磁悬浮PID参数整定仿真

Simulation of Parameters Optimization of Maglev PID Controller

研究磁悬浮列车悬浮稳定安全优化控制问题。磁悬浮控制是磁浮列车的技术难点之一,由于系统的复杂性和列车运行环境的不确定性,涉及许多非线性因素,使控制系统稳定性和响应性差。为了得到合适的稳定悬浮间隙,提高控制质量,提出用卡尔曼滤波的方法,引入蚁群优化算法(ACA),对卡尔曼滤波方法进行改进,提出了蚁群算法和卡尔曼滤波的PID控制器参数组合优化方法。进行仿真,通过卡尔曼滤波器抑制干扰信号和测量信号。仿真结果表明改进的滤波方法效率高,控制效果得到了改善,为磁悬浮系统的优化设计提供了参考。

Optimal control problem of maglev train levitating stably and safely was researched. Maglev control is one of technical difficulties in maglev train, due to the complexity of the system and the uncertainty of train＇ s running environment. In order to obtain a suitable stable levitation gap and improve control quality, an ant colony optimization algorithm （ACA） was introduced on the basis of Kalman filter, to improve the Kalman filtering method. Parameters optimization of PID controller based on ACA and Kalman Filter was proposed. Simulation was carried out, and the Kalman filter was used to suppress interference signals and measuring signals. The simulation results show that the improved filtering method has high efficienc, the control effect has been improved and enhanced, and it provides a reference for the optimization of maglev system.