•  
  •  
 

Abstract

This paper examines the development of regularized algorithms for synthesizing control devices in control systems for polynomial objects, described by multidimensional Volterra functional series. The synthesis problem is solved using a two-stage procedure. In the first stage, the optimization problem is initially solved for an open-loop system. The second stage involves determining the parameters of the control device, i.e., its impulse response functions, by using the relationship between the characteristics of the open-loop and closed-loop systems. Regular algorithms are presented for finding the impulse response functions of the control device based on methods for regularizing the solution of operator equations with positive-definite matrices and an approximately defined right-hand side. The presented regularized algorithms for synthesizing control devices in systems for polynomial objects can be easily implemented using modern computing tools. Furthermore, the concepts and methods of the regularization approach prove highly effective for increasing the accuracy of the control device synthesis procedure.

First Page

60

Last Page

68

References

  1. Nelles, O. (2020). Nonlinear Dynamic System Identification. Nonlinear System Identification. Springer, Cham. 715–830. https://doi.org/10.1007/978-3-030-47439-3_18
  2. Makarychev, P. (2020). Structural and Parametric Identification of Nonlinear Dynamic Objects. 2020 Moscow Workshop on Electronic and Networking Technologies (MWENT), 1–4. https://doi.org/10.1109/mwent47943.2020.9067500
  3. Igamberdiyev, H.Z. (2024). Theory of Automatic Control. Tashkent: Zuxro baraka biznes. 680 p.
  4. Mamirov, U.F. (2021). Regular synthesis of systems for adaptive control of uncertain dynamic objects. Tashkent: Bilim va intellektual salohiyat. 215 p.
  5. Ling, W.K. (2007). Nonlinear digital filters: analysis and applications. Academic Press. 216 p.
  6. Boguslavsky, I.A. (2006). Polynomial approximation for nonlinear problems of evaluation and control. Fizmatlit. 371 p.
  7. Gayduk, A.R. (2012). Theory and Methods of Analytical Synthesis of Automatic Control Systems (Polynomial Approach). Moscow: Fizmatlit.
  8. Wang, Y., Tang, S., Gu, X. (2021). Parameter estimation for nonlinear Volterra systems by using the multi-innovation identification theory and tensor decomposition. Journal of the Franklin Institute, 359, 1782–1802. https://doi.org/10.1016/j.jfranklin.2021.11.015
  9. Fujii, K., Nakao, K. (1969). Identification and Modeling of Nonlinear Dynamical Systems Using Volterra Functional Series. Journal of the Society of Instrument and Control Engineers, 5, 368–377. https://doi.org/10.9746/sicetr1965.5.368
  10. Mathews, V.J., Sicuranza, G.L. (2000). Polynomial signal processing. Wiley-Interscience. 452 p.
  11. Abdurakhmanov, I.Yu., Zaripov, O.O., Igamberdiyev, H.Z. (2015). Regularization algorithms of synthesis actuation devices in control systems of polynomial objects. International Journal of Emerging Technology and Advanced Engineering, 5(7), 457–461.
  12. Tikhonov, A., Leonov, A., Yagola, A. (1997). Nonlinear Ill-Posed Problems. https://doi.org/10.1007/978-94-017-5167-4
  13. Aghayeva, G., Juraev, D. (2025). Application of ill-posed problems in mathematical modeling, data analysis, and business mathematics. IETI Transactions on Data Analysis and Forecasting, 3(2). https://doi.org/10.3991/itdaf.v3i2.56443
  14. Srinivasamurthy, S. (2012). Methods of solving ill-posed problems. arXiv: Numerical Analysis. https://doi.org/10.1007/0-387-23218-4_2
  15. Bakushinsky, A., Kokurin, M. (2018). Regularization algorithms for ill-posed problems. https://doi.org/10.1515/9783110557350
  16. Benning, M., Burger, M. (2018). Modern regularization methods for inverse problems. Acta Numerica, 27, 1–111. https://doi.org/10.1017/s0962492918000016
  17. Igamberdiyev, X.Z., Sevinov, J.U., Zaripov, O.O. (2014). Regularnye metody i algoritmy sinteza adaptivnykh system upravleniya s nastraivaemymi modelyami [egular methods and algorithms for the synthesis of adaptive control systems with adjustable (tunable) models]. Tashkent: TashGTU. 160 p. (in Russian)
  18. Igamberdiev, Kh.Z., Yusupbekov, A.N., Zaripov, O.O. (2012). Regular methods of assessing and managing dynamic objects under conditions of uncertainty. Tashkent: Tashkent State Technical University. 320 p. (in Russian).

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.