摘要
近空间用无刷直流电机(BLDCM)受环境参数影响出现不确定性参数摄动和负载扰动,系统的控制性能降低。为消除不确定性因素的影响,提出了一种基于RBF网络补偿的自适应模糊控制算法。该控制算法是在自适应模糊控制的基础上,引入RBF网络补偿控制器,对参数摄动和负载转矩突变引起的转速误差进行在线辨识和动态补偿,以达到快速鲁棒自适应控制目的。对比具有RBF网络补偿的自适应模糊控制和自适应模糊控制的模拟仿真实验结果表明:在转速变化、负载转矩突变和转动惯量改变条件下,有RBF网络补偿控制的响应时间缩短了10 ms以上,响应过程中,电磁转矩的瞬时峰值减少了20%左右,对近空间BLDCM系统的不确定性鲁棒性强。
Because of the environmental parameters transformation, the parameters perturbation and load torque disturbances of the brushless direct current motor （ BLDCM） in near space will appear, and the response speed and stability of control system will be bad. To solve this problem, we propose an adaptive fuzzy control algorithm based on RBF（ radial basis function） neural network compensation. The adaptive fuzzy controller is deduced to ensure the BLDCM system has good dynamic performance, the RBF neural network is adopted to do online identification and compensate for the speed error when the parameters perturbation and load torque disturbance appear in order to a-chieve the purposes of fast response speed and good robustness. Comparing the simulation results of adaptive fuzzy control with those of RBF neural network compensation and adaptive fuzzy control, we show preliminarily that：（1） the adaptive fuzzy control Based on RBF neural network has a strong robustness against the uncertainties of the BLDCM;（2） its response time is shorten by adaptive fuzzy control over 10ms;（3） its peak electromagnetic torque is decreased about 20% during the response process.
出处
《西北工业大学学报》
EI
CAS
CSCD
北大核心
2014年第3期394-399,共6页
Journal of Northwestern Polytechnical University
基金
国家自然科学基金面上项目(90716026)资助
