Support Vector Machine-based Fuzzy Self-learning Control for Induction Machines

摘要：

In this paper, because the induction machines are described as the plants of highly nonlinear and parameters time-varying, to obtain excellent control performances and the self-learning of fuzzy inference system (FIS), based on a support vector machine (SVM), a fuzzy self-learning control strategy for induction motors is presented based on the rotor field oriented motion model of induction machines. The fuzzy self-learning controller incorporated into the SVM-FIS, and a fast modified variable metric optimal learning algorithm (MDFP) and a support vector machine identifier (SVMI) for induction motors (IM) adjustable speed system are designed. Simulation results show that the proposed control strategy is of the feasibility, correctness and effectiveness.

关键词： induction machine (IM) motor dynamic model Juzzy inference system (FIS) support vector machine (SVM) modified variable metric optimal learning algorithm (MDFP)

作者: Zongkai Shao

作者单位: School of Hydropower & Information Engineering Huazhong University of Science and Technology Wuhan, China

会议类型: 国际会议

会议名称: 2010 International Conference on Computer and Communication Technologies in Agriculture Engineering(计算机与通信技术在农业工程国际会议 CCTAE 2010)

会议地点: 成都

会议语种:英文

页码: 12-16

在线出版日期: 2010-06-12（万方平台首次上网日期，不代表论文的发表时间）