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为减小磁浮列车气隙控制中非线性的影响,将粒子群优化(PSO)算法用于磁浮列车控制器参数优化,并在线性递减权重粒子群算法的基础上,提出了一种改进的粒子群优化算法.算法采用了邻域结构、停滞检测以及对全局最佳粒子的微扰,以改善算法的优化速度和收敛性.仿真和实验结果表明,将改进算法获得的优化参数用于磁浮列车的比例积分微分(PID)控制器,比原有PID控制器的输出超调减小45%.
In order to reduce the influence of nonlinearity on the air gap control of the maglev train, a particle swarm optimization (PSO) algorithm is used to optimize the parameters of the maglev train controller. Based on the linear descending weighted particle swarm optimization algorithm, an improved particle Swarm optimization algorithm.The algorithm uses neighborhood structure, stagnation detection and perturbation to the global best particle to improve the optimization speed and convergence of the algorithm.The simulation and experimental results show that the optimization parameters obtained by the improved algorithm are used in the maglev train Proportional Integral Derivative (PID) controller, than the original PID controller output overshoot reduced by 45%.