Adaptive Flux 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 the flux observer provides global exponential convergence of estimation errors to zero if the corresponding regression function satisfies the persistent excitation condition. Also, the observer provides global asymptotic convergence if the regression function is square integrable. In comparison with known analogues this paper gives a constructive way of the flux reconstruction for a nonsalient PMSM with guaranteed performance (monotonicity, convergence rate regulation) and, from other hand, a straightforwardly easy implementation of the proposed observer to embedded systems.

Convergence of neutral type proportional-delayed HCNNs with D operators

This paper is concerned with neutral type high-order cellular neural networks (HCNNs) involving proportional delays and [Formula: see text] operators. Some criteria are established for the global exponential convergence of the addressed models by using differential inequality techniques. Moreover, an example and its numerical simulations are employed to illustrate the main results.