摘要
PID控制算法是目前应用最广泛的四旋翼控制算法。但目前传统工业控制中的PID参数整定设置方法难以给出合适的参数导致无人机控制精度低且鲁棒性差。采用粒子群优化算法(PSO)可对PID参数进行优化。针对基本PSO存在的收敛速度慢、收敛精度低、易过早收敛等缺陷,该文在传统粒子群算法基础上提出,非线性权重;非对称学习因子;柯西变异;改进PSO算法用于PID参数整定中。仿真结果表明,该文所提出的改进粒子群算法在PID参数整定中的效果优于传统PSO算法,且鲁棒性强。
PID control algorithm is the most widely used four-rotor control algorithm at present.However,the current PID parameter setting method in traditional industrial control is difficult to give appropriate parameters,resulting in low control accuracy and poor robustness of UAV.Particle swarm optimization(PSO)can be used to optimize PID parameters.In view of the shortcomings of basic PSO,such as slow convergence speed,low convergence accuracy and easy premature convergence,this paper proposes the following on the basis of traditional particle swarm optimization:nonlinear weight;asymmetric learning factor;cauchy variation;the improved PSO algorithm is used in PID parameter tuning,The simulation results show that the improved particle swarm optimization algorithm proposed in this paper is better than the traditional PSO algorithm in PID parameter tuning and has strong robustness.
出处
《科技创新与应用》
2023年第13期55-58,63,共5页
Technology Innovation and Application
关键词
PID
参数优化
粒子群优化算法
柯西变异
非线性权重
PID
parameter optimization
particle swarm optimization algorithm
cauchy variation
nonlinear weight
