispersion step more in line with the optimization process. Finally, the improved algorithm was applied to the PID parameter optimization problem in the autonomous heading control of USV, and the effectiveness of the algorithm’s PID parameter optimization was verified through semi-physical simulation experiments.