ONLINE SPEED CONTROL OF PERMANENT-MAGNET SYNCHRONOUS MOTOR USING SELF-CONSTRUCTING RECURRENT FUZZY NEURAL NETWORK

摘要：

In this paper, a self-constructing recurrent fuzzy neural network (SCRFNN) method is proposed to control the speed of a permanent-magnet synchronous motor to track periodic reference trajectories. The proposed SCRKNN combines the merits of self-constructing fuzzy neural network (SCKNN) and the recurrent neural network (KNN). The structure learning is based on the partition of input space, and the parameter learning is based on the supervised gradient-decent method. In addition, the .Ylahalanobis distance (M-distance) formula is employed that the neural network has the ability of identification of the neurons will be generated or not. Finally, the simulated results show that the control effort is robust.

关键词： Fuzzy neural network Recurrent neural network Self-constructing Mahalanobis distance Permanent-magnet synchronous motor

作者: HUNG-CHING LU MING-HUNG CHANG

作者单位: Department of Electrical Engineering, Tatung University, Taipei, Taiwan

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 3857-3862

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）