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Data-driven Model Free Formation Control for Multi-USV System in Complex Marine Environments

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Abstract

Formation control of multi-USV system in complex marine environments is investigated in this study, and a data-driven disturbance observer (DDO)-based model free adaptive control (MFAC) framework is suggested. The FMAC scheme is designed by using only the input and output data of complex USV dynamics, including the calculation of pseudo-Jacobian matrix (PJM) and the calculation of control law. To avoid the problem that the MFAC method cannot be directly applied to USV systems, an improved DDO is used to estimate the complex environmental disturbances and PJM estimation errors. Further, forgetting factor based MFAC (FMFAC) is proposed to avoid overshoot caused by redundant data. Then, the PJM estimation is proved to be accurate while the control structure of DDO-FMFAC is proved to be bounded-input and bounded-output (BIBO). Finally, the performances of the proposed method including effectiveness and robustness are shown in simulation results.

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References

  1. T. Fossen, Handbook of Marine Craft Hydrodynamics and Motion Control, John Wiley & Sons Ltd, Chichester, UK, 2011.

    Book  Google Scholar 

  2. Y. Liao, Z. Jia, W. Zhang, Q. Jia, and Y. Li, “Layered berthing method and experiment of unmanned surface vehicle based on multiple constraints analysis,” Applied Ocean Research, vol. 86, pp. 47–60, May 2019.

    Article  Google Scholar 

  3. H. Sang, Y. You, X. Sun, Y. Zhou, and F. Liu, “The hybrid path planning algorithm based on improved A* and artificial potential field for unmanned surface vehicle formations,” Ocean Engineering, vol. 223, pp. 1–16, March 2021.

    Article  Google Scholar 

  4. B. Qiu, G. Wang, Y. Fan, D. Mu, and X. Sun, “Path following of underactuated unmanned surface vehicle based on trajectory linearization control with input saturation and external disturbances,” International Journal of Control, Automation, and Systems, vol. 18, no. 8, pp. 2108–2119, March 2020.

    Article  Google Scholar 

  5. N. Gu, D. Wang, Z. Peng, and L. Liu, “Distributed containment maneuvering of uncertain under-actuated unmanned surface vehicles guided by multiple virtual leaders with a formation,” Ocean Engineering, vol. 187, pp. 1–10, September 2019.

    Article  Google Scholar 

  6. Y. Lu, G. Zhang, Z. Sun, and W. Zhang, “Robust adaptive formation control of underactuated autonomous surface vessels based on MLP and DOB,” Nonlinear Dynamics, vol. 94, no. 9, pp. 503–519, June 2018.

    Article  MATH  Google Scholar 

  7. K. Do, “Synchronization motion tracking control of multiple underactuated ships with collision avoidance,” IEEE Transactions on Industrial Electronics, vol. 63, no. 5, pp. 2976–2989, May 2016.

    Article  MathSciNet  Google Scholar 

  8. M. Mirzaei, N. Meskin, and F. Abdollahi, “Robust consensus of autonomous underactuated surface vessels,” IET Control Theory & Applications, vol. 11, no. 4, pp. 486–494, January 2017.

    Article  MathSciNet  Google Scholar 

  9. M. Ran and L. Xie, “Practical output consensus of nonlinear heterogeneous multi-agent systems with limited data rate,” Automatica, vol. 129, p. 109624, July 2021.

    Article  MathSciNet  MATH  Google Scholar 

  10. B. Park and S. Yoo, “Connectivity-maintaining and collision-avoiding performance function approach for robust leader-follower formation control of multiple uncertain underactuated surface vessels,” Automatica, vol. 127, p. 109501, May 2021.

    Article  MathSciNet  MATH  Google Scholar 

  11. B. Park and S. Yoo, “Adaptive-observer-based formation tracking of networked uncertain underactuated surface vessels with connectivity preservation and collision avoidance,” Journal of the Franklin Institute, vol. 356, no. 15, pp. 7947–7966, October 2019.

    Article  MathSciNet  MATH  Google Scholar 

  12. Z. Liu, H. Hou, and Y. Wang, “Formation-containment control of multiple underactuated surface vessels with sampling communication via hierarchical sliding mode approach,” ISA Transactions, vol. 124, pp. 458–467, 2022.

    Article  Google Scholar 

  13. S. Gao, Z. Peng, L. Liu, H. Wang, and D. Wang, “Coordinated target tracking by multiple unmanned surface vehicles with communication delays based on a distributed event-triggered extended state observer,” Ocean Engineering, vol. 227, pp. 1–12, May 2021.

    Article  Google Scholar 

  14. D. Wang, S. Ge, M. Fu, and D. Li, “Bioinspired neurodynamics based formation control for unmanned surface vehicles with line-of-sight range and angle constraints,” Neurocomputing, vol. 425, no. 15, pp. 127–134, February 2021.

    Google Scholar 

  15. J. Ghommem, F. Mnif, G. Poisson, and N. Derbel, “Nonlinear formation control of a group of underactuated ships,” Proc. of Oceans 2007 — Europe, IEEE, June 2007.

  16. J. Ghommem and F. Mnif, “Coordinated path-following control for a group of underactuated surface vessels,” IEEE Transactions on Industiral Electronics, vol. 56, no. 10, pp. 3951–3963, October 2009.

    Article  Google Scholar 

  17. I. F. Ihle, J. Jouffroy, and T. Fossen, “Formation control of marine surface craft: A Lagrangian approach,” IEEE Journal of Oceanic Engineering, vol. 31, no. 4, pp. 922–934, October 2006.

    Article  MATH  Google Scholar 

  18. C. Huang, X. Zhang, and G. Zhang, “Improved decentralized finite-time formation control of underactuated USVs via a novel disturbance observer,” Ocean Engineering, vol. 174, no. 4, pp. 117–124, February 2019.

    Article  Google Scholar 

  19. S. Dian, L. Chen, S. Hoang, T. Zhao, and J. Tan, “Gain scheduled dynamic surface control for a class of underactuated mechanical systems using neural network disturbance observer,” Neurocomputing, vol. 275, pp. 1998–2008, January 2018.

    Article  Google Scholar 

  20. J. Li, H. Jahanshahi, S. Kacar, T. Zhao, Y. Chu, J. Aguilar, N. Alotaibi, and K. Alharbi, “On the variable-order fractional memristor oscillator: Data security applications and synchronization using a type-2 fuzzy disturbance observer-based robust control,” Chaos, Solitons & Fractals, vol. 145, pp. 1–13, April 2021.

    Article  MathSciNet  MATH  Google Scholar 

  21. J. Lai, X. Yin, X. Yin, and L. Jiang, “Fractional order harmonic disturbance observer control for three-phase LCL-type inverter,” Control Engineering Practice, vol. 107, pp. 1–13, December 2021.

    Article  Google Scholar 

  22. X. Ma, J. Zhang, and J. Wang, “Design of disturbance observer based sliding mode control for fuzzy systems,” IFAC-PapersOnLine, vol. 50, no. 1, pp. 717–722, Februay 2021.

    Google Scholar 

  23. Z. Hou and S. Jin, “Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems,” IEEE Transactions on Neural Networks, vol. 22, no. 12, pp. 2173–2188, December 2011.

    Article  Google Scholar 

  24. K. Duan, S. Fong, and C. Chen, “Multilayer neural networks-based control of underwater vehicles with uncertain dynamics and disturbances,” Nonlinear Dynamics, vol. 100, pp. 3555–3573, June 2020.

    Article  Google Scholar 

  25. M. Campi and S. Savaresi, “Direct nonlinear control design: The virtual reference feedback tuning (VRFT) approach,” IEEE Transactions on Automatic Control, vol. 51, no. 1, pp. 14–27, January 2006.

    Article  MathSciNet  MATH  Google Scholar 

  26. J. Helvoort, B. Jager, and M. Steinbuch, “Data-driven multivariable controller design using ellipsoidal unfalsified control,” Proc. of American Control Conference, pp. 510–515, 2007.

  27. Z. Wang and J. Wang, “Ultra-local model predictive control: A model-free approach and its application on automated vehicle trajectory tracking,” Control Engineering Practice, vol. 101, pp. 1–14, August 2020.

    Article  Google Scholar 

  28. W. Zhang, D. Xu, B. Jiang, and T. Pan, “Prescribed performance based model-free adaptive sliding mode constrained control for a class of nonlinear systems,” Information Sciences, vol. 544, pp. 97–116, January 2021.

    Article  MathSciNet  MATH  Google Scholar 

  29. Z. Wang, D. He, X. Zhu, J. Luo, Y. Liang, and X. Wang, “Data-driven model-free adaptive control of particle quality in drug development phase of spray fluidized-bed granulation process,” Complexity, vol. 2017, pp. 1–17, December 2017.

    MATH  Google Scholar 

  30. N. Ebrahimi, S. Ozgoli, and A. Ramezani, “Model free sliding mode controller for blood glucose control: Towards artificial pancreas without need to mathematical model of the system,” Computer Methods and Programs in Biomedicine, vol. 195, pp. 1–10, December 2020.

    Article  Google Scholar 

  31. S. Ge, C. Yang, Y. Li, and H. L. Tong, “Decentralized adaptive control of a class of discrete-time multi-agent systems for hidden leader following problem,” Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5065–5070, 2020.

  32. F. Del-Rio-Rivera, V. Ramirez, A. Donaire, and J. Ferguson, “Robust trajectory tracking control for fully actuated marine surface vehicle,” IEEE Access, vol. 8, pp. 223897–223904, December 2020.

    Article  Google Scholar 

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Authors and Affiliations

  1. College of the Electrical Engineering of Yanshan University, Qinhuangdao, Hebei, 066004, China

    Hongbin Wang, Qianda Luo, Ning Li & Wei Zheng

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  1. Hongbin Wang
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  2. Qianda Luo
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  4. Wei Zheng
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Correspondence to Qianda Luo.

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Hongbin Wang received his Bachelor’s and Master’s degrees in automation from Northeast Heavy Machinery Institute, Qinhuangdao, China, and Yanshan University, Qinhuangdao, China, and a Ph.D. degree in control theory and control engineering from Yanshan University, Qinhuangdao, China, in 1988, 1993, and 2005, respectively. His current research interests include process automation, robot control technology, variable structure control system, robust control, and visual servo.

Qianda Luo received his Master’s degree from the School of Electrical Engineering, North China University of Science and Technology. Currently, he is studying for a Ph.D. degree in the School of Electrical Engineering, Yanshan University, majoring in control theory and control engineering. His research interests include multiagent control, data-driven control, and optimal control.

Ning Li received his B.S. degree in 2017. Currently, he is a Ph.D. student based in control theory and control engineering at Yanshan University. His research interests include nonlinear control, unmanned aerial vehicle control, and multiagent control.

Wei Zheng received her Ph.D. degree in control science and engineering from the School of Electrical Engineering, Yanshan University, Qinhuangdao, China, in 2020. She has been with the Yanshan University, Qinghuangdao, China. Her current research interests include multiagent systems and adaptive control-time-delay systems.

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Wang, H., Luo, Q., Li, N. et al. Data-driven Model Free Formation Control for Multi-USV System in Complex Marine Environments. Int. J. Control Autom. Syst. 20, 3666–3677 (2022). https://doi.org/10.1007/s12555-021-0593-z

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  • DOI: https://doi.org/10.1007/s12555-021-0593-z

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