改进自适应粒子群算法的PID参数优化

PID parameter optimization based on improved adaptive particle swarm optimization algorithm

标准粒子群优化算法(PSO)易在局部求得最优解,为解决这一问题,采用自适应权重,在迭代中调整惯性,确保在每一次迭代中使用最佳权重,较好地解决局部最优的问题。该方式可改善传统PSO算法,与实践经验整定法、标准粒子群算法及一般自适应粒子群算法的寻优结果对比。结果显示:其超调量为3.2%,调节时间减少到0.165 s,与其他3种方法相比,振荡更低,且收敛速度更快。采用该方法调整,系统参数确保比例、积分、微分(PID)控制器的性能指标更好,以及控制系统更稳定。

The standard particle swarm optimization(PSO)algorithm often converges to local optima.To address this issue,an adaptive weighting scheme is proposed in this study.In each iteration,the inertia is adjusted to ensure the utilization of optimal weights,effectively tackling the problem of local optima.This approach enhances the traditional PSO algorithm.A comparative analysis is conducted against the results obtained from empirical tuning,the standard PSO algorithm,and a general adaptive PSO algorithm.The outcomes reveal an overshoot of 3.2%and a reduced settling time of 0.165 s.When compared to the other three methods,the proposed approach exhibits lower oscillations and faster convergence.Through the adoption of this approach,the adjustment of system parameters ensures improved performance metrics for the proportion,integration,and differentiation(PID)controller,as well as enhanced stability of the control system.