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Robust path-following control of the underactuated AUV with multiple uncertainties using combined UDE and UKF

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Abstract

In the complex ocean environment, the autonomous underwater vehicle (AUV) is subject to the multiple uncertainties including deterministic uncertainties and stochastic uncertainties, which greatly degrade the control performance. To solve this problem, a novel robust control strategy using combined uncertainty and disturbance estimator (UDE) and unscented Kalman filter (UKF) is proposed for the path-following control (PFC) of the underactuated AUV under multiple uncertainties in this paper. First, the UDE technique is adopted to handle the deterministic uncertainties, including deterministic components of environmental disturbances, parameter uncertainties, and unmodeled dynamics, etc. Second, the uncertainty of unactuated lateral channel, which cannot be dealt with by the UDE, is treated as an unknown parameter. Also, to tackle the stochastic uncertainties, which is often ignored in previous studies, including stochastic components of environmental disturbances, measurement noise, and the estimation errors of UDE which are treated as the process noise, the augmented UKF technique is adopted to jointly estimate the system states and the lateral channel unknown parameter. Finally, extensive numerical simulations and comparative analyses are presented to verify the efficiency and robustness of the proposed control strategy.

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References

  1. Karkoub M, Wu HM, Hwang CL (2017) Nonlinear trajectory-tracking control of an autonomous underwater vehicle. Ocean Eng 145:188–198

    Article  Google Scholar 

  2. Cho GR et al (2020) Robust trajectory tracking of autonomous underwater vehicles using back-stepping control and time delay estimation. Ocean Eng 201:107131

    Article  Google Scholar 

  3. Nhut Thanh PN, Tam PM, Huy Anh HP (2021) A new approach for three-dimensional trajectory tracking control of under-actuated AUVs with model uncertainties. Ocean Eng 228:108951

    Article  Google Scholar 

  4. Xiao C et al (2018) Adaptive sliding-mode path following control system of the underactuated USV under the influence of ocean currents. J Syst Eng Electron 29(6):1271

    Article  Google Scholar 

  5. Liu L et al (2021) Active disturbance rejection path following control of USV based on fuzzy optimization. In: 2021 6th International Conference on Automation, Control and Robotics Engineering (CACRE), pp 521–527

  6. Kim S, Cho H, Jung D (2021) Robust path following control via command-filtered backstepping scheme. Int J Aeronaut Space Sci 22:1141–1153

    Article  Google Scholar 

  7. Harun N, Zain ZM, Noh MM (2017) PSO approach for a PID back-stepping control method in stabilizing an underactuated X4-AUV[C]//2017. In: IEEE 7th international conference on underwater system technology: theory and applications (USYS). IEEE, pp 1–6

  8. Khodayari MH, Balochian S (2015) 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. https://doi.org/10.1007/s00773-015-0312-7

    Article  Google Scholar 

  9. Zhang GC, Huang H, Qin HD et al (2018) A novel adaptive second order sliding mode path following control for a portable AUV. Ocean Eng 151(Mar):82–92

    Google Scholar 

  10. Shen C, Shi Y, Buckham B (2017) Trajectory tracking control of an autonomous underwater vehicle using Lyapunov-based model predictive control. IEEE Trans Ind Electron 65:5796–5805

    Article  Google Scholar 

  11. Yan Z, Liu X, Zhou J et al (2018) Coordinated target tracking strategy for multiple unmanned underwater vehicles with time delays. IEEE Access 6:1–1

    Google Scholar 

  12. Lapierre L, Soetanto D (2007) Nonlinear path-following control of an AUV. Ocean Eng 34(11–12):1734–1744

    Article  Google Scholar 

  13. Zhang JL et al (2021) Approach-angle-based three-dimensional indirect adaptive fuzzy path following of under-actuated AUV with input saturation. Appl Ocean Res 107:13

    Article  Google Scholar 

  14. von Ellenrieder KD (2019) Dynamic surface control of trajectory tracking marine vehicles with actuator magnitude and rate limits. Automatica 105:433–442

    Article  MathSciNet  MATH  Google Scholar 

  15. Miao J et al (2017) Spatial curvilinear path following control of underactuated AUV with multiple uncertainties. ISA Trans 67:107–130

    Article  Google Scholar 

  16. Talole SE, Ghosh A, Phadke SB (2006) Proportional navigation guidance using predictive and time delay control. Control Eng Pract 14(12):1445–1453

    Article  Google Scholar 

  17. Xu J-X, Cao W-J (2000) Synthesized sliding mode and time-delay control for a class of uncertain systems. Automatica 36(12):1909–1914

    Article  MathSciNet  MATH  Google Scholar 

  18. Nejatbakhsh-Esfahani H, Azimirad V, Danesh M (2015) A time delay controller included terminal sliding mode and fuzzy gain tuning for underwater vehicle-manipulator systems. Ocean Eng 107:97–107

    Article  Google Scholar 

  19. Youcef-Toumi K, Ito O (1990) A time delay controller for systems with unknown dynamics. J Dyn Syst Meas Control 112(1):133–142

    Article  MATH  Google Scholar 

  20. Zhong Q-C, Rees D (2004) Control of uncertain LTI systems based on an uncertainty and disturbance estimator. J Dyn Syst Meas Control 126:905

    Article  Google Scholar 

  21. Kuperman A, Zhong Q-C (2011) Robust control of uncertain nonlinear systems with state delays based on an uncertainty and disturbance estimator. Int J Robust Nonlinear Control 21(1):79–92

    Article  MathSciNet  MATH  Google Scholar 

  22. Deshpande VS, Phadke SB (2011) Control of uncertain nonlinear systems using an uncertainty and disturbance estimator. J Dyn Syst Meas Control 21(1):79–92

    Google Scholar 

  23. Talole SE, Phadke SB (2008) Model following sliding mode control based on uncertainty and disturbance estimator. J Dyn Syst Meas Control 130(3):034501

    Article  Google Scholar 

  24. Talole SE, Phadke SB (2009) Robust input–output linearisation using uncertainty and disturbance estimation. Int J Control 82(10):1794–1803

    Article  MathSciNet  MATH  Google Scholar 

  25. Ren B, Zhong Q-C, Chen J (2015) Robust control for a class of nonaffine nonlinear systems based on the uncertainty and disturbance estimator. IEEE Trans Ind Electron 62(9):5881–5888

    Article  Google Scholar 

  26. Bucy RS, Senne KD (1971) Digital synthesis of non-linear filters. Automatica 7(3):287–298

    Article  MATH  Google Scholar 

  27. Pan Quan Y, Liang Y, Yan L, Yongmei C (2005) A class of nonlinear filters—UKF overview. Control Decis Mak 20(5)

  28. Julier SJ, Uhlmann JK (1997) A new extension of the Kalman filter to nonlinear systems. In: Int. symp. Aerospace/defense Sensing, Simul and controls, 3(26): 182–193

  29. Julier SJ, Uhlmann JK (2004) Unscented filtering and nonlinear estimation. Proc IEEE 92(3):401–422

    Article  Google Scholar 

  30. Pan C, Gao J, Li Z et al (2021) Multiple fading factors-based strong tracking variational Bayesian adaptive Kalman filter. Measurement 176:109139

    Article  Google Scholar 

  31. Miao J et al (2017) Compound line-of-sight nonlinear path following control of underactuated marine vehicles exposed to wind, waves, and ocean currents. Nonlinear Dyn 89(4):2441–2459

    Article  MathSciNet  Google Scholar 

  32. Xiang X, Lapierre L, Jouvencel B (2015) Smooth transition of AUV motion control: from fully-actuated to under-actuated configuration. Robot Auton Syst 67:14–22

    Article  Google Scholar 

  33. Chang L, Xiaoluo J (2011) Kalman filter based on SVM innovation update for predicting state-of health of VRLA batteries. Applied informatics and communication. Springer, Berlin, pp 455–463

    Chapter  Google Scholar 

  34. Zhao X, Lu J, Yahagi T (2006) Nonlinear time series prediction using wavelet network with Kalman filter based algorithm. IEEJ Trans Electron Inform Syst 126(10):1255–1260

    Google Scholar 

  35. Lapierre L, Jouvencel B (2008) Robust nonlinear path-following control of an AUV. IEEE J Oceanic Eng 33(2):89–102

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (42227901, 52371358, 52305083), Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (311020011), Key-Area Research and Development Program of Guangdong Province (2020B1111010004), and the Special project for marine economy development of Guangdong Province (GDNRC [2022] 31).

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Correspondence to Chao Peng.

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Appendix 1: The model parameters of AUV

Appendix 1: The model parameters of AUV

Term

Description

\(x,y\)

Underactuated AUV’s positions

\(\psi\)

Heading angle

\(u\)

Surge speed

\(v\)

Sway speed

\(r\)

Yaw speed

\(\tau_{u} ,\tau_{r}\)

Control inputs

\(I_{z}\)

Moment of inertia

\(d_{u} ,d_{v} ,d_{r}\)

Lumped uncertainties

\(m_{11} ,m_{22} ,m_{33}\)

Combined total mass terms

\(X_{u} ,X_{{\dot{u}}} ,X_{u\left| u \right|} ,Y_{v} ,Y_{{\dot{v}}} ,Y_{v\left| v \right|} ,N_{r} ,N_{{\dot{r}}} ,N_{r\left| r \right|}\)

Nominal hydrodynamic parameters

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Miao, J., Liu, W., Peng, C. et al. Robust path-following control of the underactuated AUV with multiple uncertainties using combined UDE and UKF. J Mar Sci Technol 28, 804–818 (2023). https://doi.org/10.1007/s00773-023-00958-1

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