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A bioinspired neural dynamics-based approach to tracking control of autonomous surface vehicles subject to unknown ocean currents

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Abstract

This paper addresses the trajectory tracking control problem of an autonomous surface vehicle (ASV) subject to unknown ocean currents, where smooth and continuous velocity commands are desirable for safe and effective operation. A novel bioinspired approach is proposed by integrating three neural dynamics models into the conventional Lyapunov synthesis. The tracking controller is derived from the error dynamics analysis of the ASV and the stability analysis of the control system. A simple observer is proposed to estimate the unknown ocean currents, which only requires the position of the ASV. The overall control system under the controller and observer is rigorously proved to be asymptotically stable by a Lyapunov stability theory for cascaded systems. The most contribution is that the proposed tracking controller is capable of eliminating the sharp velocity jumps due to sudden tracking error changes and generating smooth and continuous control signals. In addition, it can deals with the situation with unknown ocean currents. The effectiveness and efficiency of the proposed approach are demonstrated through simulation and comparison studies.

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References

  1. Fossen TI (1994) Guidance and control of ocean vehicles. Wiley, Chichester

    Google Scholar 

  2. Manley JE (2008) Unmanned surface vehicles, 15 years of development. In: Proceedings of oceans 2008. Quebec City pp 1–4

  3. Ashrafiuon H, Muske KR, McNinch LC, Soltan RA (2008) Sliding-mode tracking control of surface vessels. IEEE Trans Ind Electron 55(11):4004–4012

    Article  Google Scholar 

  4. Lin CM, Chen CH (2006) Adaptive RCMAC sliding mode control for uncertain nonlinear systems. Neural Comput Appl 15(3–4):253–267

    Article  Google Scholar 

  5. McNinch LC, Ashrafiuon H (2011) Predictive and sliding mode cascade control for unmanned surface vessels. In: Proceedings of 2011 American Control Conference, San Francisco, USA, pp 184–189

  6. Sira-Ramirez H (2002) Dynamic second-order sliding mode control of the hovercraft vessel. IEEE Trans Control Syst Technol 10(6):860–865

    Article  Google Scholar 

  7. Ghommam J, Mnif F, Derbel N (2010) Global stabilisation and tracking control of underactuated surface vessels. IET Control Theory Appl 4(1):71–88

    Article  MathSciNet  Google Scholar 

  8. Pettersen KY, Nijmeijer H (1999) Global practical stabilization and tracking for an underactuated ship—a combined averaging and backstepping approach. Model Identif Control 20(4):189–199

    Article  Google Scholar 

  9. Zhang G, Yu L, Meng X, Zhang L (2011) Tracking control of underactuated ship based on partial state feedback scheme. In: Proceedings of 2011 IEEE/ICME international conference on complex medical engineering, Harbin pp 678–683

  10. Jagannathan S, Galan G (2003) One-layer neural-network controller with preprocessed inputs for autonomous underwater vehicles. IEEE Trans Veh Technol 52(5):1342–1355

    Article  Google Scholar 

  11. Sutton R, Craven PJ (1998) The ANFIS approach applied to AUV autopilot design. Neural Comput Appl 7(2):131–140

    Article  MATH  Google Scholar 

  12. Zhang LJ, Jia HM, Qi X (2011b) NNFFC-adaptive output feedback trajectory tracking control for a surface ship at high speed. Ocean Eng 38:1430–1438

    Article  Google Scholar 

  13. Lin CM, Tai CF, Chung CC (2014) Intelligent control system design for UAV using a recurrent wavelet neural network. Neural Comput Appl. doi:10.1007/s00521-012-1242-5

  14. Chen M, Ge SS, Cui R (2010) Adaptive NN tracking control overactuated ocean surface vessels. In: Proceedings of the 8th world congress on intelligent control and automation. Jinan pp 548–553

  15. Guerrero-González A, Garcia-Córdova F, Gilabert J (2011) A biologically inspired neural network for navigation with obstacle avoidance in autonomous underwater and surface vehicles. In: Proceedings of 2011 IEEE-Spain Oceans. Santander

  16. Bi FY, Wei YJ, Zhang JZ, Cao W (2010) Position-tracking control of underactuated autonomous underwater vehicles in the presence of unknown ocean currents. IET Control Theory Appl 11(11):2369–2380

    Article  MathSciNet  Google Scholar 

  17. Haghi P, Naraghi M, Vanini SAS (2007) Adaptive position and attitude tracking of an auv in the presence of ocean currents disturbances. In: Proceedings of 16th IEEE international conference on control applications, Singapore, pp 741–746

  18. Zhu D, Huang H, Yang SX (2013) Dynamic task assignment and path planning of multi-AUV system based on an improved self-organizing map and velocity synthesis method in three-dimensional underwater workspace. IEEE Trans Cybern 43(2):504–514

    Article  Google Scholar 

  19. Yang Y, Du J, Guo C, Li G (2011) Trajectory tracking control of nonlinear full actuated ship with disturbances. In: Proceedings of 2011 International conference on soft computing and pattern recognition. Dalian, pp 318–323

  20. Tee KP, Ge SS (2006) Control of fully actuated ocean surface vessels using a class of feedforward approximators. IEEE Trans Control Syst Technol 14(4):750–756

    Article  Google Scholar 

  21. Ghommam J, Mnif F, Benali A, Poisson G (2007) Observer design for euler lagrange systems: application to path following control of an underactuated surface vessel. In: Proceedings of IEEE/RSJ international conference on intelligent robots and systems. San Diego, pp 2883–2888

  22. Radke A, Gao Z (2006) A survey of state and disturbance observers for practitioners. In: Proceedings of American control conference. Minneapolis, pp 5183–5188

  23. Khalil HK (2008) High-gain observers in nonlinear feedback control. In: Proceedings of international conference on control, automation and systems, Seoul, pp xlvii-lvii

  24. Batista P, Silvestre C, Oliveira P (2009) Position and velocity navigation systems for unmanned vehicles. IEEE Trans Control Syst Technol 17(3):707–715

    Article  Google Scholar 

  25. Jaffre FM, Austin TC, Allen BG, Stokey R, Von Alt CJ (2005) Ultra short baseline acoustic receiver/processor. In: Proceedings of ocean-Europe, pp 1382–1385

  26. Hodgkin AL, Huxley AF (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol 117:500–554

    Article  Google Scholar 

  27. Grossberg S (1988) Nonlinear neural networks: principles, mechanisms, and architecture. Neural Netw 1:17–61

    Article  Google Scholar 

  28. Öğmen H, Gagné S (1990) Neural network architectures for motion perception and elementary motion detection in the fly visual system. Neural Netw 3:487–505

    Article  Google Scholar 

  29. Willms AR, Yang SX (2008) Real-time robot path planning via a distance-propagating dynamic system with obstacle clearance. IEEE Trans Syst Man Cybern B Cybern 38(3):884–893

    Article  Google Scholar 

  30. Yang SX, Meng MQH (2001) Neural network approaches to dynamic collision-free trajectory generation. IEEE Trans Syst Man Cybern B Cybern 31(3):302–318

    Article  MathSciNet  Google Scholar 

  31. Yang SX, Zhu A, Yuan G, Meng MH (2012) A bioinspired neurodynamics based approach to tracking control of mobile robots. IEEE Trans Ind Electron 59(8):3211–3220

    Article  Google Scholar 

  32. Luo C, Yang SX (2008) A bioinspired neural network for real-time concurrent map building and complete coverage robot navigation in unknown environments. IEEE Trans Neural Netw 19(7):1279–1298

    Article  Google Scholar 

  33. Yuan X, Yang SX (2007) Multi-robot-based nanoassembly planning with automated path generation. IEEE/ASME Trans Mechatron 12(3):352–356

    Article  Google Scholar 

  34. Yang SX, Meng MQH (2001b) Neural network approaches to dynamic collision-free trajectory generation. IEEE Trans Syst Man Cybern B Cybern 31(3):302–318

    Article  MathSciNet  Google Scholar 

  35. Sun B, Zhu D, Yang SX (2014) A bioinspired filtered backstepping tracking control of 7000-m manned submarine vehicle. IEEE Trans Ind Electron 61(7):3682–3693

    Article  Google Scholar 

  36. Panteley E, Lefeber E, Lorıa A, Nijmeijer H (1998) Exponential tracking control of a mobile car using a cascaded approach. In: Proceedings of IFAC workshop on motion control. Grenoble, pp 221–226

Download references

Acknowledgments

The authors would like to thank the anonymous reviewers for the valuable suggestions and comments. This work was supported in part by the National Science Foundation of China under Grants 61074112 and 61403135, the Scientific Research Fund of Hunan Provincial Education Department under Grant 14C0440, and Natural Sciences and Engineering Research Council (NSERC) Discovery Grant of Canada.

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Authors and Affiliations

  1. School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, 411201, Hunan, China

    Changzhong Pan

  2. School of Automation, China University of Geosciences, Wuhan, 430074, Hubei, China

    Xuzhi Lai & Min Wu

  3. School of Engineering, University of Guelph, Guelph, N1G 2W1, ON, Canada

    Simon X. Yang

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  1. Changzhong Pan
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  2. Xuzhi Lai
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  3. Simon X. Yang
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  4. Min Wu
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Correspondence to Xuzhi Lai.

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Pan, C., Lai, X., Yang, S.X. et al. A bioinspired neural dynamics-based approach to tracking control of autonomous surface vehicles subject to unknown ocean currents. Neural Comput & Applic 26, 1929–1938 (2015). https://doi.org/10.1007/s00521-015-1839-6

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  • DOI: https://doi.org/10.1007/s00521-015-1839-6

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