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

Lihui Wang, Guojun Tan and Jie Meng
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Lihui Wang: School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China
Guojun Tan: School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China
Jie Meng: School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China

Energies, 2019, vol. 12, issue 24, 1-16

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.

Keywords: interior permanent magnet synchronous motor (IPMSM); optimal control; model predictive control (MPC); maximum torque per ampere (MTPA); parameter mismatch; parameter identification; Adaline neural network; control performance (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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