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Wednesday, 10 February 2016

Sensor less Speed Estimation and Vector Control of an Induction Motor drive Using Model Reference Adaptive Control

Now-a-days ,sensor less speed control modes of operation are becoming standard solutions in the area of electric drives. The technological developments require a compact and efficient drive to meet the challenging strategies in operation of the system. This paper provides a speed sensor less control of an Induction motor with a model based adaptive controller with stator current vectors. The purpose of the proposed control scheme is to create an algorithm that will make it possible to control induction motors without sensors. A closed loop estimation of the system with robustness against parameter variation is used for the control approach. A Model Reference Adaptive System (MRAS) is one of the major approaches used for adaptive control. The MRAS provides relatively easy implementation with a higher speed adaptation algorithm. MRAS proposed in this paper owing to its low complexity and less computational effort proposes a feasible methodology to control the speed of an Induction Motor (1M) drive without using speed sensors. Simulations results validate the effectiveness of this technique


                          1. Indirect Field oriented control,
                              2. induction motor drive
                              3. sensor less speed estimation,
                              4. Model Reference Adaptive control.



 Fig1. Proposed Block Diagram of MRAS based 1M drive using PI controller


Fig2 Overall Simulink model of sensor less control of induction motor using MRAS with PI controller.


Fig.3 MRAS speed response (w"r= 1500 rpm and no-load)  speed
Fig4MRAS response with step changes in reference

Fig. 5. MRAS speed response(w"r=1500 rpm and load of  3 Nm).


  Fig.6. MRAS speed response with a load of 3Nm

Fig7. Response of MRAS and Conventional Controller.


The model based control scheme is basically an adaptive control mechanism. The reference model of the proposed system consists of the response to be obtained for the input conditions. The adaptive mechanism continuously monitors the adaptable parameter (speed in this case). The adaptable parameter is continuously subjected to changes based on its deviation obtained by comparing it with the response of the reference model. The speed estimation algorithm in MRAS is computationally less intensive. MRAS is a relatively simple algorithm and hence less sophisticated processing can be employed. MRAS strategy is more robust than the conventional one. This makes it better suited if the drive is to be operated in hostile environments. Owing to less sophisticated processing requirements, MRAS technique costs cheaper and hence overall cost of the drive is reduced. With lower cost and greater reliability without mounting problems, the sensor less vector control schemes have made remarkable developments in electric drive technology. Due to lesser rise time taken by MRAS, this method gives faster steady state response and this scheme has better reliability than the conventional scheme.


[I] Teresa Orlowska - Kowalska and Mateusz Dybkowski , "Stator Current based MRAS estimator for a wide range speed Sensor less induction motor drives", IEEE Transactions on Industrial Electronics vo1.51, No. 4, April 2010, pp. 1296 - 1308.
[2] B. K. Bose, Power Electronics and Motor Drives, Pearson Education Inc., Delhi, India, 2003.
[3] M. Rodic and K. Jezernik, "Speed-sensor less sliding-mode torque control of induction motor," IEEE Transactions on Industrial  Electronics, vol. 49, no. I, pp. 87-95, February 2002.
[4] L. Harnefors, M. Jansson, R. Ottersten and K. Pietilainen, "Unified sensor less vector control of synchronous and induction motors," IEEE Transactions on Industrial Electronics, vol. 50, no. 1, pp. 153-160, February 2003.

[5] M. Comanescu and L. Xu, "An improved flux observer based on PLL frequency estimator for sensor less vector control of induction motors ," IEEE Transactions on Industrial Electronics, vol. 53, no.1, pp. 50-56, February 2006.