114

The questions of constructing regular algorithms for parametric identification
of a linear dynamic plant with random parameters with guaranteed mean square accuracy are
considered. To estimate the vector of unknown parameters of a dynamic system, a sequential
version of the least squares estimates is used. When solving the considered ill-posed problem,
a regular algorithm is used. When choosing the regularization parameter, the methods of
quasi-optimality and cross-significance were used in the work. The considered algorithms
make it possible to produce a stable identification of a linear dynamic system with random parameters, and thereby improve the accuracy of the synthesized adaptive control system for
the considered class of objects.
 

  • Ссылка в интернете
  • DOI
  • Дата создание в систему UzSCI 16-01-2023
  • Количество прочтений 114
  • Дата публикации 13-01-2023
  • Язык статьиIngliz
  • Страницы154-158
English

The questions of constructing regular algorithms for parametric identification
of a linear dynamic plant with random parameters with guaranteed mean square accuracy are
considered. To estimate the vector of unknown parameters of a dynamic system, a sequential
version of the least squares estimates is used. When solving the considered ill-posed problem,
a regular algorithm is used. When choosing the regularization parameter, the methods of
quasi-optimality and cross-significance were used in the work. The considered algorithms
make it possible to produce a stable identification of a linear dynamic system with random parameters, and thereby improve the accuracy of the synthesized adaptive control system for
the considered class of objects.
 

№ Имя автора Должность Наименование организации
1 H I.S. lecturer Fergana branch of TUIT of the Department of "Information Technologies"
№ Название ссылки
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11 L. Duan., J.B. Yang. Iterative parametric minimax method for a class of composite optimization problems. “Journal of mathematical analysis and applications”, 1996. 64
12 M.V. Zhirov., V.V. Makarov. “Adaptive identification of non-stationary technological processes with Markov parameters in stochastic control problems”, 2002. 56.
13 A.N. Tikhonov., A.S. Leonov., A.G. Yagola. “Nonlinear ill-posed problems”, 1995. 308
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15 O.O. Zaripov., D.A. Akhmedov., U.F. Mamirov. Adaptive estimation algorithms for the state of nonlinear dynamic systems. “International Journal of Psychosocial Rehabilitation”, 2017. 247
16 Kh.Z. Igamberdiev., A.N. Yusupbekov., O.O. Zaripov. “Regular methods for estimating and managing dynamic objects under uncertainty”, 2012. 320.
17 Kh.Z. Igamberdiev., J.U. Sevinov., O.O. Zaripov. “Regular methods and algorithms for the synthesis of adaptive control systems with customizable models”, 2014. 187
18 U.F. Mamirov. “Regular synthesis of adaptive control systems for uncertain dynamic objects”, 2021. 215
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