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Dynamic Event-Triggered Adaptive Formation With Disturbance Rejection for Marine Vehicles Under Unknown Model Dynamics
School of Mathematics and Statistics Science, Ludong University, Yantai, China.
Marine College, Zhejiang Ocean University, Zhoushan, China.ORCID iD: 0000-0002-8267-9437
School of Navigation, Hubei Key Laboratory of Inland Shipping Technology, Wuhan University of Technology, Wuhan, China.ORCID iD: 0000-0003-0418-9210
Faculty of Mechanical Engineering, Opole University of Technology, Opole, Poland.ORCID iD: 0000-0003-4067-0669
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2023 (English)In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 72, no 5, p. 5664-5676Article in journal (Refereed) Published
Abstract [en]

This paper investigates the dynamic event-triggered adaptive neural coordinated disturbance rejection for marine vehicles with external disturbances as the sinusoidal superpositions with unknown frequencies, amplitudes and phases. The vehicle movement mathematical models are transformed into parameterized expressions with the neural networks approximating nonlinear dynamics. The parametric exogenous systems are exploited to express external disturbances, which are converted into the linear canonical models with coordinated changes. The adaptive technique together with disturbance filters realize the disturbance estimation and rejection. By using the vectorial backstepping, the dynamic event-triggered adaptive neural coordinated disturbance rejection controller is derived with the dynamic event-triggering conditions being incorporated to reduce execution frequencies of vehicle's propulsion systems. The coordinated formation control can be achieved with the closed-loop semi-global stability. The dynamic event-triggered adaptive disturbance rejection scheme achieves the disturbance estimation and cancellation without requiring the a priori marine vehicle's model dynamics. Illustrative simulations and comparisons validate the proposed scheme.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023. Vol. 72, no 5, p. 5664-5676
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:mau:diva-57001DOI: 10.1109/tvt.2022.3231585ISI: 000991849700011Scopus ID: 2-s2.0-85146215359OAI: oai:DiVA.org:mau-57001DiVA, id: diva2:1723114
Available from: 2023-01-02 Created: 2023-01-02 Last updated: 2023-08-15Bibliographically approved

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Malekian, Reza

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Zhu, GuibingMa, YongLi, ZhixiongMalekian, Reza
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