In this article, a neural network (NN) approach is introduced to estimate the nonnoisy speed
and torque from noisy measured currents and voltages in induction motors with variable …

Sensorless control of permanent-magnet synchronous motors at low velocity remains a
challenging task. A now well-established method consists of injecting a high-frequency …

Recent advances in the real‐time simulation of electric machines are linked with the
increase in the operation speed of the numerical models retaining the calculation accuracy …

Electrical motors are the most important source of mechanical energy in the industrial world.
Their modeling traditionally relies on a physics-based approach, which aims at taking their …

This paper focuses on the quantitative analysis of deep neural networks used in data-driven
modeling of induction motor dynamics. With the availability of a large amount of data …

Modeling induction motor dynamics is a crucial problem in the industry. The previous works
mainly model the dynamics based on the physical model assumption and state equation …

This paper describes a position estimation approach based on high-frequency voltage
injections for dual three-phase permanent magnet synchronous machines (DTP-PMSM) …

We analyze why low-speed sensorless control of the IM is intrinsically difficult, and what is
gained by signal injection. The explanation relies on the control-theoretic concept of …

This paper proposes a method based on signal injection to obtain the saturated current-flux
relations of a PMSM from locked-rotor experiments. With respect to the classical method …

DEVELOPMENT FUncTIOnS InTEGRATIOn be emphasized that the described methods are
universally applicable to any machine topology, including switched reluctance and …