Model predictive control of electromagnetic torque in permanent magnet synchronous machines
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This study focuses on the development of novel model predictive control method for PMSM drive system. The aims of the proposed control method are flux and torque ripples reduction. The performances of the proposed model predictive control method and conventional direct torque control (DTC) are comparatively studied in both simulation and experimental tests. In recent years, various variable speed drive strategies and structures for PMSMs have been widely investigated and reported. Among these control strategies, the six-step control, field oriented control (FOC) and DTC are the most popular ones. Among them, the conventional DTC, which selects the desired voltage vector based on hysteresis comparators and switching table, features a fast dynamic response and very simple structure. The major demerits of DTC are large torque and flux ripples, variable switching frequency, and acoustic noises. Recently, the model based predictive control (MPC) was introduced to overcome these problems. However, the improvement was limited for the purposes of torque and flux ripple reduction and the MPC still suffered from variable switching frequency. The conventional DTC and MPC are similar in that they both select only one voltage vector per sampling period. This may result in overregulation, which is the key issue for torque and flux ripples and excessive acoustic noise. In this thesis, an improved MPC method with duty ratio optimization was proposed for PMSMs. The proposed method features low torque and flux ripples and relatively stable switching frequency. It is of most benefit when the drive system is working at a low sampling frequency because in the low frequency range, the proposed MPC drive system can achieve much lower torque and flux ripples than the original MPC and DTC, which is a very desirable feature for high power applications (e.g. electric vehicles). Finally, the numerical simulation and experimental test results of the conventional DTC, MPC, and improved MPC were presented to verify its effectiveness.
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