CONTROL SYSTEM SYNTHESIS WITH SELF-ADAPTING REGULATOR BY INTERACTIVE ADAPTATION METHOD

Authors

Isamiddin Xakimovich Siddikov, Tashkent State Technical University, Address: 2 Universitetskaya st., 100095, Tashkent city, Republic of Uzbekistan, E-mail: isamiddin54@gmail.com, Phone: +998-90-359-52-68;Follow
Renata Nikolayevna Izmaylova, Tashkent State Technical University, Address: 2 Universitetskaya st., 100095, Tashkent city, Republic of Uzbekistan, E-mail: verona781r@gmail.com, Phone: +998-97-722-32-15.Follow

Abstract

The research results on the creation of a self-adapting control system for a nonlinear dynamic object based on the theory of intelligent control using interactive adaptation methods have been presented. The lack of an accurate description and the nonlinearity of the dynamic properties of the object under study, the presence of mutual influence of cross communication channels in the control circuits, as well as the influence of various types of uncertainties associated with the lack of a priori information about the process, significantly complicate the solution of the problem of synthesis of highly efficient control systems for dynamic objects, which necessitates the hybrid application of neural network methods and fuzzy technology involving methods of traditional automatic control theory. In order to solve these problems, it is proposed to use self-adapting neural network regulators capable of selecting the structure and adjusting the parameters of the regulators taking into account changes in both the properties of the object under study and external disturbing influences. The architecture of the proposed neural network has been presented in the form of a multilayer neural network. Training and correction of the parameters of the weight connections of the neural network have been implemented by the method of interactive adaptation, carried out implicitly. Self-adaptation of neuroregulator parameters was provided taking into account the rate of change of technological parameters of the control object. An adaptive fuzzy algorithm for the synthesis of self-adaptation of a control system for technological objects based on a dynamic model with robust properties has been developed.

First Page

51

Last Page

57

References

1. W.Xu.Xun, Newman Stephen T., “Making CNC machine tools more open, interoperable and intelligent – a review of the technologies”. In Computers in Industry. 2006, Vol.12, no 2, pp. 141-152.
2. O.Cordon, F.Herrera, F.Hoffmann, L.Magdalena, “Genetic Fuzzy Systems: Evolutionary Tuning and learning of Fuzzy Knowledge Bases”. World Scientific Publishing, 2001, pp. 217-346. https:// doi:10.1142/4177.
3. D.Pelusi, R.Mascella, “Optimal control algorithms for second order systems”. Journal of Computer Science. 2013, Vol. 9, no 2, pp. 183-197, https://doi.org/10.3844/jcssp.2013.183.197.
4. K.J.Astrom, “Revisiting the Ziegler-Nichols step response method for PID control”. Journal of Process Control. 2004, Vol. 4, pp. 635-650. https://doi:10.1016/j.jprocont.2004.01.002.
5. C.Lu, C.Hsu, C.Juang, “Coordinated control of flexible AC transmission system devices using an evolutionary fuzzy lead-lag controller with advanced continuous ant colony optimization”. IEEE Transactions on Power Systems. 2013, Vol. 28, No. 1, pp. 385-392. https://doi: 10.1109/TPWRS.2012.2206410.
6. E.H.Mamdani, S.Assilian, “An Experiment in Linguistic Synthesis with Fuzzy Logic Controller”. Int. J. Man-Machine Studies. 1975, Vol. 7, no 1, pp. 1-13. https://doi.org/10.1016/S0020-7373(75)80002-2.
7. L.P.Holmblad, J.J.Osregaard, “Control of Cement Kiln by Fuzzy Logic. In: Approximate Reasoning in Decision Analysis”. Eds. Amsterdam, New York, Oxford, 1982, pp. 389-400.
8. S.Hu, Z.Liang, W.Zhang, X.He, “Research on the integration of hybrid energy storage system and dual three-phase PMSM Drive in EV”. IEEE Transactions on Industrial Electronics. 2018, Vol. 65, no 8, pp. 6602-6611.
9. P.J.Macvicar-Whelan, “Fuzzy Sets for Man-Machine Interaction”. Int. J. Man- Mach. Studies. 1976, Vol. 8, pp. 687-697.
10. S.Zeng, H.Hu, L.Xu, G.Li, “Nonlinear Adaptive PID Control for Greenhouse Environment Based on RBF Network”. Sensors. 2012, Vol. 12, pp. 5328-5348, https:// doi: 10.3390/s120505328.
11. K.Passino, S.Yurkovich, “Fuzzy Control”. Addison-Wesley, NJ, 1998, pp. 324-502, https://www2.ece.ohio-state.edu/~passino/FCbook.pdf.
12. X.S.Yang, “Engineering Optimization: An Introduc tion with Metaheuristic Applications”. Hoboken: John Wiley & Sons, 2010, pp. 174-286, https://www.academia.edu/457294/Engineering_Optimization_An_Introduct ion_with_Metaheuristic_Applications.
13. D.Pelusi, “PID and intelligent controllers for optimal timing performances of industrial actuators”. International Journal of Simulation: Systems, Science and Technology. 2012, Vol. 13, no 2, pp. 65-71.
14. I.X.Siddikov, Z.Iskandarov, “Synthesis of adaptive-fuzzy control system of dynamic in conditions of uncertainty of information”. International Journal of Advanced Research in Science, Engineering and Technology, 2018, Vol. 5, Issue 1. pp. 5089-5093, https://doi.org/10.1007/978-3-030-68004-6_57.
15. K.J.Astrom, T.Hagglund, “Advanced PID control”. The Instrumentation, Systems, and Automation Society. 2006, pp. 214-348.
16. C.J.Wenk, Y.Bar-Shalom, “A multiple model of an adaptive dual control algorithm for stochastic systems with unknown parameters”. Automatic Control, IEEE Trans. 2003, Vol. 25, Iss. 4, pp. 703-710.
17. O’Dwyer, “Handbook of PI and PID Controller Tuning Rules”. 3 rd Edition, Dublin: Institute of Technology, Ireland, Imperial College Press, 2009, pp. 281-476.
18. T.Kim, I.Maruta, T.Sugie, “Robust PID controller tuning based on the constrained particle swarm optimization”. Automatica. 2008. Vol. 44, no 4, pp. 1104-1110.
19. X.Siddikov, U.O.Khujanazarov, R.N.Izmaylova, S.T.Yunusova, G.R.Alimova, “Design Of Standard Regulators For Multi-Link Control Objects”. Turkish Journal of Computer and Mathematics Education, 2021, Vol. 12, no 7, pp. 349-353. https://doi.org/10.17762/turcomat.v12i7.2588.
20. S.Das, I.Pan, K.Halder, A.Gupta, “LQR based improved discrete pid controller design via optimum selection of weighting matrices using fractional order integral performance index”. Applied Mathematical Modelling: Simulation and Computation for Engineering and Environmental Systems. 2013, Vol. 37, no 6, pp. 4253-4268.
21. X.Zhu, H.Zhang, D.Cao, Z.Fang, “Robust control of integrated motor-transmission powertrain system over controller area network for automotive applications”. Mechanical Systems and Signal Processing. 2014, Vol. 58, pp. 15-28.

Recommended Citation

Siddikov, Isamiddin Xakimovich and Izmaylova, Renata Nikolayevna (2021) "CONTROL SYSTEM SYNTHESIS WITH SELF-ADAPTING REGULATOR BY INTERACTIVE ADAPTATION METHOD," Chemical Technology, Control and Management: Vol. 2021: Iss. 5, Article 8.
DOI: https://doi.org/10.51346/tstu-02.21.5-77-0041