The conventional model predictive control for dual three-phase motors only evaluates the largest voltage vectors to alleviate the computation burden, but at the costs of large current harmonics and torque ripple. This paper presents a simplified model predictive torque control (MPTC) for an asymmetrical dual three-phase permanent magnet synchronous motor (PMSM), which can significantly improve the machine performances. First, the 36 active voltage vectors in the outer three layers are considered with the proposed control method. In order to suppress the stator harmonic currents, the voltage vectors are preselected based on a switching table according to the flux position and torque deviation in the α-β, x-y subspace. Simultaneously, the number of prediction voltage vectors can also be reduced, thus effectively decreasing the computation time. In particular, with the proposed MPTC method, the harmonic currents can be effectively suppressed. Furthermore, the computational burden can be considerably alleviated. In the meantime, the merits of the conventional MPTC such as fast dynamic response and intuitive implementation are preserved. Finally, both simulation and experimental results are performed to verify the validation of the proposed MPTC methodology when comparing with the conventional MPTC for the dual three-phase PMSMs.

This paper 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 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.

This paper proposes a novel dead-beat current control (DBCC) based model predictive control for an asymmetrical six-phase permanent magnet synchronous machine (PMSM). 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 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. So, only two prediction vectors need to be evaluated and the computation burden is highly alleviated. The weighting factor involved in predictive torque control is avoided. In addition, to achieve the readily implementation with standard PWM switching sequence, 18 virtual vectors are artfully replaced by their equivalent virtual vectors. Finally, the proposed method is comparatively studied and compared with the conventional model predictive current control method. Experimental results are offered to confirm the effectiveness of the proposed method.

This paper proposes a multi-vector based model predictive torque control (MPTC) with fixed switching frequency for a six-phase permanent-magnet synchronous machine (PMSM) 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 are carried out to verify the validity of the proposed method.

This paper proposes a model predictive control (MPC) scheme based remedial control for the single-phase open-circuit fault (OCF) of an asymmetrical six-phase PMSM machine. The proposed strategy adopts the normal vector space decomposition (VSD) transformation matrix without reconfiguring the controller topology. The key is to derive the perturbation term of the prediction vectors in α-β and x-y subspace, which is based on the difference of the faulty phase voltage under healthy and faulty condition. Through the compensation of the prediction vectors, the predictive model can be accurately described in the faulty condition. Thus, the current variables in the next instant is accurately predicted and the torque fluctuation is suppressed. Finally, the experimental results are provided to verify the validity of the proposed method on pre- and post-fault tolerant operation.