The application of model predictive control in electrical drives has been studied extensively in the past decade. This article 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 article aims to enable readers to have a more precise overview while facilitating their future research work in this exciting area.

This paper presents the application of Model Predictive Control (MPC) in high-performance drives. A wide variety of machines have been considered: induction machines, synchronous machines, linear motors, switched reluctance motors, and multiphase machines. The control of these machines has been done by introducing minor and easy-to-understand modifications to the basic predictive control concept, showing the high flexibility and simplicity of the strategy. The second part of the paper is dedicated to the performance comparison of MPC with classical control techniques such as field-oriented control and direct torque control. The comparison considers the dynamic behavior of the drive and steady-state performance metrics such as inverter losses, current distortion in the motor, and acoustic noise. The main conclusion is that MPC is very competitive concerning classic control methods by reducing the inverter losses and the current distortion with comparable acoustic noise.

Model predictive torque control (MPTC) is emerging as a powerful control scheme for high performance control of induction motor (IM) drives. Compared to direct torque control, MPTC is more effective and accurate in voltage vector selection by incorporating the system model directly with the finite switching states. However, for two-level inverter-fed IM drives, the sampling frequency of MPTC has to be high to achieve good performance due to the limited number of voltage vectors. Recently, the concept of duty cycle control was introduced in MPTC by inserting a null vector along with the selected active voltage vector to achieve torque ripple reduction. The active vector is first selected from conventional MPTC and then its duration is determined based on a certain principle. The cascaded processing of active vector and its duration leads to poor low-speed performance and the sampling frequency still has to be high. This paper proposes an improved MPTC with duty cycle control by optimizing the vector selection and its duration simultaneously when minimizing both torque and flux errors. As a result, better steady-state performance at both low and high speeds is achieved, even if the sampling frequency is reduced by half. The effectiveness of the proposed MPTC is verified by both simulation and experimental results.

In conventional direct torque controlled (DTC) permanent magnet synchronous motor drive, there is usually undesired torque and flux ripple. The existing literature have proposed some methods to reduce torque and flux ripple by optimizing the duty ratio of the active vector. However, these methods are usually complicated and parameter dependent. This paper first compares the performances of three duty determination methods in detail and then proposes a very simple but effective method to obtain the duty ratio. The novel method is superior to the existing methods in terms of simplicity and robustness. By appropriately arranging the sequence of the vectors, the commutation frequency is reduced effectively without performance degradation. To further improve the performance of system, a low-pass filter-based voltage model with compensations of amplitude and phase is employed to acquire accurate stator flux estimation. The proposed scheme is able to reduce the torque and flux ripple significantly while maintaining the simplicity and robustness of conventional DTC at the most. Simulations and presented experimental results validate the effectiveness of the proposed schemes in this paper.

Model-predictive current control (MPCC) is widely recognized as a high-performance control strategy of permanent magnet synchronous machine (PMSM) drives due to its quick response and simple principle. It uses a cost function to select the best voltage vector minimizing the current error between the reference value and the feedback value. However, as only one voltage vector is applied during one control period, it fails to give satisfactory performance due to the limited voltage vectors, especially in the case of two-level converters. This paper proposes an improved MPCC strategy for PMSM drives, which first estimates the back electromotive force (EMF) based on the past value of stator voltage and currents and then applies the estimated EMF in the stator current prediction. To achieve steady-state performance improvement, a null vector along with the active vector obtained from conventional MPCC is applied during one control period. Two methods are proposed to achieve optimal vector selection and vector duration. The first one requires six predictions and the calculation of current differentiation, while the second one only requires one prediction to obtain the best voltage vector and its optimal duty can be obtained in a very efficient way. The proposed methods are comparatively studied and compared to conventional MPCC and deadbeat control with space vector modulation. Both simulation and experimental results confirm the effectiveness of the proposed methods in achieving good steady-state performance while maintaining quick dynamic response.