594 IEEE TRANSACTIONS ONINDUSTRIAL ELECTRONICS, VOL. 55, NO. 2, FEBRUARY 2008 Model Reference Adaptive Controller-Based Rotor Resistance and Speed Estimation Techniques for Vector Controlled Induction Motor Drive Utilizing Reactive Power Suman Maiti, Chandan Chakraborty, Senior Member, IEEE, Yoichi Hori, Fellow, IEEE, and Minh C. Ta, Senior Member, IEEE Abstract—In this paper, a detailed study on the Model Refer- ence Adaptive Controller (MRAC) utilizing the reactive power is presented for the online estimation of rotor resistance to maintain proper flux orientation in an Indirect Vector Controlled Induction Motor Drive. Selection of reactive power as the functional candi- date in the MRAC automatically makes the system immune to the variation of stator resistance. Moreover, the unique formation of the MRAC with the instantaneous and steady-state reactive power completely eliminates the requirement of any flux estimation in the process of computation. Thus, the method is less sensitive to integrator-related problems like drift and saturation (requiring no integration). This also makes the estimation at or near zero speed quite accurate. Adding flux estimators to the MRAC, a speed sensorless scheme is developed. Simulation and experimen- tal results have been presented to confirm the effectiveness of the technique. Index Terms—Drift and saturation, indirect vector control, model reference adaptive controller (MRAC), reactive power. LIST OF SYMBOLS ν ds d component of the stator voltage vector. ν qs q component of the stator voltage vector. I ds d component of the stator current vector. I qs q component of the stator current vector. Ψ dr d component of the rotor flux vector. ψ qr q component of the rotor flux vector. L s Stator inductance. L r Rotor inductance. L sl Stator leakage inductance. L rl Rotor leakage inductance. L m Mutual inductance. R s Resistance of a stator phase winding. R r Resistance of a rotor phase winding. T ′ s = σT s , transient time constant of the machine. Manuscript received April 5, 2006; revised September 27, 2007. S. Maiti and C. Chakraborty are with the Department of Electrical Engineer- ing, Indian Institute of Technology, Kharagpur 721302, India (e-mail: suman@ ee.iitkgp.ernet.in; sumanmaiti@rediffmail.com; chakraborty@ieee.org). Y. Hori is with the Department of Electrical Engineering, Institute of Indus- trial Science, University of Tokyo, Tokyo 153-8505, Japan (e-mail: hori@iis. u-tokyo.ac.jp; y.hori@ieee.org). M. C. Ta is with the Department of Industrial Automation, Hanoi University of Technology, Hanoi, Vietnam (e-mail: tacaominh@ieee.org). Digital Object Identifier 10.1109/TIE.2007.911952 T s Stator time constant. T r Rotor time constant. σ =1 − L 2 m /(L s L r ), total leakage factor. ω r Rotor electrical angular velocity. I. I NTRODUCTION T HE indirect field oriented (IFO)-controlled induction motor (IM) drive is widely used in high performance industry applications [1], [2] due to its simplicity and fast dy- namic response. However, feedforward adjustment of the slip- frequency, which requires rotor resistance, makes this scheme dependent on machine parameters. Of all the parameters, the rotor resistance undergoes considerable variation and if care is not taken to compensate for the change, the flux orientation is lost, resulting in coupling between the d- and q-axes variables. As is well known, the coupling makes the performance of the drive system sluggish. Attention is focused to enforce field ori- entation through online estimation of the machine parameters [3]–[6]. Many online parameter estimation schemes are available in literature [7]–[20]. They are broadly classified as follows. A. Signal Injection-Based Method This class of parameter estimation technique involving signal injection is proposed in [7] and [8]. The main drawbacks of this method are the adverse effect of injecting signal on motor dynamics and the requirement of extra hardware for signal injection. B. Observer-Based Method This class of parameter identification technique is mainly comprised of Extended Kalman Filter (EKF), Extended Luenberger Observer (ELO), and adaptive observer. Here, the rotor time constant can be treated as additional state variable along with rotor speed. So, for joint speed and parameter esti- mation [9] these methods are efficient. However, the problems related to EKF or ELO are the large memory requirement, computational intricacy, and the constraint such as treating all inductances to be constant in the machine model. 0278-0046/$25.00 © 2008 IEEE