1. Science
  2. Publications
  3. Systems of Arms and Military Equipment
  4. 2(58)'2019
  5. Fuzzy control of the three-mass electromechanical system

Fuzzy control of the three-mass electromechanical system

G. Kaniuk, T. Vasilets, O. Varfolomiyev, O. Blyznychenko, O. Tolstorebrov
Annotations languages:


Description: The synthesis of the fuzzy control system of the electric drive of the lifting mechanism of the industrial plant on the basis of the Fuzzy regulator, which provides high-quality adjustment, taking into account the elastic properties of the lifting rope, is performed. As a result of the analysis of the dynamic characteristics of multi-mass electromechanical systems and taking into account the requirements for modern control systems, the prospect of the use of fuzzy approximating systems for controlling a three-mass electromechanical system of the lifting mechanism of an industrial plant. The mathematical model of the dynamics of the object of control of the system is developed, taking into account the elastic properties of the lifting rope in the form of a three-mass electromechanical system. It is shown that the transients in the three-mass system are unsatisfactory. To provide the desired dynamic characteristics of the three-mass system, fuzzy modeling technology is used, which is currently one of the most effective technologies for designing control systems. The structural scheme of a three-mass electromechanical system with a Fuzzy regulator is developed, which is the implementation of the fuzzy logic algorithm. Input and output linguistic variables of the regulator are defined. In the operating system environment of the MATLAB system using the Fuzzy Logic Toolbox application package, fuzzy system synthesis was performed. In SIMULINK mode, a schematic diagram of a control system with a Fuzzy controller is developed that includes a controlled object block and a Fuzzy Logic Controller block. Simulated fuzzy system with synthesized regulator is executed. Studies have shown that transient graphs in a system with a Fuzzy controller have high quality performance. Thus, the use of fuzzy control methods for multi-mass electromechanical systems makes it possible to use all the advantages provided by fuzzy controllers. The control algorithms used for these systems can be used to control systems with complex kinematic bonds in the absence of quantitative characteristics of all elements and bonds.


Keywords: fuzzy technologies, fuzzy inference systems, fuzzy systems, fuzzy control, three-mass electromechanical system, Fuzzy controller.

References

1. German, E.E. (2008), “Sovremennoe sostoyanie i perspektivyi razvitiya sistem nechetkogo upravleniya” [The current state and development prospects of fuzzy control systems], Bulletin of the National Technical University “KhPI”, No. 57, pp. 37-44.
2. German, E.E. and Klimenko, L.A. (2015), “Proektirovanie nechetkih modeley intellektualnyih promyishlennyih regulya-torov i sistem upravleniya” [Design of fuzzy models of intelligent industrial regulators and control systems], Information and Control Systems on the Railway Transport, No. 3, pp. 24-31.
3. Sharma, D. (2011), Designing and Modeling Fuzzy Control Systems, International Journal of Computer Applications, Vol. 16, No. 1, pp. 46-53. https://doi.org/10.5120/1973-2644.
4. Chopra, S., Mitra, R. and Kumar, V. (2005), Fuzzy Controller: Choosing an Appropriate and Smallest Rule Set, Interna-tional Journal of Computational Cognition, Vol. 3, No. 4, pp. 73-79.
5. Filo, G. (2010), Modelling of fuzzy logic control system using the MATLAB SIMULINK program, Technical Transac-tions, Vol. R. 107, z. 2-M, No. 8, pp. 73-81.
6. Klepikov, V.B., Banev, E.F. and Mehovich, S.A. (2010), “Energosberegayuschee fuzzy upravlenie elektroprivodom eskalatora metropolitena sistemyi TPN-AD” [Energy-saving fuzzy control of the electric drive of the metropolitan escalator of the TPN-AD system], Bulletin of the National Technical University “KhPI”, No. 28, pp. 579-582.
7. Cherevko, E.A. (2014), “Upravlenie elektroprivodom rolikov rolgangov TLS s ispolzovaniem fazzi-logiki” [Control of the electric drive of the rollers of the TLS roller tables using fuzzy logic], Bulletin of the Priazov State Technical University, No. 28, pp. 179-183.
8. Shchokin, V.P., Sushentsev, O.O. and Kolomits, G.V. (2009), “Intelektualna systema upravlinnia z nechitkym adaptyvnym emuliatorom” [Intelligent control system with fuzzy adaptive emulator], Automatics. Automation. Electrical Complexes and Systems, No. 1, pp. 177-181.
9. Stepanets, O.V. and Karakoy, A.V. (2016), “Rozrobka nechitkoho rehuliatora dlia zadachi zabezpechennia temperaturnoi skladovoi komfortnoho mikroklimatu” [Development of a fuzzy controller for the task of providing a temperature component of a comfortable microclimate], Technological Audit and Production Reserves, No. 1(2), pp. 50-55.
10. Fedin, S.S. (2016), “Modelirovanie fuzzy-sistemyi navedeniya raketyi na tsel” [Modeling of a fuzzy-missile guidance system on a target], Systems of Arms and Military Equipment, No. 1(45), pp. 190-195.
11. Priya, R. and Sherly, E. (2016), Design of an adaptive constrained based neuro-fuzzy controller for fault detection of a power plant system, Indian journal of computer Science and Engeneering, Vol. 7, No. 5, pp. 208-218.
12. Khaksar, M., Rezvani, A. and Moradi, M.H. (2016), Simulation of novel hybrid method to improve dynamic responses with PSS and UPFC by fuzzy logic controller, Neural Computing and Applications, Vol. 29, No. 3, pp. 837-85. https://doi.org/10.1007/s00521-016-2487-1.
13. Singhala, P., Shah, D.N. and Patel, B. (2014), Temperature Control using Fuzzy Logic, International Journal of In-strumentation and Control Systems (IJICS), Vol. 4, No. 1, pp. 1-10. https://doi.org/10.5121/ijics.2014.41011.
14. Saudagar, P.A., Dhote, D.S. and Chinchkhede, K.D. (2012), Design of Fuzzy Logic Controller for Humidity Control in Greenhouse, International Journal of Engineering Inventions, Vol. 1, No. 11, pp. 45-49.
15. Solanke, D.R., Chinchkhede, K.D. and Manwar, A.B. (2017), Design & Implementation of Fuzzy Inference System For Automatic Braking System, International Journal of Research in Science and Engineering, Vol. 6, No. 9, pp. 1242-1255.
16. Vichuzhanin, V. (2012), Realization of a fuzzy controller with fuzzy dynamic correction, Central European Journal of Engineering, No. 2(3), pp. 392-398. https://doi.org/10.2478/s13531-012-0003-7.
17. Herman, E.E., Lisachenko, I.G. and Bespalov, K.I. (2015), “Syntez systemy upravlinnia sushylnoiu ustanovkoiu z vykorystanniam nechitkoho kontrolera z samo nalashtuvanniam” [Synthesis of the control system of a drying installation using a fuzzy controller with the setting itself], Information Control Systems in the Railway Transport, No. 1, pp. 71-74.
18. Kharchenko, R.Yu. (2012), “Sravnitelnyiy analiz metodov aktivnoy adaptatsii PI-regulyatorov i nechetkih regulyatorov dlya sistem konditsionirovaniya i ventilyatsii (SKV) morskih sudov” [Comparative analysis of methods of active adaptation of PI-regulators and fuzzy regulators for air-conditioning and ventilation systems (VS) of sea vessels], Scientific Bulletin of the Kherson State Maritime Academy, No. 2(7), pp. 276-286.
19. Isaev, Ye.O. and Simanenkov, A.L. (2013), “Analiz system nechitkoho keruvannia sudnovymy elektro-enerhetychnymy kompleksamy na prykladi avtomatychnykh rehuliatoriv temperatury” [Analysis of systems of fuzzy control of ship electrical and energy complexes by the example of automatic temperature controllers], Scientific Bulletin of the Kherson State Maritime Academy, No. 2(9), pp. 35-40.
20. Almatheel, Y.A. and Abdelrahman, A. (2017), Speed control of DC motor using Fuzzy Logic Controller, International
Conferenceon Communication, Control, Computing and Electronics Engineering, pp. 586-594. https://doi.org/10.1109/ICCCCEE.2017.7867673.
21. Ramjug-Ballgobin, R., Sayed Hassen, S.Z. and Veerapen, S. (2015), Load frequency control of a nonlinear two-area power system, International Conference on Computing, pp. 54-55. https://doi.org/10.1109/CCCS.2015.7374172.
22. Chaudhary, H., Khatoon, S. and Singh, R. (2016), ANFIS based speed control of DC motor, Second International Inno-vative Applications of Computational Intelligence on Power, Energy and Controls with their Impact on Humanity, pp. 63-68. https://doi.org/10.1109/CIPECH.2016.7918738.
23. Carvajal, J., Chen, G. and Ogmen, H. (2000), Fuzzy PID controller: Design, performance evaluation, and stability analysis, Information Sciences, Vol. 123, pp. 249-270. https://doi.org/10.1016/S0020-0255(99)00127-9.
24. Tang, K.S., Man, K.F. and Chen, G. (2001), An optimal fuzzy PID controller, IEEE Transactions on Industrial Elec-tronics, Vol. 48, pp. 757-765. https://doi.org/10.1109/41.937407.
25. Xie, X. and Long, Z. (2015), Fuzzy PID Temperature Control System Design Based on Single Chip Microcomputer, In-ternational Journal of Online and Biometrical Engineering, Vol. 11, pp. 29-33. https://doi.org/10.3991/ijoe.v11i8.4881.
26. Jigang, H., Jie, W. and Hui, F. (2017), An anti-windup self-tuning fuzzy PID controller for speed control of brushless DC motor, Journal for Control, Measurement, Electronics, Computing and Communications, Vol. 58, pp. 321-336. https://doi.org/10.1080/00051144.2018.1423724.
27. Kim, J., Chang, P. and Jin, M. (2016), Fuzzy PID controller design using time-delay estimation, Transactions of the In-stitute of Measurement and Control, Vol. 39, pp. 1329-1338. https://doi.org/10.1177/0142331216634833.

Reference:
 Kaniuk, H.I., Vasylets, T.Yu., Varfolomiiev, O.O., Blyznychenko, O.M. and Tolstorebrov, O.T. (2019), “Nechitke upravlinnia trokhmasovoiu elektromekhanichnoiu systemoiu” [Fuzzy control of the three-mass electromechanical system], Systems of Arms and Military Equipment, No. 2(58), pp. 102-110. https://doi.org/10.30748/soivt.2019.58.13.