Descriptor-type Kalman Filter and TLS EXIN Speed Estimate for Sensorless Control of a Linear Induction Motor

Abstract

This paper proposes a speed observer for linear induction motors which is composed of two parts: 1) a Kalman filter (KF for the on-line estimation of the machine state variables (inductor currents and induced part flux linkage components), 2) a speed estimator based on the total least-squares (TLS) EXIN neuron. The TLS estimator receives as inputs the state variables, as estimated by the KF, and provides as output the linear LIM speed which is fed back to the KF and the control system. The KF is based on the classic space-vector model of the rotating induction machine (RIM). The TLS EXIN neuron has been used to compute, in recursive form, the machine linear speed on-line, since it is the only neural network able to solve on-line in a recursive form a total least-squares problem. The proposed KF-TLS speed observer has been tested experimentally on a suitably developed test setup.