Neuro-adaptive cooperative control for high-order nonlinear multi-agent systems with uncertainties

• Autorzy: Peng Cheng , Zhang Anguo , Li Junyu

Identyfikatory

DOI

10.34768/amcs-2021-0044

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

multiagent system; radial basis function; RBF neural network; sliding mode control; cooperative control

PL

system wieloagentowy; radialna funkcja bazowa; sieć neuronowa RBF; sterowanie ślizgowe

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2021
• Tom: Vol. 31, no. 4
• Strony: 635--645
• Opis fizyczny: Bibliogr. 37 poz., rys., wykr.

Twórcy

autor

Peng Cheng

• School of Electrical and Mechanical Engineering, Hefei Technology College, Hefei 230012, China

autor

Zhang Anguo

• College of Physics and Information Engineering, Fuzhou University, Fuzhou 350018, China

autor

Li Junyu

• School of Electrical and Mechanical Engineering, Hefei Technology College, Hefei 230012, China

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).