Abstract: In this paper, we consider the problem of flux, position and speed observer design for permanent magnet synchronous motors (PMSMs) with uncertain parameters. It is assumed that the only measured signals are stator currents and control voltages. The key feature of the proposed approach is that it requires the knowledge of only one structural parameter of PMSM model – the number of pole pairs. Thus, all electrical and mechanical parameters, namely, the stator resistance and inductance, constant flux from permanent magnets, motor inertia and viscous friction coefficient are assumed to be unknown. A new nonlinear parameterization of motor model is proposed that is resulted in the regression model of eleven unknown parameters including the stator resistance and inductance as well as two parameters involved in the state observer design. The dynamic regressor extension and mixing (DREM) estimator is used to provide good performance and fast estimation of unknown parameters which is more efficient than the standard gradient approach in the case of high-dimensional regression models. Simulation results carried out for a typical scenario of motor operation illustrate good performance of the designed observer and parameter estimators.
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