Improved Model Predictive Control of Permanent Magnet Synchronous Motor

被引：0
|
作者：
Wu, Xuan
Wang, Hui
Huang, Shoudao
Feng, Yaojing
机构：
来源：
2014 17TH INTERNATIONAL CONFERENCE ON ELECTRICAL MACHINES AND SYSTEMS (ICEMS) | 2014年
关键词：
Permanent Magnet SynchronousMotor((PMSM); Recursive Model Predictive Control (RMPC); Recursive Levenberg Marquardt Algorithm(RLMA); Iterative Learning Control (ILC); NONLINEAR DISTURBANCE OBSERVER; SPEED CONTROL; NEURAL-NETWORK; DESIGN; DRIVE; IMPLEMENTATION;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This paper depicts realization of a rapid current control system of permanent magnet synchronous motor(PMSM) based on Recursive model predictive control (RMPC),In traditional MPC can only be adopted in slow dynamics,its sample time is measured in seconds or minutes. It has to be calculated online, and its iterative computational program requires long computational time. To shorten computational time,a recursive method based on Recursive Levenberg Marquardt Algorithm (RLMA) and Iterative Learning Control (ILC) is proposed to resolve the optimization problem in MPC.Then, Simulation and TMS320F2-8335DSP experimental results show the effectiveness of RMPC compared with traditional MPC.
引用
收藏
页码：598 / 604
页数：7
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