Stochastic Discrete Nonlinear Control System for Minimum Dispersion of the Output Variable

Kolesnikova, S. I.

doi:10.1007/978-3-030-19813-8_33

Cited by 9 publications

(5 citation statements)

References 5 publications

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“…are a nonlinear function and control, respectively, the output signal dispersion cannot be smaller than that of the noise presented in the object's description under any control (see, e.g., [6][7][8]). Thus, here we deal with the so-called unrecoverable uncertainty in the control objects.…”

Section: The Relevance For Correct Estimation Of Time Series For a DImentioning

confidence: 99%

“…In description (1), similarly to the case in [9] the following notations are used: variables V , S, F are the concentrations of antigens in the target affected organs, plasma cells, and blood antibodies, respectively; Omitting technical details, we use the discrete method of analytical design of aggregated stochastic regulators (ADAR(S)) [7,8] that lead to the control:…”

Section: Application Of Kernel Filtering In a Stochastic Control Algomentioning

confidence: 99%

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Forecasting Non-Stationary Time Series Using Kernel Regression for Control Problems

Kolesnikova

Avramyonok

Bogdanova

2021

Communications in Computer and Information Science

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A combined algorithm for a time series analysis is considered based on two basic methods: the empirical mode decomposition and kernel regression. The essence of the presented algorithm is the sequential calculation of nuclear regressions and residues, which results in the decomposition of the original series into an additive mixture of the number of regressions and residual series. The illustrative examples for the application of the proposed algorithm (immunology, economics, and other fields of studies) are provided along with their statistical results of numerical simulation. The results obtained would be useful for a smart control system design and real-time decision making support as it concerns the problems of stochastic control over a wide range of poorly formalized objects from various applied areas.

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Section: The Relevance For Correct Estimation Of Time Series For a DImentioning

confidence: 99%

Section: Application Of Kernel Filtering In a Stochastic Control Algomentioning

confidence: 99%

Forecasting Non-Stationary Time Series Using Kernel Regression for Control Problems

Kolesnikova

Avramyonok

Bogdanova

2021

Communications in Computer and Information Science

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“…The article presents two algorithms for designing control in the state space of the unstable object with a different physical nature of disturbances. Let us name them conditionally NAD (Nonlinear ADaptation algorithm for a nonrandom object [7]- [9]) and NAS (Nonlinear Adaptation algorithm for a Stochastic object [10]). Note that both algorithms are correct extensions of the classic ADAR-algorithm [7], [10] (see Appendix A1).…”

Section: Control Problem Statementmentioning

confidence: 99%

“…Let us name them conditionally NAD (Nonlinear ADaptation algorithm for a nonrandom object [7]- [9]) and NAS (Nonlinear Adaptation algorithm for a Stochastic object [10]). Note that both algorithms are correct extensions of the classic ADAR-algorithm [7], [10] (see Appendix A1).…”

Section: Control Problem Statementmentioning

confidence: 99%

“…... Restrictions on the choice of command variables are as follows [10], [11]: 1) control strategies are selected from the class of discrete ADAR-controls; 2) those strategies, for which the value of the control variable ( ) is a function of the previous states and controls, are taken into consideration: ( ) = ( ), ( − 1), . .…”

Section: Basic Provisions Of the Nad-algorithmmentioning

confidence: 99%

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Synthesis of a robust stochastic regulator for the model of electrohydraulic brake system

Kolesnikova¹,

Semenikhin²

2021

J. vibroeng.

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Three new algorithms for synthesizing control for the model of an electrohydraulic disk brake system are presented, which are based on the synergetic control theory. The first algorithm is developed relying on the classical method of analytical design of aggregated regulators in an assumption of a completely defined object. The second algorithm represents an algorithm of nonlinear adaptation on a target manifold and is designed for an object with a nonrandom disturbance in the control channel. The third algorithm takes account of the random disturbance in the discrete description of this object and rests on the strategies minimizing the dispersion of the output macrovariable. The results of a comparative numerical simulation of the three control algorithms are presented and the recommendations concerning the selection of the regulator parameters are formulated depending on the level of systematic disturbances and random noise.

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Discrete Synthesis of a Stochastic Regulator for Adaptation of a Discrete Economic Object on a Target Manifold

Kolesnikova

2019

Computational Statistics and Mathematical Modeling Methods in Intelligent Systems

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Stochastic Discrete Nonlinear Control System for Minimum Dispersion of the Output Variable

Cited by 9 publications

References 5 publications

Forecasting Non-Stationary Time Series Using Kernel Regression for Control Problems

Forecasting Non-Stationary Time Series Using Kernel Regression for Control Problems

Synthesis of a robust stochastic regulator for the model of electrohydraulic brake system

Discrete Synthesis of a Stochastic Regulator for Adaptation of a Discrete Economic Object on a Target Manifold

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