Despite advances in modern control theory and artificial intelligence technology, current methods for tuning proportional-integral-derivative (PID) controller parameters based on the traditional particle swarm optimization (PSO) algorithm do not meet the requirements for controlling an unmanned surface vessel (USV) propulsion motor. To overcome the disadvantages of the PSO algorithm, such as low precision and easily falling into a local optimum, the beetle antennae search (BAS) algorithm can be introduced into the PSO algorithm by replacing particles with beetles, and effectively prevents the PSO algorithm from easily falling into the local optimum. At the same time, the BAS algorithm will no longer be limited to single objective parameterization. Herein, we propose a PID parameter optimization method based on the hybrid BAS-PSO algorithm for a USV propulsion motor. The PID parameter optimization of propulsion motor effectively becomes a beetle foraging problem with group optimization. Numerical results show that the method can effectively solve the problems of PSO and greatly improve convergence speed. Compared with the genetic algorithm and standard PSO algorithm, the BAS-PSO algorithm is superior for PID parameter tuning and can improve performance of USV propulsion system.

尽管现代控制理论和人工智能技术取得了进步，但目前基于传统粒子群优化 (PSO) 算法调整比例积分微分 (PID) 控制器参数的方法仍不能满足无人水面舰艇 (USV) 控制的要求推进马达。为克服PSO算法精度低、易陷入局部最优等缺点，可将甲虫天线搜索（Beetle antennae search，BAS）算法引入PSO算法，将粒子替换为甲虫，有效防止PSO算法容易陷入陷入局部最优。同时，BAS 算法将不再局限于单一目标参数化。在此处，我们提出了一种基于混合 BAS-PSO 算法的 USV 推进电机的 PID 参数优化方法。推进电机的PID参数优化有效地成为群优化的甲虫觅食问题。数值结果表明，该方法能有效解决粒子群优化问题，大大提高收敛速度。与遗传算法和标准 PSO 算法相比，BAS-PSO 算法在 PID 参数整定方面具有优势，可以提高 USV 推进系统的性能。