Research on the Recursive Model Predictive Control Algorithm of PMSM

Author

Xuan Wu ; Hui Wang ; Sheng Huang ; Shoudao Huang

fYear

2014

fDate

Aug. 31 2014-Sept. 3 2014

Firstpage

1

Lastpage

6

Abstract

In order to optimize the current-control performance of the permanent-magnet synchronous motor system(PMSM) with different disturbances and nonlinearity, a improved current control algorithm for the PMSM systems using Recursive Model Model Predictive Control (RMPC) is developed in this paper. Because of the conventional MPC has to be computed online, and its iterative computational procedure need long calculated time. To enhanced computational speed, a recursive method based on Recursive Levenberg Marquardt Algorithm (RLMA) and Iterative Learning Control (ILC) is introduced to solve the optimization issue in MPC. Fianl, the effectiveness of the proposed algorithms have been verified by Simulation and TMS320F28335DSP experimental results.

Keywords

electric current control; iterative methods; learning systems; machine control; permanent magnet motors; predictive control; synchronous motors; ILC; PMSM systems; RLMA; RMPC; TMS320F28335DSP experimental results; current control algorithm; current-control performance; iterative computational procedure; iterative learning control; permanent-magnet synchronous motor system; recursive Levenberg Marquardt algorithm; recursive model predictive control algorithm; Control systems; Heuristic algorithms; Mathematical model; Optimization; Prediction algorithms; Predictive control; Predictive models; Iterative Learning Control (ILC); Permanent Magnet SynchronousMotor((PMSM); Recursive Levenberg Marquardt Algorithm(RLMA); Recursive Model Predictive Control (RMPC);

fLanguage

English

Publisher

ieee

Conference_Titel

Transportation Electrification Asia-Pacific (ITEC Asia-Pacific), 2014 IEEE Conference and Expo

Conference_Location

Beijing

Print_ISBN

978-1-4799-4240-4

Type

conf

DOI

10.1109/ITEC-AP.2014.6941177

Filename

6941177