Adaptive Full State Observer for Nonsalient PMSM with Noised Measurements of the Current and Voltage

An algorithm of adaptive estimation of the magnetic flux for the non-salient permanent magnet synchronous motor (PMSM) for the case when measurable electrical signals are corrupted by a constant offset is presented. A new nonlinear parameterization of the electric drive model based on dynamical regressor extension and mixing (DREM) procedure is proposed. Due to this parameterization the problem of flux estimation is translated to the auxiliary task of identification of unknown constant parameters related to measurement errors. It is proved that when both current and voltage measurements are biased the proposed algorithm ensures convergence of the flux observation error to a bounded set. At the same time the position error converges to zero. The observer provides global exponential convergence if the corresponding regression function satisfies the persistent excitation condition. If the regression function is not square integrable the global asymptotic convergence is ensured. In comparison with known analogues this paper gives a constructive way of the flux reconstruction for a nonsalient PMSM with guaranteed performance (low oscillation, convergence rate regulation) and, from other hand, a straightforwardly easy implementation of the proposed observer to embedded systems.