基于粒子群优化的自适应模糊制冷控制算法

Particle Swarm Optimized Adaptive Fuzzy Cryocooler Control Algorithm

摘要:

为解决空间斯特林制冷机和探测器热负载不确定及存在变化的问题,提出了自适应模糊PID制冷控制。在空间环境中使用的斯特林制冷机参数会随着时间的变化而发生改变,探测器负载也会随着工作模式和工作时间的变化而变化,整个制冷系统涉及的变量多,参数非线性。采用传统的控制方法,在固定的单一条件、环境下得到的控制参数,环境和负载发生变化后容易性能变差甚至不稳定,控制精度和稳定性不能满足使用要求。设计了一种自适应斯特林制冷机控制器,通过综合自适应模糊PID控制的方法,采用粒子群优化算法调整控制参数以减小代价函数。通过仿真和试验验证算法的有效性和鲁棒性。

Abstract:

To solve the control problem of stirling cryocooler for space using and detector load in the present of parameter uncertainties and changes, we design an adaptive fuzzy PID method for cryocooler control. Space using stirling cryocooler parameter is changing with time and different environment, detector load is changing with working mode and working hours, the whole cooling system is a multi-variable, nonlinear control system. Traditional control method is designed in certain condition cannot fulfill the accuracy and stability with different environment and load changing. This paper puts forward an adaptive fuzzy PID controller according to the different cold quantity and types of need of refrigeration of the detector load, control parameters is realized by using fuzzy control of self-adjustment. Particle swarm optimization (PSO) algorithm is adopted to determine the optimal parameters for the controller by minimizing the objective function. Finally, the simulation and test results are presented to proof the effectiveness and robustness of the proposed controller.