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    Wyświetlanie 1-4 z 4
    Tytuł:
    Neuro-adaptive cooperative control for high-order nonlinear multi-agent systems with uncertainties
    Autorzy:
    Peng, Cheng
    Zhang, Anguo
    Li, Junyu
    Powiązania:
    https://bibliotekanauki.pl/articles/2055174.pdf
    Data publikacji:
    2021
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    multiagent system
    radial basis function
    RBF neural network
    sliding mode control
    cooperative control
    system wieloagentowy
    radialna funkcja bazowa
    sieć neuronowa RBF
    sterowanie ślizgowe
    Opis:
    The consensus problem for a class of high-order nonlinear multi-agent systems (MASs) with external disturbance and system uncertainty is studied. We design an online-update radial basis function (RBF) neural network based distributed adaptive control protocol, where the sliding model control method is also applied to eliminate the influence of the external disturbance and system uncertainty. System consensus is verified by using the Lyapunov stability theorem, and sufficient conditions for cooperative uniform ultimately boundedness (CUUB) are also derived. Two simulation examples demonstrate the effectiveness of the proposed method for both homogeneous and heterogeneous MASs.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2021, 31, 4; 635--645
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Cluster consensus of general fractional-order nonlinear multi agent systems via adaptive sliding mode controller
    Autorzy:
    Yaghoubi, Zahra
    Talebi, Heidar Ali
    Powiązania:
    https://bibliotekanauki.pl/articles/230002.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    nonlinear multi agent systems
    cluster consensus
    fractional-order systems
    adaptive sliding mode controller
    Opis:
    In this paper cluster consensus is investigated for general fractional-order multi agent systems with nonlinear dynamics via adaptive sliding mode controller. First, cluster consensus for fractional-order nonlinear multi agent systems with general form is investigated. Then, cluster consensus for the fractional-order nonlinear multi agent systems with first-order and general form dynamics is investigated by using adaptive sliding mode controller. Sufficient conditions for achieving cluster consensus for general fractional-order nonlinear multi agent systems are proved based on algebraic graph theory, Lyapunov stability theorem and Mittag-Leffler function. Finally, simulation examples are presented for first-order and general form multi agent systems, i.e. a single-link flexible joint manipulator which demonstrates the efficiency of the proposed adaptive controller.
    Źródło:
    Archives of Control Sciences; 2019, 29, 4; 643-665
    1230-2384
    Pojawia się w:
    Archives of Control Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Event-triggered cooperative control for high-order nonlinear multi-agent systems with finite-time consensus
    Autorzy:
    Gong, Shiyin
    Zheng, Meirong
    Hu, Jing
    Zhang, Anguo
    Powiązania:
    https://bibliotekanauki.pl/articles/24200691.pdf
    Data publikacji:
    2023
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    multiagent system
    cooperative control
    event triggered control
    neuroadaptive control
    prescribed performance
    system wieloagentowy
    sterowanie wyzwalane zdarzeniami
    sterowanie neuroadaptacyjne
    Opis:
    An event-triggered adaptive control algorithm is proposed for cooperative tracking control of high-order nonlinear multiagent systems (MASs) with prescribed performance and full-state constraints. The algorithm combines dynamic surface technology and the backstepping recursive design method, with radial basis function neural networks (RBFNNs) used to approximate the unknown nonlinearity. The barrier Lyapunov function and finite-time stability theory are employed to prove that all agent states are semi-globally uniform and ultimately bounded, with the tracking error converging to a bounded neighborhood of zero in a finite time. Numerical simulations are provided to demonstrate the effectiveness of the proposed control scheme.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2023, 33, 3; 439--448
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Nonlinear convergence algorithm: structural properties with doubly stochastic quadratic operators for multi-agent systems
    Autorzy:
    Abdulghafor, R.
    Turaev, S.
    Zeki, A.
    Abubaker, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/91876.pdf
    Data publikacji:
    2018
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    doubly stochastic quadratic operators
    nonlinear convergence algorithm
    consensus problem
    multi-agent systems
    kwadratowe operatory podwójnie stochastyczne
    problem konsensusu
    systemy wieloagentowe
    Opis:
    This paper proposes nonlinear operator of extreme doubly stochastic quadratic operator (EDSQO) for convergence algorithm aimed at solving consensus problem (CP) of discrete-time for multi-agent systems (MAS) on n-dimensional simplex. The first part undertakes systematic review of consensus problems. Convergence was generated via extreme doubly stochastic quadratic operators (EDSQOs) in the other part. However, this work was able to formulate convergence algorithms from doubly stochastic matrices, majorization theory, graph theory and stochastic analysis. We develop two algorithms: 1) the nonlinear algorithm of extreme doubly stochastic quadratic operator (NLAEDSQO) to generate all the convergent EDSQOs and 2) the nonlinear convergence algorithm (NLCA) of EDSQOs to investigate the optimal consensus for MAS. Experimental evaluation on convergent of EDSQOs yielded an optimal consensus for MAS. Comparative analysis with the convergence of EDSQOs and DeGroot model were carried out. The comparison was based on the complexity of operators, number of iterations to converge and the time required for convergences. This research proposed algorithm on convergence which is faster than the DeGroot linear model.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2018, 8, 1; 49-61
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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