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Adaptive simplified surge-heading tracking control for underwater vehicles with thruster’s dead-zone compensation

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Abstract

Remotely operated underwater vehicles are usually equipped with four horizontal thrusters that form an X-shaped actuation configuration. Yet, thruster’s inherent dead-zone may possibly result in strong chatter of moment inputs and motion tracking of underwater vehicles. This paper proposes a two-layer cascade tracking controller together with a dead-zone compensator, in order to achieve simplified and effective surge-heading control of underwater vehicles equipped with an X-shaped horizontal actuation configuration. For the sake of brevity, the surge and heading dynamics are firstly unified as a second-order dynamic system where the known and unknown parts are separated, respectively. Based on this model, a feedback linearization control law with a combined error measure is designed in the first-layer cascade system for the simplified dynamics tracking. Then, a reduced-order extended state observer without using any priori knowledge of uncertainties is utilized in the second-layer cascade system to estimate the complex uncertainty of the dynamics. It is noted that this two-layer tracking controller has only two gains to be adjusted, ensuring a simple calculation and microprogramming. Subsequently, a dedicated dead-zone compensator is proposed for the X-shaped actuation configuration and the input-to-state stability of the whole tracking system is analyzed. Finally, comparative numerical cases are provided to demonstrate the adaptivity and robustness of the designed surge-heading tracking controller, i.e., up to 56% reduction of the maximum surge tracking error owing to this dead-zone compensator and less than 0.03\(^\circ \) of the heading steady state error against different initial states.

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References

  1. Zhang, F., Marani, G., Smith, R.N., Choi, H.T.: Future trends in marine robotics. IEEE Robotics Autom. Mag. 22(1), 14–122 (2015)

    Article  Google Scholar 

  2. Macreadie, P.I., McLean, D.L., Thomson, P.G., Partridge, J.C., Jones, D.O., Gates, A.R., Benfield, M.C., Collin, S.P., Booth, D.J., Smith, L.L., et al.: Eyes in the sea: unlocking the mysteries of the ocean using industrial, remotely operated vehicles (ROVs). Sci. Total Environ. 634, 1077–1091 (2018)

    Article  Google Scholar 

  3. Wang, Z., Yang, S., Xiang, X., Vasilijevic, A., Miskovic, N., Nad, D.: Cloud-based mission control of USV fleet: architecture, implementation and experiments. Control Eng. Pract. 106, 104,657 (2021)

    Article  Google Scholar 

  4. Yu, C., Xiang, X., Lapierre, L., Zhang, Q.: Robust magnetic tracking of subsea cable by AUV in the presence of sensor noise and ocean currents. IEEE J. Ocean. Eng. 43(2), 311–322 (2018)

    Article  Google Scholar 

  5. Liu, L., Wang, D., Peng, Z.: Direct and composite iterative neural control for cooperative dynamic positioning of marine surface vessels. Nonlinear Dyn. 81(3), 1315–1328 (2015)

    Article  MATH  Google Scholar 

  6. Wu, D., Ren, F., Qiao, L., Zhang, W.: Active disturbance rejection controller design for dynamically positioned vessels based on adaptive hybrid biogeography-based optimization and differential evolution. ISA Trans. 78, 56–65 (2018)

    Article  Google Scholar 

  7. Gao, X., Li, T., Yuan, L., Bai, W.: Robust fuzzy adaptive output feedback optimal tracking control for dynamic positioning of marine vessels with unknown disturbances and uncertain dynamics. Int. J. Fuzzy Syst. 23(7), 2283–2296 (2021)

    Article  Google Scholar 

  8. Rout, R., Cui, R., Han, Z.: Modified line-of-sight guidance law with adaptive neural network control of underactuated marine vehicles with state and input constraints. IEEE Trans. Control Syst. Technol. 28(5), 1902–1914 (2020)

    Article  Google Scholar 

  9. Zhang, G., Huang, C., Zhang, X., Tian, B.: Robust adaptive control for dynamic positioning ships in the presence of input constraints. J. Mar. Sci. Technol. 24(4), 1172–1182 (2019)

    Article  Google Scholar 

  10. Xu, H., Fossen, T.I., Soares, C.G.: Uniformly semiglobally exponential stability of vector field guidance law and autopilot for path-following. Eur. J. Control. 53, 88–97 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  11. Peng, Z., Wang, J., Han, Q.: Path-following control of autonomous underwater vehicles subject to velocity and input constraints via neurodynamic optimization. IEEE Trans. Ind. Electron. 66(11), 8724–8732 (2019)

    Article  Google Scholar 

  12. Wang, N., Sun, Z., Jiao, Y., Han, G.: Surge-heading guidance-based finite-time path following of underactuated marine vehicles. IEEE Trans. Veh. Technol. 68(9), 8523–8532 (2019)

    Article  Google Scholar 

  13. Chen, L., Cui, R., Yang, C., Yan, W.: Adaptive neural network control of underactuated surface vessels with guaranteed transient performance: theory and experimental results. IEEE Trans. Ind. Electron. 67(5), 4024–4035 (2019)

    Article  Google Scholar 

  14. Chu, Z., Xiang, X., Zhu, D., Luo, C., Xie, D.: Adaptive trajectory tracking control for remotely operated vehicles considering thruster dynamics and saturation constraints. ISA Trans. 100, 28–37 (2020)

    Article  Google Scholar 

  15. Liu, S., Liu, Y., Liang, X., Wang, N.: Uncertainty observation-based adaptive succinct fuzzy-neuro dynamic surface control for trajectory tracking of fully actuated underwater vehicle system with input saturation. Nonlinear Dyn. 98(8), 1683–1699 (2019)

    Article  MATH  Google Scholar 

  16. Khodayari, M.H., Balochian, S.: Modeling and control of autonomous underwater vehicle (AUV) in heading and depth attitude via self-adaptive fuzzy PID controller. J. Mar. Sci. Technol. 20(3), 559–578 (2015)

    Article  Google Scholar 

  17. Ishaque, K., Abdullah, S., Ayob, S., Salam, Z.: A simplified approach to design fuzzy logic controller for an underwater vehicle. Ocean Eng. 38(1), 271–284 (2015)

    Article  Google Scholar 

  18. Fossen, T.I., Lekkas, A.M.: Direct and indirect adaptive integral line-of-sight path-following controllers for marine craft exposed to ocean currents. Int. J. Adapt. Control Signal Process. 31(4), 445–463 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  19. Bessa, W.M., Kreuzer, E., Lange, J., Pick, M.A., Solowjow, E.: Design and adaptive depth control of a micro diving agent. IEEE Robotics Autom. Lett. 2(4), 1871–1877 (2017)

    Article  Google Scholar 

  20. Lu, D., Xiong, C., Zeng, Z., Lian, L.: Adaptive dynamic surface control for a hybrid aerial underwater vehicle with parametric dynamics and uncertainties. IEEE J. Ocean. Eng. 45(3), 740–758 (2020)

    Article  Google Scholar 

  21. Chu, Z., Zhu, D., Yang, S.X., Jan, G.E.: Adaptive sliding mode control for depth trajectory tracking of remotely operated vehicle with thruster nonlinearity. J. Navig. 70(1), 149–164 (2017)

    Article  Google Scholar 

  22. Ropars, B., Lasbouygues, A., Lapierre, L., Andreu, D.: Thruster’s dead-zones compensation for the actuation system of an underwater vehicle. In: Proceedings of the 2015 European Control Conference (ECC), pp. 741–746 (2015)

  23. Ropars, B., Lapierre, L., Lasbouygues, A., Andreu, D., Zapata, R.: Redundant actuation system of an underwater vehicle. Ocean Eng. 151, 276–289 (2018)

    Article  Google Scholar 

  24. Xia, G., Pang, C., Xue, J.: Fuzzy neural network-based robust adaptive control for dynamic positioning of underwater vehicles with input dead-zone. J. Intell. Fuzzy Syst. 29(6), 2585–2595 (2015)

    Article  MATH  Google Scholar 

  25. Sontag, E.D., Wang, Y.: On characterizations of the input-to-state stability property. Syst. Control Lett. 24(5), 351–359 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  26. Liu, L., Wang, D., Peng, Z.: ESO-based line-of-sight guidance law for path following of underactuated marine surface vehicles with exact sideslip compensation. IEEE J. Ocean. Eng. 42(2), 477–487 (2017)

    Article  Google Scholar 

  27. Khalil, H.K. (ed.): Nonlinear Control (Global Edition). Pearson Education Limited, Edinburgh Gate Harlow Essex CM20 2JE England (2015)

  28. Omerdic, E., Roberts, G.: Thruster fault diagnosis and accommodation for open-frame underwater vehicles. Control Eng. Pract. 12(12), 1575–1598 (2004)

    Article  Google Scholar 

  29. Fossen, T.I.: Handbook of Marine Craft Hydrodynamics and Motion Control. Wiley (2011)

  30. 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 

  31. Wang, N., Karimi, H.R.: Successive waypoints tracking of an underactuated surface vehicle. IEEE Trans. Ind. Inf. 16(2), 898–908 (2020)

    Article  Google Scholar 

  32. Lu, Y., Zhang, G., Sun, Z., Zhang, W.: Robust adaptive formation control of underactuated autonomous surface vessels based on MLP and DOB. Nonlinear Dyn. 94, 503–519 (2018)

    Article  MATH  Google Scholar 

  33. Yu, C., Xiang, X., Wilson, P.A., Zhang, Q.: Guidance-error-based robust fuzzy adaptive control for bottom following of a flight-style auv with saturated actuator dynamics. IEEE Trans. Cybern. 50(5), 1887–1899 (2020)

    Article  Google Scholar 

  34. Tanakitkorn, K., Wilson, P.A., Turnock, S.R., Phillips, A.B.: Depth control for an over-actuated, hover-capable autonomous underwater vehicle with experimental verification. Mechatronics 41, 67–81 (2017)

    Article  Google Scholar 

  35. Agarwal, R.P., Hodis, S., O’Regan, D.: 500 Examples and Problems of Applied Differential Equations. Springer, Berlin (2019)

    Book  MATH  Google Scholar 

  36. Batlle, J., Ridao, P., Garcia, R., Carreras, M., Cufi, X., El-Fakdi, A., Ribas, D., Nicosevici, T., Batlle, E., Oliver, G., et al.: URIS: Underwater robotic intelligent system. Autom. Marit. Ind. 177–203 (2005)

  37. Sun, B., Zhu, D., Ding, F., Yang, S.X.: A novel tracking control approach for unmanned underwater vehicles based on bio-inspired neurodynamics. J. Mar. Sci. Technol. 18(1), 63–74 (2013)

  38. Qiao, L., Zhang, W.: Adaptive second-order fast nonsingular terminal sliding mode tracking control for fully actuated autonomous underwater vehicles. IEEE J. Ocean. Eng. 44(2), 363–385 (2019)

    Article  Google Scholar 

  39. Tang, X., Zhai, D., Li, X.: Adaptive fault-tolerance control based finite-time backstepping for hypersonic flight vehicle with full state constrains. Inf. Sci. 507, 53–66 (2020)

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

The authors would like to thank anonymous reviewers for their invaluable comments on the improvement of this paper.

Funding

This work was supported in part by the National Natural Science Foundation of China under Grants 51909161 and 52071153, in part by the Natural Science Foundation of Shanghai under Grant 22ZR1434600, in part by the Shanghai Sailing Program under Grant 19YF1424100, in part by the Open Research Fund of Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources under Grant MESTA-2020-B008, and in part by the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University under Grants SL2022MS016 and 2020QY10.

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Correspondence to Lian Lian or Xianbo Xiang.

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Yu, C., Zhong, Y., Lian, L. et al. Adaptive simplified surge-heading tracking control for underwater vehicles with thruster’s dead-zone compensation. Nonlinear Dyn 111, 13073–13088 (2023). https://doi.org/10.1007/s11071-023-08512-9

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