基于多种群遗传算法的水面无人艇航迹控制方法

针对水面无人艇(USV)的航迹控制问题,提出了一种由视线导向法和多种群遗传算法整定的PID航向控制器组成的航迹跟踪控制方法.该方法采用多种群遗传算法克服了传统遗传算法容易陷入局部最优的问题,增强了算法的全局寻优能力;并根据模型特点改进了适应度函数,使得对控制器性能的评价更加合理.与标准遗传算法和粒子群算法的对比仿真表明,多种群遗传算法在PID参数整定方面寻优能力更强、稳定性更高;同时,整定出的PID控制器针对不同的模型参数,均表现出收敛速度快、无超调、无稳态误差的优良特性.航迹仿真结果表明,设计的航迹控制方法能够有效跟踪给定航迹.

Path Tracking Control Method of Unmanned Surface Vehicle Based on Multi-Population Genetic Algorithm

To solve the path following problem of unmanned surface vehicle(USV),a method consisting of lineof-sight guidance and multi-population genetic algorithm tuning PID heading controller was proposed.By using multi-population genetic algorithm,this method overcame the problem that the traditional genetic algorithm was easy to fall into local optimal solution,and enhanced the global optimization ability of the algorithm.The fitness function was improved according to the model characteristics,which made the evaluation of the controller performance more reasonable.Simulation results showed that compared with the standard genetic algorithm and particle swarm optimization algorithm,the multi-population genetic algorithm has better optimization ability and stability in PID parameter tuning.At the same time,the adjusted PID controllers showed good characteristics of fast convergence speed,no overshoot and no steady-state error for different model parameters.Track simulation results show that the designed control method can follow the given path effectively.