Adaptive fuzzy logic speed-sensorless control improvement of induction motor for standstill and low speed operations Katia Kouzi Electrical Engineering Department, Laghouat University, Laghouat, Algeria, and Mohamed Saı ¨d Naı ¨t-Saı ¨d Propulsion Induction Electromagnetic Systems Laboratory, Batna University, Batna, Algeria Abstract Purpose – This work proposes a method to improve the estimation performance at standstill and low speed operations of an adaptive fuzzy logic speed-sensorless field-oriented control of an induction motor. Design/methodology/approach – First, the speed estimation algorithm presented in Tursini et al., which it has been designed to consider constant speed operation, is modified in an attempt to reduce the estimation error. Second, the speed regulation by fuzzy logic controller (FLC) with fuzzy adapted gains (FAG) is proposed for speed regulation. The main features of the proposed algorithm are investigated and compared with those of the algorithm of (Tursini) considering different dynamic operating conditions. Findings – Simulation results clearly show the performance of the proposed algorithm. Originality/value – The proposed scheme is recommended for applications requiring robust speed control and field-orientation even in the presence of some key parameter deviations. Keywords Fuzzy control, Simulation, Electric motors Paper type Research paper Introduction Sensorless vector control is presently given an increasing attention in many industrial application. Sensorless control eliminates speed, flux and torque sensors and replaces them by digital signal processor (DSP) based estimators, using the machine terminal voltages and currents. Thus, the cost of the drive is reduced and reliability is enhanced (Schauder, 1992; Pinto et al., 2001; Yan and Utkin, 2000; Tursini et al., 2000; Tsuji et al., 2003; Boussak and Jarray, 2006). Several schemes of speed estimators have been proposed in the literature. They can be generally grouped in two major classes: (1) deterministic observers (MRAS, full or reduced-order adaptive observer, Luenberger observer); and (2) stochastic observers based on extended Kalman filter theory (Tursini et al., 2000; Montanari et al., 2000; Tajima et al., 2000; Kouzi and Mokrani, 2004; Lin et al., 1998; Guidi and Umida, 2000; Kim et al., 2001). The current issue and full text archive of this journal is available at www.emeraldinsight.com/0332-1649.htm COMPEL 26,1 22 COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering Vol. 26 No. 1, 2007 pp. 22-35 q Emerald Group Publishing Limited 0332-1649 DOI 10.1108/03321640710713949