•  
  •  
 

Technical science and innovation

Abstract

One of the ways to increase the efficiency of the process of managing continuous dynamic objects is to develop new or improve existing control systems based on modern methods involving the achievements of information technology. The article deals with the creation of highly efficient control algorithms for technological objects, operating in conditions of uncertainty, designed to manage real-life objects. An algorithm is proposed for the structural-parametric adaptation of the PID parameters (proportional-integral-differential) -regulator, which allows to reduce the number of iterations in the learning process of the fuzzy-logical inference algorithm by reducing empty solutions. To determine the empty solutions, hybrid algorithms are used, which include modernized genetic and immune algorithms, which in turn allow you to configure the adaptation parameters of artificial neural network models. A block diagram of an automated control system for executive mechanisms is proposed, which includes a block for adapting the correction of not only parameters, but also the structure of the control system, which allows to reduce the error in the results of training a neuro-fuzzy network from 8 to 1%. The proposed algorithm is simple to implement on microcontrollers, which allows it to be implemented in the tasks of process control in the conditions of information uncertainty in real conditions at the operation stage.

First Page

154

Last Page

160

References

1. Bobir M.V. Modifitsirovannii algoritm nechetko-logicheskogo vivoda v zadachax upravleniya oborudovaniem s CHPU // Mexatronika , Avtomatizatsiya, Upravlenie. 2001. №4. S.26-32.

2. Bobir M.V., Titov V.S., Ansiferov A.V. Algoritm visokoskorostnoy obrabotki detaley na osnove nechetkoy logiki // Mexatronika, Avtomatizatsiya, Upravlenie, 2012. №6. S.21-26.

3. Otsenka dostovernosti pri modelirovanii nechetko-logicheskix sistem / Bobir M.V., Titov V.S., P.V. Globin, N.A. Milostnaya // Promishlennie ASU i kontrolleri, 2012. №7. S. 32-38.

4. Bobir M.V. Metodi postroeniya funksiy prinadlejnostey dlya nechetkix baz znaniy // Promishlennie ASU i kotrolleri, 2011. №2. S.27-33.

5. Bobir M.V., Titov V.S., CHervyakov L.M. Adaptatsiya slojnix sistem upravleniya s uchetom prognozirovaniya vozmojnix sostoyaniy // Avtomatizatsiya i sovremennie texnologii, 2012. №5. S.3-10.

6. Pupkov K.A., Barkin A.I., Voronov E.M. i dr.; Pod red. N.D. Egupova. Metodi klassicheskoy i sovremennoy teorii avtomaticheskogo upravleniya: Uchebnik dlya vuzov. V 3 t. -Moskva: Izd-vo MGTU im. N.E. Baumana, 2000.

7. Rutkovskaya D., Pilinskiy M., Rutkovskiy L. Neyronnie seti, geneticheskie algoritmi i nechetkie sistemi- M.: Goryachaya liniya-Telekom, 2006.- 452 s.

8. Pegat A. Nechetkoe modelirovanie i upravlenie.- M.: IUIT; BINOM, Laboratoriya znaniy, 2012. -798 s.

9. Antipin A.F. Sravnitelniy analiz bistrodeystviya diskretno-logicheskogo regulyatora // Programmnie produkti i sistemi. 2010. № 1 (89). S. 75–77.

10. Yurkevich V.D. Sintez nelineynix nestatsionarnix sistem upravleniya s raznotempovimi protsessami. SPb.: Nauka, 2000. 287 c.

11. Siddikov I., Iskandarov Z. Synthesis of adaptive-fuzzy control system of dynamic in conditions of uncertainty of information // International Journal of Advanced Research in Science, Engineering and Technology. Vol. 5. Issue 1. January 2018. pp. 5089-5093.

12. Zadeh, L.. Fuzzy Sets. Inf Cont, Vol. 8, Pp. 338–353, 1965.

13. Cox, E. , The Fuzzy Systems Handbook. Ap Professional - New York.1994.

14. Mehrotra, K., Mohan, C. K., And Ranka, S. ,Elements Of Artificial Neural Networks. The Mit Press, 1997

15. Lin, C-T., and C. S. George Lee. «Neural-network-based fuzzy logic control and decision system» Computers, IEEE Transactions on 40.12 (1991): 1320-1336.

16. Takagi, H. & Hayashi, I., Nn-Driven Fuzzy Reasoning. International Journal Of Approximate Reasoning, Vol. 5,Issue 3, 1991.

17. Rotach, V.Ya. (2008) The Theory of Automatic Control. Moscow: Publishing House Moscow Power Engineering Institute, - 396 p.

18. Pelusi, D., Mascella R. (2013) Optimal control algorithms for second order systems, Journal of Computer Science, vol. 9, no. 2, pp. 183–197.

19. Astrom, K.J. (2006) Advanced PID control/ K. J. Astrom, T. Hagglund – ISA (The instrumentation, Systems, and Automation Society), 2006 – 460 p.

20. Wang, L. And Mendel, J., Back-Propagation Fuzzy System As Nonlinear Dynamic System Identifiers. Proceedings Of Ieee International Conferenceon Fuzzy Systems, Pages 1409–1416, 1992.

Included in

Engineering Commons

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.