Comparison of Artificial Neural Network and Least Squares Prediction Models for Finite-Control-Set Model Predictive Control of a Permanent Magnet Synchronous Motor

2021 ◽  
Author(s):  
S. Hanke ◽  
O. Wallscheid ◽  
J. Böcker
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Model Predictive Control ◽  
Least Squares ◽  
Permanent Magnet ◽  
Predictive Control ◽  
Prediction Models ◽  
Permanent Magnet Synchronous Motor ◽  
Finite Control ◽  
Control Set

scholarly journals Direct predictive speed control of permanent magnet synchronous motor fed by matrix converter

2020 ◽  
Vol 11 (4) ◽  
pp. 2183
Author(s):  
Najmeh Movahhed Neya ◽  
Sajad Saberi ◽  
Babak Mozafari
Keyword(s):  
Permanent Magnet ◽  
Predictive Control ◽  
Control Algorithm ◽  
Permanent Magnet Synchronous Motor ◽  
Speed Control ◽  
Synchronous Motor ◽  
Matrix Converter ◽  
Mechanical Subsystem ◽  
Finite Control ◽  
Control Set

This paper proposes a non-cascade -single loop- Direct Speed Control algorithm for surface mounted Permanent Magnet Synchronous Motor (PMSM) fed by Matrix Converter. The proposed method uses Finite Control Set Model Predictive Control (FCS-MPC) to manipulate system speed and currents simultaneously. Also, for better performance of the predictive method, an observer designed to estimate mechanical torque and other uncertain parameters of the mechanical subsystem as a lumped disturbance. Simulation results using Matlab/Simulink demonstrate the performance of proposed algorithm.

scholarly journals Research on Model Predictive Control of IPMSM Based on Adaline Neural Network Parameter Identification

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4803 ◽  
Author(s):  
Lihui Wang ◽  
Guojun Tan ◽  
Jie Meng
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Parameter Identification ◽  
Permanent Magnet ◽  
Predictive Control ◽  
Control Method ◽  
Permanent Magnet Synchronous Motor ◽  
Network Parameter ◽  
Maximum Torque Per Ampere ◽  
Adaline Neural Network

This paper reports the optimal control problem on the interior permanent magnet synchronous motor (IPMSM) systems. The control performance of the traditional model predictive control (MPC) controller is ruined due to the parameter uncertainty and mismatching. In order to solve the problem that the MPC algorithm has a large dependence on system parameters, a method which integrates MPC control method and parameter identification for IPMSM is proposed. In this method, the d-q axis inductances and rotor permanent magnet flux of IPMSM motor are identified by the Adaline neural network algorithm, and then, the identification results are applied to the predictive controller and maximum torque per ampere (MTPA) module. The experimental results show that the optimized MPC control proposed in this paper has a good steady state and robust performance.

Sign in / Sign up

Export Citation Format

Share Document