Optimal Weighting Factors Design for Model Predictive Current Controller for Enhanced Dynamic Performance of PMSM Employing Deep Reinforcement Learning  
[PERSON]  
1 ESIGELEC, IRSEEM, Universite de Rouen Normandie, 76000 Rouen, France; [EMAIL_ADDRESS] (A.S.); thomas.chevet@esiglec. a result, researchers are actively seeking advanced control strategies to enhance the performance of the PMSM drive system. Among other advanced control strategies, model predictive control (MPC) has gained much attention in the control of power electronics and motor drives due to its ability to handle multivariable problems with constraints and nonlinearities [6]. MPC has been extensively used in various industrial applications, including wind power generation systems, computer numerical control machine drive systems, and high-speed rail traction drive systems [7; 8; 9]. As a result, the last decade has seen the study of various types of MPC for drive applications based on fixed or randomly distributed switching frequencies across converters. These predictive strategies can be classified into two categories, namely continuous control set (CCS) MPC and finite control set (FCS) MPC. In CCS-MPC, a constant switching frequency can be achieved through the modulation stage. However, the prediction process involved is complex [10]. On the other hand, FCS-MPC has become more popular among other predictive control strategies due to its intuitive nature and straightforward implementation. Indeed, it optimizes the switching states directly without needing an external modulation stage, resulting in a randomly distributed switching frequency [11]. Various approaches have been proposed to achieve a constant switching frequency with FCS-MPC in the literature [12; 13; 14]. Furthermore, a data-driven predictive controller has been employed to achieve gating without requiring the explicit model of the plant, helping to improve robustness and transient response [15]. Although FCS-MPC offers impressive advantages, such as high-performance multi-objective control with inherent constraints, it faces several challenges. In addition, this control strategy can lead to variable switching frequency in the inverter output, which is often undesirable, as it can cause the risk of triggering an unexpected resonance and affect the power efficiency of the inverter [16]. Then, the central element of the FCS-MPC strategy is its cost function, essential to balance various control objectives, including tracking error, control effort, total harmonic distortion (THD), switching frequency, and torque ripple, all under state constraints. All or part of these conflicting objectives can appear simultaneously in an asymmetric cost function, each weighted by a different factor, balancing their relative importance. One of the most critical issues is therefore the choice of optimal weights within the asymmetric cost function, which can significantly impact the overall effectiveness of the controller [17]. It is important to note that the weighting factors within the cost function directly impact the control performance of PMSM drives, particularly regarding total harmonic distortion (THD) and switching frequency, as they manage the trade-offs between the different objectives. In addition, current convergence ensures accurate tracking of the reference current, which directly affects torque precision and dynamic response. However, achieving fast convergence often requires fast inverter switching, which leads to increased switching frequency, power losses, and thermal stress on the semiconductor devices (IGBT, MOSFET). On the other hand, minimizing switching transitions improves inverter efficiency, reduces electromagnetic interference, and enhances the reliability and lifespan of power electronics, but may compromise current tracking performance and introduce torque ripples. Incorrectly set weighting factors can lead to misalignment in prioritizing control targets, ultimately reducing control effectiveness [18].