遗传优化同步电机分数阶PID预测函数励磁控制器

Fractional-order Prediction Function Excitation Controller Based on the Genetically Optimized Synchronous Motor

为提高同步电机励磁调节器的控制性能,提出一种基于遗传算法优化同步电机分数阶PID预测函数励磁控制器的设计方法。将同步电机的励磁系统进行线性化处理,在分数阶系统理论的基础上用预测函数控制推导出同步电机励磁调节器的分数阶传递函数模型,并根据所得到的系统性能综合指标评价函数再用遗传算法对分数阶PID预测函数控制器的参数进行优化整定。经仿真实验验证,与传统分数阶PID控制和预测函数控制相比,不仅减小了调节时间,增强了系统抗干扰能力,无稳态误差。并且在参数整定方面,遗传算法的收敛速度和寻优能力也明显优于模糊控制和粒子群算法。

To improve the control performance of the excitation regulator of the synchronous motor,this paper presents a method for designing the fractional-order PID prediction function excitation controller based on the genetically optimized synchronous motor. The excitation system of the synchronous motor is linealized. Based on the theory of the fractional-order system,a fractional-order transfer function model is derived through prediction function for the excitation regulator of the synchronous motor. Then,on the basis of the acquired comprehensive index evaluation function for the system performance,genetic algorithm is used to optimize the parameters of the fractional-order PID prediction function controller. Simulation results show that the setting time is reduced and the anti-interference capability is enhanced when compared with traditional fractional-order PID control and prediction function control,and no steady-state error occurs. Furthermore,in the aspect of parameter setting,the convergence rate and optimization ability of the genetic algorithm are obviously superior to those of fuzzy control and particle swarm algorithm.