Sensorless Vector Control of Induction Motor Drive - A Model Based Approach

Induction machines are more rugged, compact, cheap and reliable in comparison to other machines used in similar applications. Vector controlled induction motor drive outperforms the dc motor drive because of higher transient current capability, increased speed range and lower rotor inertia. Sensors widely used in electric drives degrade the reliability of the system especially in hostile environments and require special attention to electrical noise. Moreover, it is difficult to mount sensors in certain applications in addition to extra expenses involved. Therefore, a lot of researches are underway to develop accurate speed estimation techniques. With sensorless vector control we have a decoupled control structure similar to that of a separately excited dc motor retaining the inherent ruggedness of the induction motor at the same time. Speed sensorless control technique first appeared in (Abbondante & Brennen, 1975). The commonly used methods for speed estimation are Model Reference Adaptive System (MRAS) (Schauder, 1992; Tajima & Hori, 1993; Peng & Fukao, 1994; Choy et al., 1996), Neural Networks (Simoes & Bose, 1995; Fodor et al., 1995; Ben-Brahim & Kudor, 1995; Kim et al., 2001; Toqeer & Bayindir, 2003; Haghgoeian et al., 2005), Extended Kalman Filter (EKF) (Kim et al.,1994, Comnac et al., 2001; Ma & Gui, 2002; Du et al., 1995; Thongam & Thoudam, 2004) and Nonlinear Observer (Bodson et al., 1995; Liu et al., 2001, Pappano et al., 1998). The aim of this chapter is to provide with a brief overview of high performance sensorless induction motor drive. There exist two approaches to speed estimation for sensorless control of induction machine: non model based approach and model based approach. The non model based technique tracks a machine anisotropy: either saturation for flux estimation or rotor slotting for rotor position estimation, whereas the model based technique rely mostly on back emf voltage associated with fundamental component excitation of the machine. Model-based observers are considered very well adapted for state estimation and allow, in most cases, a stability proof and a methodology to tune observer gains. Among the observers the reduced order observers are more frequently implemented than the full order ones as they don’t require heavy computations. Two sensorless vector control strategies using machine model-based estimation are presented in this chapter.