【摘要】 从模糊逻辑控制方式出发,借鉴PID控制的结构而形成的S面控制器.针对粒子群优化(PSO)算法存在早熟、易陷入局部极小等现象,对基本PSO算法进行了改进,引入变学习因子和惩戒因子,更好地协调全局和局部搜索能力,有利于快速找到全局最优点,提高了PSO算法在水下机器人S面运动控制器参数整定中的优化能力.通过水下机器人的运动控制仿真试验,验证了该方法的可行性和优越性.更多还原

【Abstract】 Based on the analysis of fuzzy control,combined with the form of PID control,the S Plane Control has been applied.Aiming at the defects inherited in particle swarm optimization(PSO) algorithm,such as precocity and tendency in local minima,the concept of dynamic learning factor and concept of chasten factor are introduced to improve the basic PSO algorithm.In this manner,the global and local search capability can be coordinated to make for locating the global optimum quickly.The application of the improved PSO algorithm proposed enhances the optimizing ability in tuning of S plane parameters in AUV’s motion control.The feasibility and advantages of this method has been demonstrated by simulation results.更多还原