A neural network left-inversion flux estimation for induction motor filed-oriented control

This paper presents a new rotor flux estimation algorithm using neural network for induction motors, based on the left-inversion method. Using the fifth order model of the three-phase induction machines in a stationary two axes reference frame, a rotor flux "assumed inherent sensor" is constructed and its left-invertible is validated. The ANN left-inversion flux estimator is composed of two relatively independent parts - a static ANN used to approximate the complex nonlinear function and several differentiators used to represent its dynamic behaviors, so that the ANN left-inversion is a special kind of dynamic ANN in essence. The performance of the proposed algorithm is tested through simulation, proving the driven system has good behavior both in transient and steady-state operating conditions.