摘要 针对船舶运动具有时滞性特点,传统PID控制航向算法难以达到预期的控制精度,采用改进粒子群优化算法(Particle Swarm Optimization,PSO)对PID控制器参数优化。首先采用Nomoto模型作为船舶运动模型;其次将随机正弦调整策略与概率变异策略相结合对目标函数寻优,提高粒子群在解空间的搜索能力,避免其陷入局部最优解,加快算法收敛速度;最后将优化后的参数输入到控制系统中仿真船舶航向保持运动。仿真结果表明采用改进粒子群优化算法调整PID控制参数提高了船舶的航向控制性能,船舶航向调整时未出现超调,且控制系统可以控制船舶航向稳定在设定航向附近。 Aiming at the time-delay characteristics of ship motion,it is difficult for the conventional PID control heading algorithm to achieve the desired control precision.Improved Particle Swarm Optimization(PSO)is used to optimize the PID controller parameters.Firstly,the Nomoto model is adopted as the ship motion model;Secondly,the random sine adjustment strategy and the probability mutation strategy are combined to optimize the objective function,which improves the search ability of the particle swarm in the solution space,avoids it from falling into the local optimal solution,and accelerates the algorithm convergence speed;Finally,the optimized parameters are input into the control system to simulate the course of the ship to keep moving.The simulation results show that the adjustment of PID control parameters by the improved particle swarm optimization algorithm improves the ship's heading control performance.There is no overshoot when the ship's heading is adjusted,and the control system can control the ship's heading to stabilize near the set heading.
机构地区 浙江海洋大学东海科学技术学院 浙江海洋大学船舶与机电工程学院
出处 《浙江海洋大学学报:自然科学版》 CAS 北大核心 2020年第3期252-257,共6页 Journal of Zhejiang Ocean University:Natural Science
基金 国家自然科学基金(51809236) 浙江省自然科学基金(LY17E090002 LQ17E090003)。
关键词 随机正弦调整策略 概率变异策略 目标函数 航向控制 random sine adjustment strategy probability variation strategy objective function heading control
分类号 U675.91 [交通运输工程—船舶及航道工程]
