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

Lihui Wang,Jie Meng,Guojun Tan

doi:10.3390/en12244803

Lihui Wang, Jie Meng + Show 1 more

Open Access

https://doi.org/10.3390/en12244803

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Journal: Energies	Publication Date: Dec 17, 2019
Citations: 16	License type: CC BY 4.0

Affiliation: China University of Mining and Technology

Abstract

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.

Highlights

interior permanent magnet synchronous motor (IPMSM) has been extensively utilized in the fields of electromechanical drives, servo systems, and automotive industry due to its strong reliability, high efficiency, high power density, and a large torque-ampere ratio [1,2]
In order to achieve excellent performance of both dynamic and steady-state current control, many researches focused on the control algorithms of IPMSM, and the most common control algorithms are hysteresis control, proportional-integral (PI) control [3], model predictive control (MPC) [4,5], and nonlinear control [6]
Ψf0 ), which arepractical utilized experiment, for experiments the true parameters of IPMSM (Ld0, Lq0, and Ψf0 ), which are utilized for experiments under parameter mismatch conditions

Summary

Introduction

IPMSM has been extensively utilized in the fields of electromechanical drives, servo systems, and automotive industry due to its strong reliability, high efficiency, high power density, and a large torque-ampere ratio [1,2]. In order to achieve excellent performance of both dynamic and steady-state current control, many researches focused on the control algorithms of IPMSM, and the most common control algorithms are hysteresis control, proportional-integral (PI) control [3], MPC [4,5], and nonlinear control [6]. Among all these control algorithms, hysteresis control has many advantages such as quick current responses, good robustness, and simple algorithm implementation. For PI-based current double closed-loop IPMSM control system, the method of setting PI parameters is too complicated [7,8]

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Conclusion