基于双种群混沌鲸鱼算法的自抗扰控制器参数整定

非线性自抗扰控制器具有多个耦合参数且依靠传统的经验整定法难以整定,以至于对控制器的精度和抗扰动能力造成一定影响。针对此问题,提出一种双种群混沌鲸鱼算法对控制器参数进行在线整定。针对传统鲸鱼算法易陷入局部最优以及在迭代后期出现寻优缓慢的现象,提出一种混沌策略和双种群协同机制对算法进行改进。以机械臂作为被控对象进行仿真验证,结果表明优化后的自抗扰控制器具有更好的控制精度和良好的抗扰动能力。改进后的鲸鱼优化算法可以用于非线性自抗扰控制器参数整定。

PARAMETER TUNING OF ADRC BASED ON DOUBLE POPULATION CHAOTIC WHALE ALGORITHM

The nonlinear ADRC has many coupling parameters and it is difficult to tune by traditional empirical tuning, so that the precision and anti-disturbance ability of the controller are affected. To solve this problem, we proposed a double population chaotic whale algorithm to tune the controller parameters online. Aiming at the phenomenon that traditional whale algorithm is easy to fall into local optimum and search slowly in the later iteration period, this paper proposed a chaotic strategy and a double population cooperative mechanism to improve the algorithm. The simulation results of the mechanical arm as the controlled object show that the optimized ADRC has better control precision and good anti-disturbance ability after adding disturbance. The improved whale optimization algorithm can be used to tune the parameters of the nonlinear ADRC controller.