基于优化粒子数和分段权值的粒子群直线电机PID整定

以工程中直线电机伺服系统为研究对象,提出一种优化粒子数量加分段式惯性权重递减的粒子群PID控制器参数优化算法。优化粒子数量的方法可降低函数调用次数,通过对近两代的全局最优值进行比较,得到的误差值如果大于设定值,认为是在初始寻优阶段,保持粒子数量,否则在最终优化阶段,减少粒子数量,所减少的粒子特征是最接近最佳粒子的粒子,以保证在欧氏距离内实现粒子的分散性。最后再结合指数衰减曲线加线性递减曲线构成的分段式惯性权重递减策略提升算法的全局寻优和局部寻优能力。经数值验证分析,该优化算法在保证遍历性的同时,在一定程度上提高了算法的运行速度和寻优精度。实验仿真结果表明,该算法对PID控制器进行参数优化,直线电机系统响应速度快,超调量小,调节时间短。

PID Tuning of Particle Swarm Linear Motor Based on Optimized Particle Number and Piecewise Weight

Taking the linear motor servo system in engineering as the research object,an optimization algorithm of particle swarm optimization(PSO-PID)controller parameters is proposed,which optimizes the number of particles and decreases the inertia weight step by step.The method of optimizing the number of particles can reduce the number of function calls.By comparing the global optimum values of the last two generations,if the error value is greater than the set value,it is considered that in the initial optimization stage,the number of particles should be maintained.Otherwise,in the final optimization stage,the number of particles should be reduced,and the reduced particle characteristics are the closest to the optimum particle,so as to ensure the realization of particles within Euclidean distance.Finally,a piecewise inertia weight reduction strategy composed of exponential decay curve and linear decline curve is combined to enhance the global and local optimization ability of the algorithm.Through numerical validation and analysis,the algorithm not only guarantees ergodicity,but also improves the speed and precision of the algorithm to a certain extent.The simulation results show that the algorithm optimizes the parameters of the PID controller,and the linear motor system has fast response speed,small overshoot and short adjustment time.