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Electrical Motor Parameters Estimator Improved by a Computational Algorithm

Electrical Motor Parameters Estimator Improved by a Computational Algorithm

Flah. Aymen, Habib Kraiem, Sbita. Lassaâd
Copyright: © 2015 |Pages: 34
ISBN13: 9781466672482|ISBN10: 146667248X|EISBN13: 9781466672499
DOI: 10.4018/978-1-4666-7248-2.ch021
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MLA

Aymen, Flah., et al. "Electrical Motor Parameters Estimator Improved by a Computational Algorithm." Handbook of Research on Advanced Intelligent Control Engineering and Automation, edited by Ahmad Taher Azar and Sundarapandian Vaidyanathan, IGI Global, 2015, pp. 567-600. https://doi.org/10.4018/978-1-4666-7248-2.ch021

APA

Aymen, F., Kraiem, H., & Lassaâd, S. (2015). Electrical Motor Parameters Estimator Improved by a Computational Algorithm. In A. Azar & S. Vaidyanathan (Eds.), Handbook of Research on Advanced Intelligent Control Engineering and Automation (pp. 567-600). IGI Global. https://doi.org/10.4018/978-1-4666-7248-2.ch021

Chicago

Aymen, Flah., Habib Kraiem, and Sbita. Lassaâd. "Electrical Motor Parameters Estimator Improved by a Computational Algorithm." In Handbook of Research on Advanced Intelligent Control Engineering and Automation, edited by Ahmad Taher Azar and Sundarapandian Vaidyanathan, 567-600. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-7248-2.ch021

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Abstract

In this chapter, two computational algorithms are proposed and applied on an estimation algorithm, in order to improve the global performance of the estimation phase. The proposed system is studied based on the Model Reference Adaptive System (MRAS). The importance of the estimation phase in a large applications number is basically observed on the applications applied on electrical motors, where a lot number of parameters are measured with real measurement equipments, as Tesla Meter, speed shaft, and others. The idea is based generally on the software applications, where it is possible to guarantee the desired estimation phase using a software algorithm. In this chapter the MRAS technique is proposed as the software algorithm, for replacing the measurement materials for online estimate the overall characteristic PMSM parameters. Our approach aims to ameliorate the MRAS technique with intelligent optimization methods called BFO and PSO.

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