Model Predictive Control Method Based on Data-Driven Approach for Permanent Magnet Synchronous Motor Control System

doi:10.1007/s12204-023-2600-4

Abstract

Abstract: Permanent magnet synchronous motor (PMSM) is widely used in alternating current servo systems as it provides high efficiency, high power density, and a wide speed regulation range. The servo system is placing higher demands on its control performance. The model predictive control (MPC) algorithm is emerging as a potential high-performance motor control algorithm due to its capability of handling multiple-input and multipleoutput variables and imposed constraints. For the MPC used in the PMSM control process, there is a nonlinear disturbance caused by the change of electromagnetic parameters or load disturbance that may lead to a mismatch between the nominal model and the controlled object, which causes the prediction error and thus affects the dynamic stability of the control system. This paper proposes a data-driven MPC strategy in which the historical data in an appropriate range are utilized to eliminate the impact of parameter mismatch and further improve the control performance. The stability of the proposed algorithm is proved as the simulation demonstrates the feasibility. Compared with the classical MPC strategy, the superiority of the algorithm has also been verified.

Key words: permanent magnet synchronous motor (PMSM), model predictive control (MPC), data-driven model predictive control (DDMPC)

摘要： 永磁同步电动机因其具有高效率、高功率密度和宽调速范围而广泛应用于交流伺服系统。伺服系统对控制性能提出了更高的要求。模型预测控制算法因具有处理多输入和多输出变量以及附加约束的能力，成为一种潜在的高性能电机控制算法。对于在永磁同步电机控制过程中应用模型预测控制算法，存在着由电机的电磁参数变化或负载扰动引起的非线性扰动，这将导致标称模型和受控对象之间存在失配问题，从而导致预测误差，影响控制系统的动态稳定性。本文提出了一种数据驱动的模型预测控制策略，利用适当范围内的历史数据以消除参数失配带来的影响，并进一步提高控制性能。仿真展示的可行性验证了该算法的稳定性，并与经典模型预测控制策略进行了比较，验证了算法的优越性。

关键词: 永磁同步电机，模型预测控制，数据驱动模型预测控制

CLC Number:

TM351

Li Songyang, Chen Wenbo, Wan Heng. Model Predictive Control Method Based on Data-Driven Approach for Permanent Magnet Synchronous Motor Control System[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(2): 270-279.

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