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Adaptive Neural Finite-time Trajectory Tracking Control of MSVs Subject to Uncertainties

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Abstract

This paper provides two finite-time trajectory tracking control schemes for marine surface vessels (MSVs) which are influenced by dynamic uncertainties and unknown time-varying disturbances. Neural networks (NNs) are applied to reconstruct the vehicle’s dynamic uncertainties, and the sum of upper bound of approximation error and external unknown disturbances is estimated by designing an adaptive law. According to the backstepping technique and finite-time stability theory, a finite-time trajectory tracking control scheme is presented. Further, to decrease the conservatism of the presented control scheme caused by estimating the upper bound, a multivariate sliding mode finite-time disturbance observer (MSMFTDO) is designed to estimate the unknown external disturbances and the part that is not completely reconstructed by NNs, and then a MSMFTDO-based adaptive neural finite-time trajectory tracking control law is designed. Rigorous theoretical analyses are provided to prove that, owing to the developed finite-time trajectory tracking control strategies, all the signals of the closed-loop trajectory tracking control system are bounded, and that the actual trajectory of MSVs is able to track the reference trajectory in finite time. Simulation results illustrate the effectiveness of the developed schemes.

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Authors and Affiliations

  1. School of Maritime, Shandong Jiaotong University, 264209, 1508 Hexing Road, Huancui District, Weihai City, Shandong Province, China

    Qiang Zhang & Meijuan Zhang

  2. School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan, 250357, China

    Renming Yang

  3. School of Navigation Science, Mokpo National Maritime University, Mokpo, 530729, Korea

    Namkyun Im

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  1. Qiang Zhang
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Correspondence to Qiang Zhang.

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This reaesrch work was supported by the National Natural Science Foundation of China (61873071, 51911540478, G61773015), key research and development plan of Shandong province (2018GGX105014, 2019JZZY020712, 2018GGX105003), Shandong Jiaotong University PhD Startup Foundation of Scientific Research, and Shandong Jiaotong University “Climbing” Research Innovation Team Program (SDJTUC1802). A Project of Shandong Province Higher Educational Science and Technology Program (J18KA010, J18KA043).

Qiang Zhang received his Ph.D. degree in traffic information engineering and control from Dalian Maritime University. He is currently a ship captain and professor in Shandong Jiaotong University. His current research interests include nonlinear feedback control, ship automatic berthing control, and ship robust control.

Meijuan Zhang received her bachelor’s degree in traffic equipment and control engineering from Shandong Jiaotong University, Shandong, China, in 2018. And now, she is a graduate student in waterway transport and safety engineering in Shandong Jiaotong University. Her current research interests include nonlinear feedback control, ship control and safety.

Renming Yang received his M.S. and Ph.D. degrees from Shandong Normal University and Shandong University, China, in 2006 and 2012, respectively. He joined Shandong Jiaotong University in 2012, where he is currently an associate professor. His research interests include the stability analysis and control design for nonlinear system, and nonlinear delay system.

Namkyun Im received his B.Sc. degree in navigation science from Korea Maritime University in 1992 and a Ph.D. in naval architecture and ocean engineering from Osaka University, Japan in 2002. Since then, he has worked as a senior researcher at Ship and Ocean Research Center of Samsung Heavy Industries for three years. He is currently a professor in Mokpo National Maritime University. His research fields are as follows: ship automatic control study, ship manoeuvring simulation and its applications, marine traffic simulation, ship free running model, and marine/ship environmental issues.

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Zhang, Q., Zhang, M., Yang, R. et al. Adaptive Neural Finite-time Trajectory Tracking Control of MSVs Subject to Uncertainties. Int. J. Control Autom. Syst. 19, 2238–2250 (2021). https://doi.org/10.1007/s12555-020-0130-5

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  • DOI: https://doi.org/10.1007/s12555-020-0130-5

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