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Optimization-based model reference adaptive control for dynamic positioning of a fully actuated underwater vehicle

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Abstract

This paper presents a predictive optimization-based model reference adaptive control (MRAC) approach for dynamic positioning (DP) of a fully actuated underwater vehicle subject to dynamic uncertainties and actuator saturation. Compared with conventional linear reference model-based approaches, this proposed MRAC controller utilizes an optimized reference model composed of the closed-loop approximate vehicle model under a nonlinear model predictive controller, in which both the state and input constraints are considered. An adaptive dynamic inversion controller is designed to track the reference trajectory in the presence of dynamic uncertainties, and a single hidden layer neural network is incorporated to compensate for the mismatch of the actual and approximate models and ensure the convergence of tracking errors. The effectiveness of the proposed DP approach is validated by comparative simulations performed with a remotely operated vehicle.

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Acknowledgements

The authors would like to thank the editor and reviewers for their constructive comments and suggestions that have improved the quality of the paper. This work is supported by the National Natural Science Foundation of China under Grant 51279164.

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Authors and Affiliations

  1. School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an, 710072, China

    Jian Gao, Puguo Wu & Tianrui Li

  2. Ocean Floor Geophysics Inc., Burnaby, BC, V5J 5J3, Canada

    Alison Proctor

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  1. Jian Gao
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  2. Puguo Wu
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  3. Tianrui Li
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  4. Alison Proctor
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Correspondence to Jian Gao.

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Gao, J., Wu, P., Li, T. et al. Optimization-based model reference adaptive control for dynamic positioning of a fully actuated underwater vehicle. Nonlinear Dyn 87, 2611–2623 (2017). https://doi.org/10.1007/s11071-016-3214-2

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  • DOI: https://doi.org/10.1007/s11071-016-3214-2

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