Synergizing Metaheuristic Optimization and Model Predictive Control: A Comprehensive Review for Advanced Motor Drives

Qicuan Wang, Hai Shi, Chen Ye and Huawei Zhou ()
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Qicuan Wang: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Hai Shi: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Chen Ye: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Huawei Zhou: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China

Energies, 2025, vol. 18, issue 18, 1-35

Abstract: Model predictive control (MPC) is a prominent research focus in motor drives, offering advantages in dynamic response, steady-state accuracy, robustness, and multi-objective handling. However, increasing performance demands in modern systems, coupled with power-electronic device constraints (switching frequency, saturation), impose stringent requirements: high torque response, minimal power loss, torque ripple suppression, switching frequency minimization, high real-time performance, and strong robustness. Meeting these demands requires overcoming challenges like prediction-model errors, random disturbances, coupled parameter tuning, and reconciling real-time execution with global optimality in high-dimensional nonconvex optimization. Metaheuristic optimization algorithms (MOAs) present a viable alternative to traditional methods. Requiring no explicit model and offering global search capabilities with versatile mechanisms, MOAs efficiently identify model parameters, tune cost weights, and rapidly generate multi-constraint control strategies in complex spaces. This significantly accelerates MPC’s online computation and enhances disturbance rejection. This paper systematically reviews the combined application of MOAs and MPC in modern motor-drive systems, evaluating their optimization effectiveness and engineering potential across operating conditions to provide theoretical guidance and practical insights for future research.

Keywords: model predictive control; metaheuristic optimization algorithm; dynamic modeling; parameter tuning; optimization algorithms (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: 2025
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