粒子群算法自寻优模糊PID控制器设计

Design of Self-optimizing Fuzzy-PID Controller with Particle Swarm Algorithm

针对常规模糊PID控制器的控制规则和控制参数固定不变而降低了系统自适应能力的问题,提出了一种基于粒子群算法寻优的方法对模糊控制器进行寻优。通过改变模糊控制器的3个尺度系数(K_e、K_(ec)、K_u),可以改变不同阶段系统的误差以及误差变化率所对应的权重。以ITAE指标作为粒子群寻优的目标函数,可以保证系统的快速响应性、超调量、调节时间以及稳态误差等。通过在Matlab下建立交流永磁同步电机(PMSM)模型,对其仿真分析表明:粒子群算法自寻优模糊PID控制器有着更优越的控制性能。

Because of the fixed control parameters and control rules of the conventional fuzzy PID controller,the capacity of the self-adaptation is reduced. This paper proposes a new self-optimizing method,based on the particle swarm algorithm to optimize the fuzzy controller. By changing the three scale coefficients of the fuzzy controller（ K_e、K_（ec）、K_u）,the different stages of the system error and the error rate of the corresponding weight can be changed. The ITAE is used as the optimizing objective of particle swarm optimization,thus ensuring the system＇s quick response,overshoot,adjust able time and steady state error,etc. Through the model of the permanent magnet synchronous motor（ PMSM） eatablished in Matlab,its simulation analysis is done. The results show that the self-optimizing fuzzy PID controller with the particle swarm algorithm has better control performance.