Rotor resistance and speed identification using extended Kalman filter and fuzzy logic controller for induction machine drive

This paper presents a sensorless vector control of induction motor fed by a voltage converter at two levels, this type of control has the disadvantage of requiring the use of a speed sensor, which imposes additional costs and increases complex assemblies. Additionally this command involves the resistance of the rotor and the variation of this parameter could distort the decoupling between flux and torque and, thereby, lead to the deterioration in performance. To overcome these problems, we propose to estimate speed and rotor resistance of induction motor using an extended Kalman filter (EKF). The general structure of the Kalman filter is examined and the system of vectors and matrices are defined. Including the rotor speed and rotor resistance as a state variable. Furthermore, we introduce a new speed controller based on the theory of fuzzy logic that replaces the conventional PI, this approach offers the possibility of using the mathematical precision of the PI algorithm with the adaptability, flexibility and simplicity of the fuzzy linguistic formalism. The performance of the EKF and fuzzy PI is satisfactory, even in the presence of noise or when there are variations in the parameters of the induction machine.