2019
DOI: 10.1109/access.2019.2892749
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Abstract: This paper designs an adaptive sliding mode speed controller (ASMSC) that can compensate for parameter uncertainty of an interior permanent magnet synchronous motor (IPMSM) drive. Unlike the previous control systems, the proposed ASMSC guarantees a precise speed tracking capability in the presence of severe parameter variations without accurate knowledge on the motor parameter values and uncertainty bounds. In particular, the proposed adaptive switching gain tuning (ASGT) term can effectively solve the excessi… Show more

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Cited by 26 publications
(9 citation statements)
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“…In fact, motor load occupies a large proportion in power systems [46]. Because the motor load has high requirements on the line impedance imbalance, and the control process is difficult compared with the ordinary load [47], this section uses the motor load as an example to verify the method proposed in this paper. The system parameter settings, load switching time, and load capacity are the same as in the previous section.…”
Section: ) Unbalanced Condition Under Motor Loadmentioning
confidence: 99%
“…In fact, motor load occupies a large proportion in power systems [46]. Because the motor load has high requirements on the line impedance imbalance, and the control process is difficult compared with the ordinary load [47], this section uses the motor load as an example to verify the method proposed in this paper. The system parameter settings, load switching time, and load capacity are the same as in the previous section.…”
Section: ) Unbalanced Condition Under Motor Loadmentioning
confidence: 99%
“…However, this kind of method is quite time-consuming, which results in a low utilizing rate of hardware resource when the operating conditions change slowly. The second method replaces PI controller with advanced control strategies such as model-free predictive control [13] and adaptive sliding mode control [14]. These methods have improved the control performance in some aspects, faster transient-state response, or better robustness against machine parameter variations, but the superiority comes at the expense of higher current controller complexity.…”
Section: Introductionmentioning
confidence: 99%
“…The commonly used position estimation methods for IPMSM can be divided into to two categories. One category is based on the extended back-electromotive force (Back-EMF), such as the Kalman filter method [1], [2], the model reference adaptive method [3]- [5], the observer method [6]- [9], and the artificial intelligence (AI) based method [10], [11]. Since Back-EMF is small and difficult to be observed when the rotating speed is low, these methods are only effective in the medium speed and high speed range.…”
Section: Introductionmentioning
confidence: 99%