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Comparative Study of an EKF-Based Parameter Estimation and a Nonlinear Optimization-Based Estimation on PMSM System Identification

Author

Listed:
  • Artun Sel

    (Department of Electrical-Electronics Engineering, TOBB University of Economics and Technology, 06510 Ankara, Turkey)

  • Bilgehan Sel

    (Department of Electrical and Electronics Engineering, Bilkent University, 06800 Ankara, Turkey)

  • Umit Coskun

    (Department of Physics and Astronomy, University of Kentucky, Lexington, KY 40506, USA)

  • Cosku Kasnakoglu

    (Department of Electrical-Electronics Engineering, TOBB University of Economics and Technology, 06510 Ankara, Turkey)

Abstract

In this study, two different parameter estimation algorithms are studied and compared. Iterated EKF and a nonlinear optimization algorithm based on on-line search methods are implemented to estimate parameters of a given permanent magnet synchronous motor whose dynamics are assumed to be known and nonlinear. In addition to parameters, initial conditions of the dynamical system are also considered to be unknown, and that comprises one of the differences of those two algorithms. The implementation of those algorithms for the problem and adaptations of the methods are detailed for some other variations of the problem that are reported in the literature. As for the computational aspect of the study, a convexity study is conducted to obtain the spherical neighborhood of the unknown terms around their correct values in the space. To obtain such a range is important to determine convexity properties of the optimization problem given in the estimation problem. In this study, an EKF-based parameter estimation algorithm and an optimization-based method are designed for a given nonlinear dynamical system. The design steps are detailed, and the efficacies and shortcomings of both algorithms are discussed regarding the numerical simulations.

Suggested Citation

  • Artun Sel & Bilgehan Sel & Umit Coskun & Cosku Kasnakoglu, 2021. "Comparative Study of an EKF-Based Parameter Estimation and a Nonlinear Optimization-Based Estimation on PMSM System Identification," Energies, MDPI, vol. 14(19), pages 1-14, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6108-:d:642778
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    References listed on IDEAS

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    1. Daniel Gapiński & Zbigniew Koruba, 2021. "Control of Optoelectronic Scanning and Tracking Seeker by Means the LQR Modified Method with the Input Signal Estimated Using of the Extended Kalman Filter," Energies, MDPI, vol. 14(11), pages 1-17, May.
    2. Haipeng Pan & Chengte Chen & Minming Gu, 2021. "A State of Health Estimation Method for Lithium-Ion Batteries Based on Improved Particle Filter Considering Capacity Regeneration," Energies, MDPI, vol. 14(16), pages 1-12, August.
    3. Benedikt Rzepka & Simon Bischof & Thomas Blank, 2021. "Implementing an Extended Kalman Filter for SoC Estimation of a Li-Ion Battery with Hysteresis: A Step-by-Step Guide," Energies, MDPI, vol. 14(13), pages 1-17, June.
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