Browsing by Author "Sel, Artun"

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    Comparative study of an EKF-based parameter estimation and a nonlinear optimization-based estimation on PMSM system identification
    (MDPI AG, 2021-09-25) Sel, Artun; Sel, Bilgehan; Coskun, Umit; Kasnakoğlu, Cosku
    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 imple mented 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 dynami cal 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.
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    GLSDC based parameter estimation algorithm for a PMSM model
    (MDPI AG, 2021-01-26) Sel, Artun; Sel, Bilgehan; Kasnakoğlu, Cosku
    In this study, a GLSDC (Gaussian Least Squares Differential Correction) based parameter estimation algorithm is used to identify a PMSM (Permanent Magnet Synchronous Motor) model. In this method, a nonlinear model is assumed to be the correct representation of the underlying state dynamics and the output signals are assumed to be measured in a noisy environment. Using noisy input and output signals, parameters that constitute the coefficients of the nonlinear state and input signal terms are to be estimated using the state transition matrix which is computed by the numerical means that are detailed. Since a GLSDC algorithm requires correct initial state value, this term is also estimated in addition to the unknown coefficients whose bounds are assumed to be known, which is mostly the case in the industrial applications. The batch input and output signals are used to iteratively estimate the parameter set before and after the convergence, and to recover the filtered state trajectories. A couple of different scenarios are tested by means of numerical simulations and the results are addressed. Different methods are discussed to compute better initial estimate values, to shorten the convergence time.