Simulation of MRAS-based Speed Sensorless Estimation of Induction Motor Drives using MATLAB/SIMULINK

ABSTRACT
Model Reference Adaptive System (MRAS) based techniques are one of the best methods to estimate the rotor speed due to its performance and straightforward stability approach. These techniques use two different models (the reference model and the adjustable model) which have made the speed estimation a reliable scheme especially when the motor parameters are poorly known or having large variations. The scheme uses the error vector from the comparison of both models as the feedback for speed estimation. Depending on the type of tuning signal driving the adaptation mechanism, there could be a number of schemes available such as rotor flux based MRAS, back e.m.f based MRAS, reactive power based MRAS and artificial neural network based MRAS. All these schemes have their own trends and tradeoffs. In this paper, the performance of the rotor flux based MRAS (RF-MRAS) and back e.m.f based MRAS (BEMFMRAS) for estimating the rotor speed was studied. Both schemes use the stator equation and rotor equation as the reference model and the adjustable model respectively. The output error from both models is tuned using a PI controller yielding the estimated rotor speed. The dynamic response of the RF-MRAS and BEMF-MRAS sensorless speed estimation is examined in order to evaluate the performance of each scheme.

KEYWORDS
1. BEMF-MRAS
2. MRAS
3. Parameter Variations
4. RFMRAS
5. Sensorless Speed
6. Tracking Capability.

SOFTWARE: MATLAB/SIMULINK
BLOCK DIAGRAM

Fig. 1. Basic configuration of MRAS-based speed sensorless estimation scheme.

Fig. 2. Block diagram of RF-MRAS scheme.

Fig. 3. Block diagram of BEMF-MRAS scheme.

SIMULATION RESULTS

Fig. 4. RF-MRAS estimator’s tracking performance at reference speed (a) 100rad/s, (b) 70rad/s and (c) 50rad/s (d)
30rad/s.

Fig. 5. Effect of incorrect setting of RS values to the RF-MRAS estimator’s speed response. (a) Rs (b) Rsnew = 1.1
Rs (C) Rsnew = 1.5 Rs (d) Rsnew = 2 RS.

Fig. 6. BEMF-MRAS estimator’s tracking performance at reference speed (a) 100rad/s, (b) 70rad/s and (c) 50rad/s
(d) 30rad/s.

Fig. 7. Effect of incorrect setting of Rs values to the BEMF-MRAS estimator’s speed response. (a) Rs (b) Rs,ew =
1.1 Rs (c) Rs,ew = 1.5 Rs (d) Rs,ew = 2 Rs.

CONCLUSION
Performance of RF-MRAS and BEMF-MRAS estimators based on the tracking capability and parameter sensitivity was presented. The result shows that the BEMFMRAS estimator is more superior to the RF-MRAS estimator at that particular defined range of reference speeds. This is prior to the elimination of pure integrators used in the RF-MRAS scheme. However, the BEMFMRAS estimator is more difficult to design due to the non-linear effect of the adaptation gain constants. Therefore, as a whole, considering all the key criteria of comparison, it can be concluded that the BEMF-MRAS scheme embrace the requirement as a versatile estimator. It demonstrate good tracking capability and superb in insensitivity to parameter variations.
REFERENCES
[1] M. Ta-Cao, Y. Hori and T. Uchida, “MRAS-based speed sensorless control for induction motor drives using instantaneous reactive power”, IEEE-IES Conference Record, pp. 1717-1422. 2001.
[2] S. Tamai, H. Sugimoto, M. Yano, “Speed-sensorless vector control of induction motor with model reference adaptive system”, Conf. Record of the 1985 IEEE-IAS Annual Meeting, pp. 613-620, 1985.
[3] C. Shauder, “Adaptive speed identification for vector control of induction motor without rotational transducers”, IEEE Trans. Ind. Application, Vol. 28, No. 5, pp. 1054-1061, Sept./Oct. 1992.
[4] Y.P. Landau, “Adaptive Control: The model reference approach”, Marcel Dekker, New York, 1979.
[5] M.N. Marwali, A. Kehyani, “A comparative study of rotor flux based MRAS and back e.m.f based MRAS speed estimators for speed sensorless vector control of induction machine”, IEEEIAS Annual Meeting, New Orleans, Louisiana, pp. 160- 166, 1997.