Differential Evolution Based Nonlinear Model Predictive Speed Control of PMSM Implemented on GPU

Abstract

In this paper, the novel approach to the nonlinear model predictive speed control of a permanent magnet synchronous motor and its implementation is introduced. The implementation is performed using general-purpose computing on graphics processing unit. The introduced algorithm uses the optimization method based on the differential evolution to get the optimal increment of stator voltage. The proposed algorithm is tested in the processor in the loop simulation with the Simscape model for the simulation of PMSM and the Jetson Xavier embedded device for the algorithm execution. The results show the ability of the algorithm to ensure the reference tracking and to keep the requested variables within their limits.

In this paper, the novel approach to the nonlinear model predictive speed control of a permanent magnet synchronous motor and its implementation is introduced. The implementation is performed using general-purpose computing on graphics processing unit. The introduced algorithm uses the optimization method based on the differential evolution to get the optimal increment of stator voltage. The proposed algorithm is tested in the processor in the loop simulation with the Simscape model for the simulation of PMSM and the Jetson Xavier embedded device for the algorithm execution. The results show the ability of the algorithm to ensure the reference tracking and to keep the requested variables within their limits.