Title :
Research on the Recursive Model Predictive Control Algorithm of PMSM
Author :
Xuan Wu ; Hui Wang ; Sheng Huang ; Shoudao Huang
fDate :
Aug. 31 2014-Sept. 3 2014
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);
Conference_Titel :
Transportation Electrification Asia-Pacific (ITEC Asia-Pacific), 2014 IEEE Conference and Expo
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-4240-4
DOI :
10.1109/ITEC-AP.2014.6941177
