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Adaptive Trajectory Tracking Control for Remotely Operated Vehicles Based on Disturbance Observer

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Intelligent Robotics and Applications (ICIRA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10985))

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Abstract

In this study, an adaptive trajectory tracking control method based on disturbance observer was proposed for remotely operated vehicles (ROVs). Considering the problem of modeling uncertainty and external disturbance, the online estimation method based on disturbance observer was proposed. Due to the complexity of thruster thrust model, there is an uncertain input gain in the trajectory tracking control system. Therefore, a new adaptive sliding mode control scheme is adopted. Stability analysis showed that the trajectory tracking error is uniform ultimate boundeded. Finally, simulations were conducted to show the effectiveness of the proposed controller.

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References

  1. Campos, E., Chemori, A., Creuze, V., et al.: Saturation based nonlinear depth and yaw control of underwater vehicles with stability analysis and real-time experiments. Mechatronics 45, 49–59 (2017)

    Article  Google Scholar 

  2. Yan, Y., Yu, S.: Sliding mode tracking control of autonomous underwater vehicles with the effect of quantization. Ocean Eng. 151, 322–328 (2018)

    Article  Google Scholar 

  3. Liu, X., Zhang, M., Yao, F.: Adaptive fault tolerant control and thruster fault reconstruction for autonomous underwater vehicle. Ocean Eng. 155, 10–23 (2018)

    Article  Google Scholar 

  4. Xiang, X., Yu, C., Lapierre, L., et al.: Survey on fuzzy-logic-based guidance and control of marine surface vehicles and underwater vehicles. Int. J. Fuzzy Syst. 20(2), 572–586 (2018)

    Article  MathSciNet  Google Scholar 

  5. Liu, S., Liu, Y., Wang, N.: Nonlinear disturbance observer-based backstepping finite-time sliding mode tracking control of underwater vehicles with system uncertainties and external disturbances. Nonlinear Dyn. 88(1), 465–476 (2017)

    Article  Google Scholar 

  6. Qiao, L., Zhang, W.: Adaptive non-singular integral terminal sliding mode tracking control for autonomous underwater vehicles. IET Control Theory Appl. 11(8), 1293–1306 (2017)

    Article  MathSciNet  Google Scholar 

  7. Gao, J., An, X., Proctor, A., et al.: Sliding mode adaptive neural network control for hybrid visual servoing of underwater vehicles. Ocean Eng. 142, 666–675 (2017)

    Article  Google Scholar 

  8. Zhang, G., Yan, W., Gao, J., et al.: High-gain observer-based model predictive control for cross tracking of underactuated autonomous underwater vehicles. In: IEEE International Conference on Underwater System Technology: Theory and Application. IEEE, Penang, Malaysia (2017). https://doi.org/10.1109/USYS.2016.7993920

  9. Londhe, P.S., Dhadekar, D., Patre, B.M., et al.: Uncertainty and disturbance estimator based sliding mode control of an autonomous underwater vehicle. Int. J. Dyn. Control 5(4), 1122–1138 (2017)

    Article  MathSciNet  Google Scholar 

  10. Liu, Y.C., Liu, S.Y., Wang, N.: Fully-tuned fuzzy neural network based robust adaptive tracking control of unmanned underwater vehicle with thruster dynamics. Neurocomputing 196(C), 1–13 (2016)

    Google Scholar 

  11. Zhang, M.J., Chu, Z.Z.: Adaptive sliding mode control based on local recurrent neural networks for underwear robot. Ocean Eng. 45(2), 56–62 (2012)

    Article  Google Scholar 

  12. Zakeri, E., Farahat, S., Moezi, S.A., et al.: Robust sliding mode control of a mini unmanned underwater vehicle equipped with a new arrangement of water jet propulsions: Simulation and experimental study. Appl. Ocean Res. 59, 521–542 (2016)

    Article  Google Scholar 

  13. Chu, Z., Zhu, D., Yang, S.X.: Observer-based adaptive neural network trajectory tracking control for remotely operated vehicle. IEEE Trans. Neural Networks Learn. Syst. 28(7), 1633–1645 (2017)

    Article  MathSciNet  Google Scholar 

  14. Song, Y., Zhao, K., Krstic, M.: Adaptive control with exponential regulation in the absence of persistent excitation. IEEE Trans. Autom. Control 62, 2589–2596 (2016)

    Article  MathSciNet  Google Scholar 

  15. Zhao, K., Song, Y., Qian, J., et al.: Zero-error tracking control with pre-assignable convergence mode for nonlinear systems under nonvanishing uncertainties and unknown control direction. Syst. Control Lett. 115, 34–40 (2018)

    Article  MathSciNet  Google Scholar 

  16. Zhao, K., Song, Y., Ma, T., et al.: Prescribed performance control of uncertain euler-lagrange systems subject to full-state constraints. IEEE Trans. Neural Networks Learn. Syst. (2017). https://doi.org/10.1109/TNNLS.2017.2727223

    Article  Google Scholar 

  17. Yan, X.G., Edwards, C.: Nonlinear robust fault reconstruction and estimation using a sliding mode observer. Automatics 43(9), 1605–1614 (2007)

    Article  MathSciNet  Google Scholar 

  18. Podder, T.K., Sarkar, N.: Fault-tolerant control of an autonomous underwater vehicle under thruster redundancy. Robot. Auton. Syst. 34(1), 39–52 (2001)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Shanghai Maritime University, 1550 Haigang Avenue, Shanghai, China

    Zhenzhong Chu, Daqi Zhu & Bing Sun

Authors
  1. Zhenzhong Chu
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  2. Daqi Zhu
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  3. Bing Sun
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Corresponding author

Correspondence to Zhenzhong Chu .

Editor information

Editors and Affiliations

  1. University of Newcastle, Callaghan, New South Wales, Australia

    Zhiyong Chen

  2. University of Newcastle, Callaghan, New South Wales, Australia

    Alexandre Mendes

  3. University of Newcastle, Callaghan, New South Wales, Australia

    Yamin Yan

  4. Shenzhen Institutes of Advanced Technology, Shenzhen, China

    Shifeng Chen

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Chu, Z., Zhu, D., Sun, B. (2018). Adaptive Trajectory Tracking Control for Remotely Operated Vehicles Based on Disturbance Observer. In: Chen, Z., Mendes, A., Yan, Y., Chen, S. (eds) Intelligent Robotics and Applications. ICIRA 2018. Lecture Notes in Computer Science(), vol 10985. Springer, Cham. https://doi.org/10.1007/978-3-319-97589-4_12

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  • DOI: https://doi.org/10.1007/978-3-319-97589-4_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97588-7

  • Online ISBN: 978-3-319-97589-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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