Autor: Clempner, Julio - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Robust extremum seeking for a second order uncertain plant using a sliding mode controller
Autorzy:: Solis, Cesar
Clempner, Julio
Poznyak, Alexander
Powiązania:: https://bibliotekanauki.pl/articles/330477.pdf
Data publikacji:: 2019
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: convex optimization
extremum seeking
continuous time gradient algorithm
dynamical constrained optimization
unknown function
optymalizacja wypukła
poszukiwanie ekstremum
algorytm gradientowy
optymalizacja ograniczona
Opis:: This paper suggests a novel continuous-time robust extremum seeking algorithm for an unknown convex function constrained by a dynamical plant with uncertainties. The main idea of the proposed method is to develop a robust closed-loop controller based on sliding modes where the sliding surface takes the trajectory around a zone of the optimal point. We assume that the output of the plant is given by the states and a measure of the function. We show the stability and zone-convergence of the proposed algorithm. In order to validate the proposed method, we present a numerical example.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2019, 29, 4; 703-712
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Computing a mechanism for a Bayesian and partially observable Markov approach
Autorzy:: Clempner, Julio B.
Poznyak, Alexander S.
Powiązania:: https://bibliotekanauki.pl/articles/24200692.pdf
Data publikacji:: 2023
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: dynamic mechanism design
partially observable Markov chains
games with private information
Bayesian equilibrium
mechanizm dynamiczny
łańcuchy Markova
równowaga Bayesa
Opis:: The design of incentive-compatible mechanisms for a certain class of finite Bayesian partially observable Markov games is proposed using a dynamic framework. We set forth a formal method that maintains the incomplete knowledge of both the Bayesian model and the Markov system’s states. We suggest a methodology that uses Tikhonov’s regularization technique to compute a Bayesian Nash equilibrium and the accompanying game mechanism. Our framework centers on a penalty function approach, which guarantees strong convexity of the regularized reward function and the existence of a singular solution involving equality and inequality constraints in the game. We demonstrate that the approach leads to a resolution with the smallest weighted norm. The resulting individually rational and ex post periodic incentive compatible system satisfies this requirement. We arrive at the analytical equations needed to compute the game’s mechanism and equilibrium. Finally, using a supply chain network for a profit maximization problem, we demonstrate the viability of the proposed mechanism design.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2023, 33, 3; 463--478
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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