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Development of a Resolver-to-Digital Converter Based on Second-Order Difference Generalized Predictive Control

Thyago Estrabis, Gabriel Gentil and Raymundo Cordero
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Thyago Estrabis: Graduation Program in Electrical Engineering, Federal University of Mato Grosso do Sul, Campo Grande, MS 79070-900, Brazil
Gabriel Gentil: Graduation Program in Electrical Engineering, Federal University of Mato Grosso do Sul, Campo Grande, MS 79070-900, Brazil
Raymundo Cordero: Graduation Program in Electrical Engineering, Federal University of Mato Grosso do Sul, Campo Grande, MS 79070-900, Brazil

Energies, 2021, vol. 14, issue 2, 1-22

Abstract: High-performance motor drives that operate in harsh conditions require an accurate and robust angular position measurement to correctly estimate the speed and reduce the torque ripple produced by angular estimation error. For that reason, a resolver is used in motor drives as a position sensor due to its robustness. A resolver-to-digital converter (RDC) is an observer used to get the angular position from the resolver signals. Most RDCs are based on angle tracking observers (ATOs). On the other hand, generalized predictive control (GPC) has become a powerful tool in developing controllers and observers for industrial applications. However, no GPC-based RDC with zero steady-state error during constant speed operation has been proposed. This paper proposes an RDC based on the second-order difference GPC (SOD-GPC). In SOD-GPC, the second-order difference operator is applied to design a GPC model with two embedded integrators. Thus, the SOD-GPC is used to design a type-II ATO whose steady-state angle estimation error tends to zero during constant speed operation. Simulation and experimental results prove that the proposed RDC system has better performance than other literature approaches.

Keywords: angle tracking observer; generalized predictive control; resolver; resolver-to-digital converter; tracking (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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