Abstract
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.
Highlights
There are some variations of Permanent Magnet Synchronous Motor (PMSM) that are popular in unmanned aerial vehicle (UAV) applications, such as propeller mover in fixed wing aircrafts and as camera motion stabilizers in surveillance applications
Large classes of the control algorithms, that are implemented in industrial applications, require a reliable model or a set of models that model the dynamics of the plant to an Academic Editor: Erol Kurt
To test the effectiveness of the parameter estimation algorithm derived in the previous section, a series of simulations have been conducted
Summary
For the sake of brevity, a simplified version of the rotating reference frame model is studied, the parameter estimation algorithm can be applied to the problem in a more general setting. The states x1 , x2 , x3 , x4 denote direct and quadrature axis currents, angular velocity and angular position of the rotor, respectively. U1 and u2 denote direct and quadrature axis applied stator voltages, respectively [26]. The measurement model is given as, y1 = x1 + v1. Where, v1 and v2 represent Additive White Gaussian Noise (AWGN) terms for the measurement model with v ∼ N (0, R), where R represents the noise covariance matrix. GLSDC algorithm to iteratively estimate these terms are employed and it is derived
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