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Monday, 18 November 2019

Implementation of a New MRAS Speed Sensorless Vector Control of Induction Machin



ABSTRACT:
In this paper, a novel rotor speed estimation method using model reference adaptive system (MRAS) is proposed to improve the performance of a sensorless vector control in the very low and zero speed regions. In the classical MRAS method, the rotor flux of the adaptive model is compared with that of the reference model. The rotor speed is estimated from the fluxes difference of the two models using adequate adaptive mechanism. However, the performance of this technique at low speed remains uncertain and the MRAS loses its efficiency, but in the new MRAS method, two differences are used at the same time. The first is between rotor fluxes and the second between electromagnetic torques. The adaptive mechanism used in this new structure contains two parallel loops having Proportional-integral controller and low-pass filter. The first and the second loops are used to adjust the rotor flux and electromagnetic torque. To ensure good performance, a robust vector control using sliding mode control is proposed. The controllers are designed using the Lyapunov approach. Simulation and experimental results show the effectiveness of the proposed speed estimation method at low and zero speed regions, and good robustness with respect to parameter variations, measurement errors, and noise is obtained.
KEYWORDS:

1.      Induction motor
2.      Lyapunov function
3.      Model reference
4.      Adaptive system (MRAS)
5.      Sensorless control
6.      Speed estimation
7.      Vector control

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:




Fig. 1. Block diagram of the new MRAS observer.

EXPECTED SIMULATION RESULTS:


Fig. 2. Speed estimation error.



Fig. 3. Speed tracking error.


Fig. 4. Rotor flux.


Fig. 5. Load torque and Rs variations.


Fig. 6. Classical MRAS observer: Reference, actual, and estimated speed
for load torque and Rs variations.



Fig. 7. Classical MRAS observer: Zoom of Reference, actual, and estimated
speed for load torque and Rs variations.


Fig. 8. Speed of induction motor.


Fig. 9. Speed zoom.


Fig. 10. Speed estimation error.




Fig. 11. Speed tracking error.

 CONCLUSION:
In this paper, a new MRAS rotor speed observer was proposed to improve the performance of sensorless vector controller of induction machine. The control robustness is achieved by a sliding-mode controller and its stability is proved using a Lyapunov approach. Simulation and experimental results for different speed profiles had shown, on the one hand, that the proposed new MRAS observer was able to estimate accurately the actual speed at low and zero speed when the conventional MRAS observer is limited. On the other hand, the robustness of the proposed observer regarding load torque and stator resistance variations, especially at low and zero speed, is much better than the classical observer.
REFERENCES:
[1] J. W. Finch and D. Giaouris, “Controlled AC electrical drives,” IEEE Trans. Ind. Electron., vol. 55, no. 2, pp. 481–491, Feb. 2008.
[2] J.-I. Ha and S.-K. Sul, “Sensorless field-orientation control of an induction machine by high-frequency signal injection,” IEEE Trans. Ind. Appl., vol. 35, no. 1, pp. 45–51, Jan./Feb. 1999.
[3] C. Caruana, G.M. Asher, and M. Sumner, “Performance of high frequency signal injection techniques for zero-low-frequency vector control induction machines under sensorless conditions,” IEEE Trans. Ind. Electron., vol. 53, no. 1, pp. 225–238, Feb. 2006.
[4] F. Peng and T. Fukao, “Robust speed identification for speed-sensorless vector control of induction motors,” IEEE Trans. Ind. Appl., vol. 30, no. 5, pp. 1234–1240, Sep./Oct. 1994.
[5] C. Schauder, “Adaptive speed identification for vector control of induction motors without rotational transducers,” IEEE Trans. Ind. Appl., vol. 28, no. 5, pp. 1054–1061, Sep./Oct. 1992.