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

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.