Abstract
The deadbeat control of Permanent Magnet Synchronous Machines (PMSMs) has aroused great attentions in the recent years due to its high dynamic performance. However, its control performance significantly relies on the accuracy of the parameters in PMSM model. Therefore, in this paper, a novel online parameter identification algorithm for deadbeat control of PMSM is proposed. Firstly, a new parameter identification model for deadbeat control, which can express the relations between parameter errors and controlling offsets, is proposed. In this model, the nonlinearity of Voltage Source Inverter (VSI) is also taken into fully consideration. Based on the proposed model, the parameter identification algorithm is developed. In this algorithm, a novel “parameters variation method” is proposed for gathering the essential data which is vital to the identification process. To obtain the parameter identification results, a less complicated Adaline Neural Network (ANN) is employed. Finally, the effectiveness and superiority of the proposed algorithm is verified by experimental results based on a PMSM platform.
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