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Current-Controller-Free Self-Commissioning Scheme for Deadbeat Predictive Control in Parametric Uncertain SPMSM

Authors: Jiang Long; Ming Yang 0006; Yangyang Chen 0003; Kaiyuan Liu; Dianguo Xu 0001;

Current-Controller-Free Self-Commissioning Scheme for Deadbeat Predictive Control in Parametric Uncertain SPMSM

Abstract

Deadbeat predictive control (DBPC) requires accurate model parameters in its application, therefore it is difficult to use DBPC for surface mounted permanent magnet synchronous motor (SPMSM) with uncertain motor parameters. As a solution, this paper proposes a strategy to obtain the needed parameters, and then uses them to achieve DBPC on SPMSMs whose parameters are unknown. A detailed investigation on parameter sensitivity of DBPC is presented, and attention is paid to different influences between the incremental inductance and the apparent inductance. On this basis, parameter configurations in the DBPC are suggested. And novel current-controller-free self-commissioning schemes are proposed to identify the stator resistance together with the stator inductances under different magnetic saturation levels. Compared with existing commissioning schemes that do not depend on current controllers, solutions for commissioning voltage auto-tuning is provided. So the methods are able to automatically decide the amplitudes of the injected voltage for a given motor, and achieve a controllable current feedback during the commissioning. This brings significant convenience for the inductance identification at a specific saturation level. Moreover, the inverter nonlinearity compensation is not needed but still, identification accuracy can be guaranteed. The feasibility and effectiveness of the proposed method are confirmed by the achieved DBPC and the corresponding current tracking performances on two different SPMSMs.

Related Organizations
Keywords

parameter mismatch, Permanent magnet motors, current-controller-free, Electrical engineering. Electronics. Nuclear engineering, deadbeat predictive control, TK1-9971

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
27
Top 10%
Top 10%
Top 10%
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