267

Algorithms for increasing the roughness of the procedure for assessing the state vector of control objects to the influence of uncertainty factors are given. Expressions are obtained for extended state vectors and observations. Stable inversion algorithms are given for a nondegenerate block matrix with the allocation of its left and right zero divisors of maximum rank. The presented stable computational procedures allow us to regularize the problem of synthesis of algorithms for estimating the parameters of regulators in adaptive control systems with a customizable model and to improve the quality indicators of control processes under conditions of parametric uncertainty.

  • Web Address
  • DOI
  • Date of creation in the UzSCI system22-02-2021
  • Read count265
  • Date of publication25-10-2020
  • Main LanguageIngliz
  • Pages187-191
English

Algorithms for increasing the roughness of the procedure for assessing the state vector of control objects to the influence of uncertainty factors are given. Expressions are obtained for extended state vectors and observations. Stable inversion algorithms are given for a nondegenerate block matrix with the allocation of its left and right zero divisors of maximum rank. The presented stable computational procedures allow us to regularize the problem of synthesis of algorithms for estimating the parameters of regulators in adaptive control systems with a customizable model and to improve the quality indicators of control processes under conditions of parametric uncertainty.

№ Name of reference
1 1. Kolos M.V., Kolos I.V. Metodi optimal'noy lineynoy fil'trasii. –M.: Izd-vo MGU, 2000. – 102 s.
2 2. Schmidt G.T. “Linear and nonlinear filtering techniques,” Control and Dynamic Systems. In: Leondes, C.T. (ed.). P. 63–98. Vol. 12. Academic Press.- New York (1976).
3 3. Peltsverger S.B. Algorithmic support of assessment processes in dynamic systems under uncertainty. -M.: Science, 2004. - 116 p.
4 4. Kim D.P. Teoriya avtomaticheskogo upravleniya. T.2. Mnogomernie, nelineynie, optimal'nie i adaptivnie sistemi – 2-e izd., –M.: Fizmatlit, 2016. –440 s.
5 5. Zaripov O.O., Akhmedov D.A., Mamirov U.F. “Adaptive estimation algorithms for the state of nonlinear dynamic systems,” International Journal of Psychosocial Rehabilitation, Vol. 24, Iss. 3, 2020, –pp. 247-253. DOI: 10.37200/IJPR/V24I3/PR200776
6 6. Handbook on the theory of automatic control, Ed. A.A. Krasovsky. –M.: Nauka, 1987. - 712 p.
7 7. Mamirov, Uktam Farkhodovich (2019) “Sustainable Algorithms For Synthesis Of Regulators In Adaptive Control Systems Of Parametrically Uncertain Objects,” Chemical Technology, Control and Management: Vol. 2019: Iss. 4, Article 8. https://uzjournals.edu.uz/ijctcm/vol2019/iss4/8, -pp. 126-132.
8 8. Vorob'ev N.V., Remizova O.A., Sirokvashin V.V., Fokin A.L. “Uvelichenie grubosti k parametricheskoy neopredelennosti pri reshenii zadachi fil'trasii v robastnix sistemax,” Izvestiya Sankt-Peterburgskogo gosudarstvennogo texnologicheskogo instituta (texnicheskogo universiteta) 2012. №. 14. S. 93-96.
9 9. Lawson Ch., Henson R., Numerical solution of problems in the method of least squares, 1986. -232 p.
10 10. Zhdanov A.I. Introduction to methods for solving ill-posed problems: -The publishing of the Samara state. Aerospace University, 2006. -87 p.
11 11. Yusupbekov N.R., Igamberdiev H.Z., Mamirov U.F. “Algorithms of sustainable estimation of unknown input signals in control systems,” Journal of Multiple-Valued Logic and Soft Computing. Vol. 33. Issue 1-2, 2019. –P. 1-10.
12 12. Mamirov U.F. “Algorithms of stable control of a matrix object in the conditions of parametric uncertainty,” Chemical Technology. Control and Management. 2018. №. 1. P. 164-168.
13 13. Golub J., Van Louch Ch. Matrix calculations: Per. With the English. -M.: World, 1999. -548 p.
14 14. Demmel J. Computational linear algebra. Theory and applications, 2001. - 430 p.
15 15. Horn R., Johnson C. Matrix analysis, 1989. -655 p.
16 16. Bernstein D.S. Matrix mathematics. Princeton University Press, 2009. -1184 p.
Waiting