Synthesis by using the sliding-mode control theory for the fuzzy logic controller of the direct torque control system
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- Category: Power Supply Technologies
- Last Updated on 08 November 2018
- Published on 29 October 2018
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Authors:
M.A.Melnychuk, Lviv Polytechnic National University, Lviv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
А.О.Lozynskyi, Dr. Sc. (Tech.), Prof., orcid.org/0000-0003-1351-7183, Lviv Polytechnic National University, Lviv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
О.Yu.Lozynskyi, Dr. Sc. (Tech.), Prof., orcid.org/0000-0002-4943-8746, Lviv Polytechnic National University, Lviv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
А.S.Kutsyk, Dr. Sc. (Tech.), Prof., orcid.org/0000-0002-7832-609X, Lviv Polytechnic National University, Lviv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract:
Purpose. Improvement of the direct torque control algorithm and thereby increase in the efficiency of the electric drive on the basis of the squirrel cage induction motor.
Methodology. The use of the theory of sliding-mode control and hysteresis regulators allows considering the traditional direct torque control algorithm as a special case, which is obtained for certain values of the weight coefficients of deviation from the given regimes. Changing these coefficients leads to a change in the sector boundaries of the switching table. The analysis of system behavior on the basis of the established quality criterion gives the possibility to determine the necessary changes of sector boundaries for improving the characteristics of the drive and to synthesize a fuzzy controller that implements the sliding mode control with variable weight coefficients. The efficacy of the proposed control algorithm was investigated by use the modified MatLab model of the direct torque control and the necessary computer experiments were performed.
Findings. On the basis of the established criterion, the influences of the change in the switching table sector boundaries on the characteristics of the electric drive with direct torque control were analyzed. The fuzzy controller was synthesized based on the theory of sliding mode control. The behavior of the direct torque control system with fuzzy controller was investigated.
Originality. The scientific novelty of the proposed approach is in further development of the theory of fuzzy controllers synthesis based on the methods of classical control theory and improving the direct torque control algorithm at low speeds, as well as in the high speeds with load close to the nominal.
Practical value. The proposed structure of the fuzzy controller can be easily implemented in existing systems of direct torque control and will provide an improvement of the technical and economic performance of the electric drive.
References.
1. Casadei, D., Serra, G., Tani, A. and Zarri, L., 2006. Assessment of direct torque control for induction motor drives. Bulletin of the Polish Academy of Sciences (Technical Sciences), 54(3), pp. 237‒254. Available at: <http://bulletin.pan.pl/(54-3)237.pdf> [Accessed 07 August 2017].
2. Tole Sutikno, Nik Rumzi, Nik Idris and Auzan Jidin, 2014. A review of direct torque control of induction motors for sustainable reliability and energy efficient drives. Renewable and Sustainable Energy Reviews, 32, pp. 548–558.
3. C. M. F. S. Reza, Md. Didarul Islam and Saad Mekhilef, 2014. A review of reliable and energy efficient direct torque controlled induction motor drives. Renewable and Sustainable Energy Reviews, 37, pp. 919–932.
4. Sikorski, A. and Korzeniewski, M., 2013. Improved Algorithms of Direct Torque Control Method. AUTOMATIKA, 54(2), pp. 188–198.
5. Beerten, J., Verveckken, J. and Driesen, J., 2010. Predictive direct torque control for flux and torque ripple reduction. IEEE Transactions on Industrial Electronics. 57(1), Jan. 2009, pp. 404‒412. DOI: 10.1109/TIE.2009.2033487.
6. Lisauskas, S., Udris, D. and Uznys, D., 2013. Direct Torque Control of Induction Drive Using Fuzzy Controller. Elektronika Ir Elektrotechnika, 19(5), pp. 13‒16.
7. Bhoopendra Singh, Shailendra Jain and Sanjeet Dwivedi. 2012. Direct Torque Control Induction Motor Drive with Improved Flux Response. Advances in Power Electronics, Volume 2012, Article ID 764038, 11 pages.
8. Mei, C. G., Panda, S. K., Xu, J. X. and Lim, K. W., 2002. Direct Torque of Induction Motor-Variable Switching Sectors [online]. DOI: 10.1109/PEDS.1999. 794540.
9. Ji-Su Ryu, In-Sic Yoon, Kee-Sang Lee and Soon-Chan Hong, 2001. Direct torque control of induction motors using fuzzy variable switching sector. DOI: 10.1109/ISIE.2001.931589.
10. Orlowska-Kowalska, T., Tarchala, G. and Dybkowski, M., 2014. Sliding-mode direct torque control and sliding-mode observer with a magnetizing reactance estimator for the field-weakening of the induction motor drive. Mathematics and Computers in Simulation, 98, pp. 31‒45.
11. Sampath Kumar, S., Joseph Xavier, R. and Balamurugan, S., 2016. Speed Control DTC with Torque Ripple and Flux Droop Reduction Using Sector Alteration Based Adaptive Sliding Mode Control. Asian Journal of Information Technology, 15(20), pp. 4020‒4029.
12. Lozynskyy, А. О. and Melnychuk, M. A., 2016. Direct Torque Control Based On The Theory Of Sliding Mode Control Synthesis Algorithm. Scientific and Technical Journal Electrotechnic and Computer Systems, 21(97), pp. 29‒35.
13. Sarhan, H., Issa, R. and Alia, M., 2012. Optimal Power Factor Control of Three-Phase Induction Motor Drives Using PIC-Microcontroller. International Review of Automatic Control, 5(3), pp. 349‒353. Available at:<https://www.researchgate.net/publication/ 290535208_Optimal_power_factor_control_of_three-phase_induction_motor_drives_using_PIC-microcontroller> [Accessed 15 September 2017].
14. Lozynskyy, A. and Demkiv, L., 2014. Synthesis of Multicriteria Controller by Means of Fuzzy Logic Approach. Advances in Fuzzy Systems, 2014. DOI: 10.1155/2014/758207.
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