Abstract：Finite control set-model predictive torque control (FCS-MPTC), using electromagnetic torque and stator flux as control variables, has recently received extensive attention in electric drive systems. It has the advantages of fast transient response of electromagnetic torque and ease of handling field weakening control. However, the weighting factor allocation of the FCS-MPTC cost function has always been a research challenge in the academic community. Since the essence of FCS-MPTC is the exhaustive method of discrete voltage vectors, the setting of the weight factor also needs a more theoretical basis. The rating method and cut-and-trial method are two classical weighting factor design strategies. The rating method sets the weighting factor as the ratio of rated electromagnetic torque to rated stator flux amplitude. This method can realize the coordinated control of electromagnetic torque and stator flux under the rated condition of IM, but the control performance is poor under other conditions. The cut-and-trial method obtains the weighting factor configuration through repeated online debugging, which can be more convenient and easier to obtain optimal control. Firstly, the influence mechanism of different weighting factors on stator current total harmonic distortion (THD) of stator current, root mean square (RMS) of electromagnetic torque, and RMS of stator flux linkage is studied. The results show that with the increase of the weighting factor, the stator flux RMS decreases, the electromagnetic torque RMS is just the opposite, and the stator current THD decreases first and then increases. Secondly, the stator current THD, RMS of electromagnetic torque, and RMS of stator flux linkage of the motor under different operating conditions with a fixed weighting factor are studied. The results show that when the induction motor (IM) FCS-MPTC operates at low speed, the performances of stator flux RMS and electromagnetic torque RMS worsen. For the induction motor FCS-MPTC under low speed and light load, the stator current THD exhibits large harmonics. Then, the mathematical expression of the weighting factor is derived analytically according to the internal relations of electromagnetic relations of IM, which can achieve multi-objective optimal control of induction motors under different working conditions. Because the analytical expression of the weighting factor depends on the motor parameters, this paper integrates the online parameter identification technology, which can achieve the calculation accuracy of the weighting factor and the prediction model. The proposed analytical weighting factor-model predictive torque control (AWF-MPTC) has a smaller stator current THD and electromagnetic torque ripple than traditional MPTC (T-MPTC). In steady states, the stator current THD of AWF-MPTC is 11.52%, while that of T-MPTC is 16.13%. T-MPTC stator flux ripple is 0.12 Wb, while AWF-MPTC is 0.068 Wb. At the same time, AWF-MPTC has better robustness than T-MPTC. There are two main reasons for the improvement of robustness performance. Firstly, the analytical weighting factor calculation method realizes the coordinated control of electromagnetic torque and stator flux. Secondly, the online identification of induction motor parameters improves the accuracy of the prediction model and the weighting factor. Compared with T-MPTC, the proposed AWF-MPTC has better dynamic performance, steady performance, and robustness. Under different operating conditions, the optimal coordinated control of electromagnetic torque and stator flux is realized.
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2023, Vol. 38 
