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Dynamic Event-Triggered Adaptive Neural Output Feedback Control for MSVs Using Composite Learning
School of Maritime, 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
Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).ORCID-id: 0000-0002-2763-8085
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2023 (engelsk)Inngår i: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 24, nr 1, s. 787-800Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

This paper investigates the control issue of marine surface vehicles (MSVs) subject to internal and external uncertainties without velocity information. Utilizing the specific advantages of adaptive neural network and disturbance observer, a classification reconstruction idea is developed. Based on this idea, a novel adaptive neural-based state observer with disturbance observer is proposed to recover the unmeasurable velocity. Under the vector-backstepping design framework, the classification reconstruction idea and adaptive neural-based state observer are used to resolve the control design issue for MSVs. To improve the control performance, the serial-parallel estimation model is introduced to obtain a prediction error, and then a composite learning law is designed by embedding the prediction error and estimate of lumped disturbance. To reduce the mechanical wear of actuator, a dynamic event triggering protocol is established between the control law and actuator. Finally, a new dynamic event-triggered composite learning adaptive neural output feedback control solution is developed. Employing the Lyapunov stability theory, it is strictly proved that all signals in the closed-loop control system of MSVs are bounded. Simulation and comparison results validate the effectiveness of control solution.

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Institute of Electrical and Electronics Engineers (IEEE), 2023. Vol. 24, nr 1, s. 787-800
HSV kategori
Identifikatorer
URN: urn:nbn:se:mau:diva-56495DOI: 10.1109/tits.2022.3217152ISI: 000881952800001Scopus ID: 2-s2.0-85141551954OAI: oai:DiVA.org:mau-56495DiVA, id: diva2:1717126
Tilgjengelig fra: 2022-12-07 Laget: 2022-12-07 Sist oppdatert: 2023-07-04bibliografisk kontrollert

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Malekian, Reza

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Zhu, GuibingMa, YongLi, ZhixiongMalekian, RezaSotelo, M.
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IEEE transactions on intelligent transportation systems (Print)

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