采用神经模糊控制器对无刷直流电机(Brushless Direct Current Motor,简称BLDCM)的转速进行控制。其控制规则用一对输入/输出数字信号来表示,用BP算法训练网络,经过训练的网络就相当于一个模糊关系存储器,执行模糊推理的功能。将这种神...采用神经模糊控制器对无刷直流电机(Brushless Direct Current Motor,简称BLDCM)的转速进行控制。其控制规则用一对输入/输出数字信号来表示,用BP算法训练网络,经过训练的网络就相当于一个模糊关系存储器,执行模糊推理的功能。将这种神经模糊控制器用于BDCM的转速控制,其运行效果良好。展开更多
Adaptive Type-2 fuzzy control possesses control performance better than the traditional adaptive fuzzy control.However,heavy computation burden obviously blocks the utilization of adaptive Type-2 fuzzy control in indu...Adaptive Type-2 fuzzy control possesses control performance better than the traditional adaptive fuzzy control.However,heavy computation burden obviously blocks the utilization of adaptive Type-2 fuzzy control in industrial application.By adopting novel piecewise fuzzy sets and center-average type-reduction,a simplified adaptive interval Type-2 fuzzy controller involving less computation is developed for practical industrial application.In the proposed controller,the inputs are divided into several subintervals and then two piecewise fuzzy sets are used for each subinterval.With the manner of piecewise fuzzy sets and a novel fuzzy rules inference engine,only part of fuzzy rules are simultaneously activated in one control loop,which exponentially decreases the computation and makes the controller appropriate in industrial application.The simulation and experimental study,involving the popular magnetic levitation platform,shows the predicted system with theoretical stability and good tracking performance.The analysis indicates that there is far less computation of the proposed controller than the traditional adaptive interval Type-2 fuzzy controller,especially when the number of fuzzy rules and fuzzy sets is large,and the controller still maintains good control performance as the traditional one.展开更多
文摘采用神经模糊控制器对无刷直流电机(Brushless Direct Current Motor,简称BLDCM)的转速进行控制。其控制规则用一对输入/输出数字信号来表示,用BP算法训练网络,经过训练的网络就相当于一个模糊关系存储器,执行模糊推理的功能。将这种神经模糊控制器用于BDCM的转速控制,其运行效果良好。
基金Project(51005253) supported by the National Natural Science Foundation of ChinaProject(2012ZX02702006-003) supported by the National Science and Technology Major Program of ChinaProject(JMTZ201101) supported by the Key Laboratory for Precision & Non-traditional Machining of Ministry of Education,Dalian University of Technology,China
文摘Adaptive Type-2 fuzzy control possesses control performance better than the traditional adaptive fuzzy control.However,heavy computation burden obviously blocks the utilization of adaptive Type-2 fuzzy control in industrial application.By adopting novel piecewise fuzzy sets and center-average type-reduction,a simplified adaptive interval Type-2 fuzzy controller involving less computation is developed for practical industrial application.In the proposed controller,the inputs are divided into several subintervals and then two piecewise fuzzy sets are used for each subinterval.With the manner of piecewise fuzzy sets and a novel fuzzy rules inference engine,only part of fuzzy rules are simultaneously activated in one control loop,which exponentially decreases the computation and makes the controller appropriate in industrial application.The simulation and experimental study,involving the popular magnetic levitation platform,shows the predicted system with theoretical stability and good tracking performance.The analysis indicates that there is far less computation of the proposed controller than the traditional adaptive interval Type-2 fuzzy controller,especially when the number of fuzzy rules and fuzzy sets is large,and the controller still maintains good control performance as the traditional one.
