Latest Advances of Model Predictive Control in Electrical Drives. Part I: Basic Concepts and Advanced Strategies

The application of Model Predictive Control (MPC) in electrical drives has been studied extensively in the last decade. This paper presents what the authors consider the most relevant contributions published in the last years, mainly focusing on three relevant issues: weighting factor calculation when multiple objectives are utilized in the cost function, current/torque harmonic distortion optimization when the power converter switching frequency is reduced, and robustness improvement under parameters uncertainties. Therefore, this paper aims to enable readers to have a more precise overview while facilitating their future research work in this exciting area.

Conclusions
This paper showed that Model Predictive Control is being used successfully in high-performance motor drive applications. With little effort, MPC has been applied in field-oriented control and direct control of flux and torque. Two different and interesting strategies have solved the classical problem of weighting factor calculation in the cost function when using predictive control. The first method uses sequential predictive control to avoid the use of weighting factors. This solution is straightforward to understand and implement without sacrificing the drive’s high-quality transient behavior. The second strategy uses Artificial Neural Networks to obtain the optimal value for the weighting factor, introducing artificial intelligence techniques in the core of the control method, which opens a very attractive area for future research. The use of a multiple-step prediction has reduced the distortion of the load current generated by the inverter. Besides, for higher power motor drives, optimized pulse patterns can be integrated with predictive control to further reduce the distortion in the load current while operating with a low switching frequency. Finally, the model-free strategy has demonstrated that it is possible to control the machine with high quality, without the need for a precise model, which offers a significant opportunity to improve the drive’s robustness, introducing modern estimation techniques into the control algorithm. These selected advanced topics showed that the application of Model Predictive Control opens the possibility to continue improving the behavior of high-performance motor drives, including more intelligent techniques and advanced optimization algorithms.

Jose Rodriguez; et al., “Latest Advances of Model Predictive Control in Electrical Drives. Part I: Basic Concepts and Advanced Strategies,” in IEEE Transactions on Power Electronics, doi: 10.1109/TPEL.2021.3121532.