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Model predictive control (MPC) technology for multi-phase electric drives has received increasing attention in modern industries, especially in electric vehicles, marine electrical propulsion, and wind power generation. However, MPC has several challenges in controlling multi-phase electric drives, including the design of weighting factors, high computational complexity, large harmonic currents, heavy reliance on the system model, fault-tolerant control operation, common-mode voltage, and zero-sequence current hazards. Therefore, this paper gives a comprehensive review of the latest and most effective solutions to the existing major technical challenges and prospects for the future trends of MPC for multi-phase electric drives. [ABSTRACT FROM AUTHOR]

This article proposes a novel model predictive control for a three-phase permanent-magnet synchronous motor (PMSM) with enhanced robustness against parameter variation and higher current control precision. First, the extended set of voltage vectors are adopted to increase the current control precision. Then, the parameter sensitivity problem is avoided in the first stage using an indirect reference vector strategy for the sake of computation time reduction. Subsequently, the inductance disturbance observer is adopted in the current prediction process to enhance the robustness against machine parameter mismatch. The intrinsic connection between the discrete control sets precision and disturbance observer is analyzed. The proposed method improves the robustness against parameter mismatch and current control precision simultaneously. Experimentations are conducted to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a model predictive control for a six-phase permanent magnet synchronous machine (PMSM) with a reduced-dimension cost function. Only the variables in the harmonic subspace are included into the cost function, while the energy conversion related variables are excluded. This is achieved by using the deadbeat direct torque and flux control method to obtain a reference voltage vector and then to track the torque and stator flux properly in the energy conversion related subspace. Then, according to the position of the reference vector, the appropriate prediction vectors can be determined. Subsequently, a reduced-dimension cost function including only the harmonic constraints is defined to evaluate the feasible prediction vectors. In this way, the predictive model and the cost function are simplified without redundant constraints. Meanwhile, the torque and flux can be regulated satisfactorily, and the harmonic currents are suppressed effectively. Finally, experimental results of the proposed method and the conventional model predictive torque control are presented in this paper to verify the validity of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-vector-based model predictive torque control with fixed switching frequency for a six-phase permanent-magnet synchronous machine to improve its steady-state performance. First, two active vectors are synthesized in each control period to suppress the stator current harmonics in x–y subspace. For the sake of easy implementation in the real-time system, the vectors are artfully synthesized in two different manners. Second, to achieve the fixed switching frequency, two null vectors are inserted along with the synthesized vector. The duty ratio of the null vectors is determined based on the principle of deadbeat torque control. In the meantime, the synthesized vector and its duty ratio are evaluated simultaneously by the cost function. In this way, the torque ripple can be reduced considerably. Thus, with the proposed method, both the current harmonics and the torque ripple are reduced effectively. Also, the proposed methodology can be readily implemented practically under the constant switching frequency. Finally, the experimentation is carried out to verify the validity of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a novel deadbeat current control (DBCC)-based model predictive control for an asymmetrical six-phase permanent magnet synchronous machine. First, the solution of DBCC is adopted to obtain the expected reference voltage vector (RVV). Then, two groups of virtual vectors, in the total number of 24 with different magnitudes, are defined for the sake of current harmonics suppression. Subsequently, two in-phase virtual vectors which are closest to the RVV are selected as the prediction vectors. The next step is to define a cost function which is composed of the error between the RVV and the available prediction vectors. Then, the selected two virtual vectors are evaluated and the one that minimizes the cost function will be applied in the next instant. In this way, only two prediction vectors need to be evaluated and the computation burden is highly alleviated. In the meantime, the weighting factor involved in predictive torque control is avoided. In addition, to achieve the readily implementation with standard pulsewidth modulation switching sequence, 18 virtual vectors are artfully replaced by their corresponding equivalent virtual vectors. Finally, the proposed method is comparatively studied and compared with other benchmark methods. Simulation and experimental results are offered to confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]