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Direct and composite iterative neural control for cooperative dynamic positioning of marine surface vessels

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Abstract

This paper considers the cooperative dynamic positioning of multiple marine surface vessels in the presence of dynamical uncertainty and time-varying ocean disturbances. The objective is to enable a group of vessels to automatically position themselves in a desired time-varying formation and track a reference position. By employing a dynamic surface control technique, distributed adaptive controllers are derived on the basis of the information of neighboring vessels. Neural network with iterative updating laws is used to accurately identify the dynamical uncertainty and time-varying ocean disturbances. Two types of adaptive laws are proposed and validated: (1) direct iterative updating laws based on the velocity tracking errors; (2) composite iterative updating laws based on the tracking errors and the prediction errors. For both cases, Lyapunov–Krasovskii functionals are employed to analyze the stability of the closed-loop network, and uniform ultimate boundedness of error signals are established. The key features of the proposed controllers are as follows. First, the information exchanges are reduced by employing a distributed control strategy. Second, by using the iterative updating laws, the mixed uncertainty including the internal model uncertainty and external time-varying ocean disturbances can be compensated. Besides, the proposed controllers are easier to implement in digital processors with the derivative-free updating laws. Third, the prediction errors and tracking errors are combined to construct the composite iterative neural control laws, which are able to achieve faster adaptation and improved performance. Comparison studies are given to show the effectiveness of the proposed methods.

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References

  1. Sørensen, A.J.: A survey of dynamic positioning control systems. Annu. Rev. Control 35(1), 123–136 (2011)

    Article  Google Scholar 

  2. Balchen, J.G., Jenssen, N.A., Sælid, S.: Dynamic positioning using Kalman filtering and optimal control theory. In: IFAC/IFIP Symposium on Automation in Offshore Oil Field Operation, pp. 183–186 (1976)

  3. Saelid, S., Jenssen, N.A., Balchen, J.G.: Design and analysis of a dynamic positioning system based on Kalman filtering and optimal control. IEEE Trans. Autom. Control 28(3), 331–339 (1983)

    Article  Google Scholar 

  4. Fossen, T.I., Strand, J.P.: Passive nonlinear observer design for ships using Lyapunov methods: fullscale experiments with a supply vessel. Automatica 35(1), 3–16 (1999)

    Article  MathSciNet  Google Scholar 

  5. Du, J., Inoue, Y.: Computer simulations on thruster assisted fuzzy control dynamic positioning system of floating body. Behav. Offshore Struct. 2, 743–753 (1995)

    Google Scholar 

  6. Chen, M., Jiang, B.: Adaptive control and constrained control allocation for overactuated ocean surface vessels. Int. J. Syst. Sci. 44(12), 2295–2309 (2013)

    Article  MathSciNet  Google Scholar 

  7. Witkowska, A.: Dynamic positioning system with vectorial backstepping controller. In: 18th International Conference on Methods and Models in Automation and Robotics, pp. 842–847(2013)

  8. Agostinho A.C., Moratelli J., Morishita H.M.: Sliding mode control applied to offshore dynamic positioning systems. In: Proceedings of the IFAC International Conference on Manoeuvring (2009)

  9. Tannuri, E.A., Agostinho, A.C., Morishita, H.M.: Dynamic positioning systems: an experimental analysis of sliding mode control. Control Eng. Pract. 18(6), 1121–1232 (2010)

    Article  Google Scholar 

  10. Hespanha, J.P., Morse, A.S.: Switching between stabilizing controllers. Automatica 38(11), 1905–1917 (2002)

    Article  MathSciNet  Google Scholar 

  11. Fang, S., Blanke, M.: Fault monitoring and fault recovery control for position moored tanker. In: Proceedings of the 7th Workshop on Advanced Control and Diagnosis (2009)

  12. Lapierre, L., Soetanto, D., Pascoal A.: Coordinated motion control of marine robots. In: Proceedings of the 6th IFAC Conference on Maneuvering and Control of Marine Craft (2003)

  13. Cui, R.X., Yan, W.S., Xu, D.M.: Synchronization of multiple autonomous underwater vehicles without velocity measurements. Sci. China: Inf. Sci. 55(7), 1693–1703 (2012)

    MathSciNet  Google Scholar 

  14. Cui, R.X., Ren, B., Ge, S.S.: Synchronised tracking control of multi-agent system with high-order dynamics. IET Control Theory Appl. 6(5), 603–614 (2012)

    Article  MathSciNet  Google Scholar 

  15. Peng, Z.H., Wang, D., Liu, H.T.H., Sun, G., Wang, H.: Distributed robust state and output feedback controller designs for rendezvous of networked autonomous surface vehicles using neural networks. Neurocomputing 115(4), 130–141 (2013)

    Article  Google Scholar 

  16. Peng, Z.H., Wang, D., Li, T.S., Wu, Z.L.: Leaderless and leader-follower cooperative control of multiple marine surface vehicles with unknown dynamics. Nonlinear Dyn. 74(1–2), 95–106 (2013)

    Article  MathSciNet  Google Scholar 

  17. Chen, M., Wu, Q.X., Jiang, C.S.: Disturbance-observer-based robust synchronization control of uncertain chaotic systems. Nonlinear Dyn. 70(4), 2421–2432 (2012)

    Article  MathSciNet  Google Scholar 

  18. Zhang, H.W., Lewis, F.L., Qu, Z.H.: Lyapunov, adaptive, and optimal design techniques for cooperative systems on directed communication graphs. IEEE Trans. Ind. Electron. 59(7), 3026–3041 (2012)

    Article  Google Scholar 

  19. Zhang, H.W., Lewis, F.L.: Synchronization of nonlinear cooperative systems with unknown dynamics using neural adaptive control. Automatica 48(7), 1432–1439 (2012)

    Article  MathSciNet  Google Scholar 

  20. Li, Z.K., Ren, W., Liu, X.D., Xie, L.H.: Distributed consensus of linear multi-agent systems with adaptive dynamic protocols. Automatica 49(7), 1986–1995 (2013)

    Article  MathSciNet  Google Scholar 

  21. Li, Z.K., Duan, Z.S., Lewis, F.L.: Distributed robust consensus control of multi-agent systems with heterogeneous matching uncertainties. Automatica 50(3), 883–889 (2014)

  22. Hong, Y.G., Wang, X.L., Jiang, Z.P.: Distributed output regulation of leader-follower multi-agent systems. Int. J. Robust Nonlinear Control 23(1), 48–66 (2013)

    Article  MathSciNet  Google Scholar 

  23. Chen, W.S., Wen, C.Y., Hua, S.Y., Sun, C.Y.: Distributed cooperative adaptive identification and control for a group of continuous-time systems with a cooperative PE condition via consensus. IEEE Trans. Autom. Control 59(1), 91–106 (2014)

    Article  MathSciNet  Google Scholar 

  24. Chen, W.S., Li, X.B., Jiao, L.C.: Quantized consensus of second-order continuous-time multi-agent systems with a directed topology via sampled data. Automatica 49(7), 2236–2242 (2013)

    Article  MathSciNet  Google Scholar 

  25. Wen, G.H., Duan, Z.S., Chen, G.R., Yu, W.W.: Consensus tracking of multi-agent systems with lipschitz-type node dynamics and switching topologies. IEEE Trans. Circuits Syst. I: Regul. Pap. 61(2), 499–511 (2014)

    Article  MathSciNet  Google Scholar 

  26. Wen, G.H., Hu, G.Q., Yu, W.W., Chen, G.R.: Distributed \(H_\infty \) consensus of higher order multiagent systems with switching topologies. IEEE Trans. Circuits Syst. 61(5), 359–363 (2014)

    Google Scholar 

  27. Xia, G.Q., Shi, X.C., Fu, M.Y., Wang, H.J., Bian, X.Q.: Design of dynamic positioning systems using hybrid CMAC-based PID controller for a ship. In: IEEE International Conference on Mechatronics and Automation, vol. 2, pp. 825–830 (2005)

  28. Chen, M., Ge, S.Z.S., How, B.V.E., Yoo, S.C.: Robust adaptive position mooring control for marine vessels. IEEE Trans. Control Syst. Technol. 21(2), 395–409 (2013)

    Article  Google Scholar 

  29. Dai, S.L., Wang, C., Luo, F.: Learning control of uncertain ocean surface ship dynamics using neural networks. In: IEEE 5th International Conference on Cybernetics and Intelligent Systems, pp. 380–385 (2011)

  30. Sun, G., Wang, D., Li, T.S., Peng, Z.H., Wang, H.: Single neural network approximation based adaptive control for a class of uncertain strict-feedback nonlinear systems. Nonlinear Dyn. 72(1–2), 175–184 (2013)

    Article  MathSciNet  Google Scholar 

  31. Fei, J., Zhou, J.: Robust adaptive control of MEMS triaxial gyroscope using fuzzy compensator. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 42(6), 1599–1607 (2012)

    Article  Google Scholar 

  32. Fei, J., Ding, H.: Adaptive sliding mode control of dynamic system using RBF neural network. Nonlinear Dyn. 70(2), 1563–1573 (2012)

    Article  MathSciNet  Google Scholar 

  33. Li, T.S., Li, R.H., Li, J.F.: Decentralized adaptive neural control of nonlinear systems with unknown time delays. Nonlinear Dyn. 67(3), 2017–2026 (2012)

    Article  Google Scholar 

  34. Li, T.S., Wang, D., Chen, N.X.: Adaptive fuzzy control of uncertain MIMO non-linear systems in block-triangular forms. Nonlinear Dyn. 63(1–2), 105–123 (2011)

    Article  MathSciNet  Google Scholar 

  35. Tong, S.C., Li, Y.M.: Adaptive fuzzy output feedback tracking backstepping control of strict-feedback nonlinear systems with unknown dead zones. IEEE Trans. Fuzzy Syst. 20(1), 168–180 (2012)

    Article  Google Scholar 

  36. Tong, S.C., Huo, B.Y., Li, Y.M.: Observer-based adaptive decentralized fuzzy fault-tolerant control of nonlinear large-scale systems with actuator failures. IEEE Trans. Fuzzy Syst. 22(1), 1–15 (2014)

    Article  Google Scholar 

  37. Li, Y.M., Tong, S.C., Li, T.S.: Adaptive fuzzy backstepping control of static var compensator based on state observer. Nonlinear Dyn. 73(1–2), 133–142 (2013)

    Article  MathSciNet  Google Scholar 

  38. Swaroop, D., Hedrick, J.K., Yip, P.P., Gerdes, J.C.: Dynamic surface control for a class of nonlinear system. IEEE Trans. Autom. Control 45(10), 1893–1899 (2000)

    Article  MathSciNet  Google Scholar 

  39. Wang, D., Huang, J.: Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form. IEEE Trans. Neural Netw. 6(1), 195–202 (2005)

    Article  Google Scholar 

  40. Yucelen, T., Calise, A.J.: Derivative-Free model reference adaptive control. J. Guid. Control Dyn. 34(4), 933–950 (2011)

  41. Wen, C., Fahmida, N.C.: Simultaneous identification of time-varying parameters and estimation of system states using iterative learning observers. Int. J. Syst. Sci. 38(1), 39–45 (2007)

    Article  Google Scholar 

  42. Skjetne, R., Fossen, T.I., Kokotovic, P.V.: Adaptive maneuvering, with experiments, for a model ship in a marine control laboratory. Automatica 41(2), 289–298 (2005)

    Article  MathSciNet  Google Scholar 

  43. Fossen T.I.: How to incorporate wine, waves and ocean currents in the marine craft equations of motion. In: Proceedings of the IFAC Conference on Manoeuvring and Control of Marine Craft, pp. 19–21 (2012)

Download references

Acknowledgments

This work was supported in part by the National Nature Science Foundation of China under Grants 61273137, 51209026, and in part by the Scientific Research Fund of Liaoning Provincial Education Department under Grant L2013202, and in part by the Fundamental Research Funds for the Central Universities under Grants 3132015021, 3132014321.

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Authors and Affiliations

  1. School of Marine Engineering, Dalian Maritime University, Dalian, 116026, People’s Republic of China

    Lu Liu, Dan Wang & Zhouhua Peng

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  1. Lu Liu
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  2. Dan Wang
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  3. Zhouhua Peng
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Correspondence to Dan Wang.

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Liu, L., Wang, D. & Peng, Z. Direct and composite iterative neural control for cooperative dynamic positioning of marine surface vessels. Nonlinear Dyn 81, 1315–1328 (2015). https://doi.org/10.1007/s11071-015-2071-8

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  • DOI: https://doi.org/10.1007/s11071-015-2071-8

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