无刷直流电机短反馈分数阶PID滑模的优化控制

Optimal control of brushless DC motor based on short feedback fractional PID sliding mode

针对无刷直流电机(BLDCM)的非线性、强耦合、多变量及参数时变等特性,导致其调速系统的控制性能欠佳的问题,提出了一种灰色预测短反馈分数阶PID滑模优化控制算法。引入内分泌激素调节中的超短反馈算法构成系统双层反馈机制,补偿自身的输出信号;系统反馈环节,引入GM(1,1)模型灰色预测,用灰色预测值取代测量值进行超前控制运算。针对分数阶PID型滑模趋近律参数,采用了一种改进蜣螂优化算法进行寻优,实现更加精确的控制。仿真模拟对比实验结果表明,经蜣螂算法优化的灰色预测短反馈分数阶PID滑模控制下的无刷直流电机调速控制系统,具有比常规PID控制更出色的响应速度、控制精度和鲁棒性等性能。

Brushless DC motor(BLDCM)has complex characteristics such as nonlinearity leading to poor control performance of their speed control systems.A gray predictive short feedback fractional order PID sliding mode optimal control algorithm is designed,in order to improve the deficiencies in the control system.The sliding mode control achieves intermittent control through the switching law,which has strong robustness.Fractional order theory can achieve more margin control,which can improve the flexibility of the control system and enhance the system control performance.At the same time,it overcomes the jitter problem due to sliding mode control.The ultrashort feedback algorithm in endocrine hormone regulation is introduced to constitute a two-layer feedback mechanism of the system to compensate its own output signal.In the system feedback session,the GM(1,1)model gray prediction is introduced.The gray predictions are used to replace the measured values for the override control operation.For the fractional order PID type sliding mode convergence law parameters,an improved dung beetle optimization algorithm is proposed for optimization search to achieve more accurate control in this paper.Comparative experiments through simulation show that the brushless DC motor speed control system under gray predictive short-feedback fractional-order PID sliding mode control optimized by the dung beetle algorithm has better performance in terms of response speed,control accuracy,and robustness than conventional PID control.