Linking the Researchers, Developing the Innovations Manuscripts submittal opens till 10 May 2024. Please submit your papers at or

  • Volume 2018

    Research in Sensorless Vector Control of Induction Motor based on MRAS Technique
    (International Journal of Engineering Works)

    Vol. 5, Issue 2, PP. 21-31, February 2018
    Keywords: motor; speed sensorless, model reference adaptive system, SVPWM, vector control

    Download PDF


    Model Reference Adaptive System (MRAS) represents one of the most attractive and popular solutions for sensorless control of AC drive. According to the principle of asynchronous motor vector control,taking two phase rotating coordinates current model as the adjustable model and improved voltage model as reference model,a speed sensorless vector control system is built The model reference adaptive system (MRAS) method is used to identify system speed .Model reference adaptive system method is applied to asynchronous motor speed estimation and achieves speed sensorless control of asynchronous motor. The approach is implemented on Matlab /Simulink software.The simulation results show that the system has good control performance and accuracy. It proves the feasibility and practicability of the system.


    1. Kader Ali Ibrahim: Engineering Research Center of Internet of Things Technology Applications Ministry of Education,Wuxi 214122, China.
    2. Shen Yan-Xoa: Engineering Research Center of Internet of Things Technology Applications Ministry of Education,Wuxi 214122, China.
    3. Hoch Omar Hoche: Engineering Research Center of Internet of Things Technology Applications Ministry of Education,Wuxi 214122, China

    Full Text


    Kader Ali Ibrahim, Shen Yan-Xia, Hoch Omar Hoche "Research in Sensorless Vector Control of Induction Motor based on MRAS Technique" International Journal of Engineering Works, Vol. 5, Issue 2, PP. 21-31, February 2018.


    1. [1]     Zerdali E, Barut M (2016) Novel version of bi input-extended Kalman filter for speed-sensorless control of induction motors with estimations of rotor and stator resistances, load torque, and inertia. Turk J Electr Eng Comput Sci 24 (5):4525-4544. doi:10.3906/elk-1408-136
    2. [2]     Usta MA, Okumus HI, Kahveci H (2017) A simplified three-level SVM-DTC induction motor drive with speed and stator resistance estimation based on extended Kalman filter. Electr Eng 99 (2):707-720. doi:10.1007/s00202-016-0442-x
    3. [3]     Kung YS, Thanh NP, Wang MS (2015) Design and simulation of a sensorless permanent magnet synchronous motor drive with microprocessor-based PI controller and dedicated hardware EKF estimator. Appl Math Model 39 (19):5816-5827. doi:10.1016/j.apm.2015.02.034
    4. [4]     Inan R, Barut M (2014) Bi input-extended Kalman filter-based speed-sensorless control of an induction machine capable of working in the field-weakening region. Turk J Electr Eng Comput Sci 22 (3):588-604. doi:10.3906/elk-1208-31
    5. [5]     Venkadesan A, Himavathi S, Muthuramalingam A (2013) Performance comparison of neural architectures for on-line flux estimation in sensor-less vector-controlled IM drives. Neural Comput Appl 22 (7-8):1735-1744. doi:10.1007/s00521-012-1107-y
    6. [6]     Gutierrez-Villalobos JM, Rodriguez-Resendiz J, Rivas-Araiza EA, Martinez-Hernandez MA (2015) Sensorless FOC Performance Improved with On-Line Speed and Rotor Resistance Estimator Based on an Artificial Neural Network for an Induction Motor Drive. Sensors 15 (7):15311-15325. doi:10.3390/s150715311
    7. [7]     Maiti S, Verma V, Chakraborty C, Hori Y (2012) An Adaptive Speed Sensorless Induction Motor Drive With Artificial Neural Network for Stability Enhancement. IEEE Trans Ind Inform 8 (4):757-766. doi:10.1109/tii.2012.2210229
    8. [8]     Mouna B, Aicha A, Lassaad S (2011) Neural Network Speed Sensor less Direct Vector Control of Induction Motor Using Fuzzy Logic in Speed Control Loop. Int Rev Electr Eng-IREE 6 (5):2237-2246
    9. [9]     Mishra RN, Mohanty KB (2017) Implementation of feedback-linearization-modelled induction motor drive through an adaptive simplified neuro-fuzzy approach. Sadhana-Acad Proc Eng Sci 42 (12):2113-2135. doi:10.1007/s12046-017-0741-6
    10. [10]  Kilic E, Ozcalik HR, Yilmaz S (2016) Efficient speed control of induction motor using RBF based model reference adaptive control method. Automatika 57 (3):714-723. doi:10.7305/automatika.2017.02.1330
    11. [11]  Wang SY, Tseng CL, Chiu CJ (2015) Design of a novel adaptive TSK-fuzzy speed controller for use in direct torque control induction motor drives. Appl Soft Comput 31:396-404. doi:10.1016/j.asoc.2015.03.008
    12. [12]  Zbede YB, Gadoue SM, Atkinson DJ (2016) Model Predictive MRAS Estimator for Sensorless Induction Motor Drives. IEEE Trans Ind Electron 63 (6):3511-3521. doi:10.1109/tie.2016.2521721
    13. [13]  Holakooie MH, Taheri A, Sharifian MBB (2015) MRAS Based Speed Estimator for Sensorless Vector Control of a Linear Induction Motor with Improved Adaptation Mechanisms. J Power Electron 15 (5):1274-1285. doi:10.6113/jpe.2015.15.5.1274
    14. [14]  Brandstetter P, Dobrovsky M, Kuchar M, Dong CST, Vo HH (2017) Application of BEMF-MRAS with Kalman filter in sensorless control of induction motor drive. Electr Eng 99 (4):1151-1160. doi:10.1007/s00202-017-0613-4
    15. [15]  Kumar R, Das S, Syam P, Chattopadhyay AK (2015) Review on model reference adaptive system for sensorless vector control of induction motor drives. IET Electr Power Appl 9 (7):496-511. doi:10.1049/iet-epa.2014.0220
    16. [16]  Dehghan-Azad E, Gadoue S, Atkinson D, Slater H, Barrass P, Blaabjerg F (2018) Sensorless Control of IM Based on Stator-Voltage MRAS for Limp-Home EV Applications. IEEE Trans Power Electron 33 (3):1911-1921. doi:10.1109/tpel.2017.2695259
    17. [17]  Kumar R, Das S (2017) MRAS-based speed estimation of grid-connected doubly fed induction machine drive. IET Power Electron 10 (7):13. doi:10.1049/iet-pel.2016.0768