摘要
电机在运行中受负荷和环境参数的影响,使其速度不稳定。由于电机速度过程的变化是不确定的,采用参数化模型方法,难于精确测得系统的结构参数和模型参数,基于参数化模型的控制算法也不能实施。这里给出了一种针对电机调速过程的有效的控制算法。采用Tacagi-Sugeno分段模糊化再进行连接的方法,用局部的模糊线性模型集合来代替整体的非线性模型,完成了模糊模型辨识,并基于此给出了模糊自适应预测控制算法,使得电机的输出速度保持动态稳定。仿真结果表明,所给的控制方法对于不确定动态过程的控制是有效的。
The load change of motor affects its normal run. Because the speed course is uncertain, it is difficult that applied method of parameterize model to predict the structure parameters and model parameters of the ~system, the control algorithm based on parameterize model is not implemented. It presents an efficient method that the motor speed is controlled, it applies Tacagi-Sugeno's method that each linear sub-models is connected for fuzzy algorithm through whole nonlinear model is replaced with local linear models, an algorithm of the fuzzy model identifying and fuzzy adaptive control is fulfilled. It makes speed of motor maintain steady. Results of ~simulation show that it is efficient that uncertain dynamic processes are controlled.
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2004年第z1期829-832,共4页
Chinese Journal of Scientific Instrument
关键词
电机调速
不确定过程
模糊化拟合
模糊控制
自适应预测控制
Speed control of motor Uncertain course Fuzzy connecting of sub-models Fuzzy control ~Adaptive predictive control
