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SCIENCE CHINA Information Sciences, Volume 59, Issue 9: 092210(2016) https://doi.org/10.1007/s11432-015-5384-9

Predictor-based neural dynamic surface control for distributed formation tracking of multiple marine surface vehicles with improved transient performance

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  • ReceivedJan 16, 2016
  • AcceptedApr 15, 2016
  • PublishedAug 23, 2016

Abstract

In this paper, we investigate the distributed formation tracking problem of multiple marine surface vehicles with model uncertainty and time-varying ocean disturbances induced by wind, waves, and ocean currents. The objective is to achieve a collective tracking with a time-varying trajectory, which can only be accessed by a fraction of follower vehicles. A novel predictor-based neural dynamic surface control design approach is proposed to develop the distributed adaptive formation controllers. We use prediction errors, rather than tracking errors, to construct the neural adaptive laws, which enable the fast identification of the vehicle dynamics without incurring high-frequency oscillations in control signals. We establish the stability properties of the closed-loop network via Lyapunov analysis, and quantify the transient performance by deriving the truncated $L_2$ norms of the derivatives of neural weights, which we demonstrate to be smaller than the classical neural dynamic surface control design approach. We also extend the above result to the distributed formation tracking using the relative position information of vehicles, and the advantage is that the velocity information of neighbors and leader are required. Finally, we give the comparative studies to illustrate the performance improvement of the proposed method.


Acknowledgment

Acknowledgments

The authors would like to thank the reviewers for their constructive comments, which have improved the quality of this paper. This work was in part supported by National Nature Science Foundation of China (Grants Nos. 51209026, 51179019, 61273137, 51579023, 51579022), China Postdoctoral Science Foundation (Grant No. 2015M570247), Scientific Research Fund of Liaoning Provincial Education Department (Grant No. L2013202), and Fundamental Research Funds for the Central Universities (Grant Nos. 3132015021, 3132014321).


References

[1] Jadbabaie A, Lin J, Morse A S. Coordination of groups of mobile autonomous agents using nearest neighbor rules. IEEE Trans Autom Control, 2003, 48: 988-1001 CrossRef Google Scholar

[2] Fax J A, Murray R M. Information flow and cooperative control of vehicle formations. IEEE Trans Autom Control, 2004, 49: 1465-1476 CrossRef Google Scholar

[3] Skjetne R, Moi S, Fossen T I. Nonlinear formation control of marine craft. In: Proceedings of the 41st IEEE Conference on Decision and Control, Las Vegas, 2002, 2: 1699--1704. Google Scholar

[4] Ihle I, Arcak F M, Fossen T I. Passivity-based designs for synchronized path following. Automatica, 2007, 43: 1508-1518 CrossRef Google Scholar

[5] Almeida J, Silvestre C, Pascoal A M. Cooperative control of multiple surface vessels in the presence of ocean currents and parametric model uncertainty. Int J Robust Nonlin Control, } 2010, 20: 1549-1565 CrossRef Google Scholar

[6] Breivik M, Hovstein V E, Fossen T I. Ship formation control: a guided leader-follower approach. In: {Proceedings of the 17th IFAC World Congress}, COEX, 2008, 17: 16008--16014. Google Scholar

[7] Kyrkjeb{\o} E, Pettersen K Y, Wondergem M, et al. Output synchronization control of ship replenishment operations: Theory and experiments. Control Eng Pract, } 2007, 15: 741-755 CrossRef Google Scholar

[8] Cui R X, Ge S S, How B V E, et al. Leader-follower formation control of underactuated autonomous underwater vehicles. Ocean Eng, } 2010, 37: 1491-1502 CrossRef Google Scholar

[9] Peng Z H, Wang D, Hu X J. Robust adaptive formation control of underactuated autonomous surface vehicles with uncertain dynamics. IET Control Theory Appl, } 2011, 5: 1378-1387 CrossRef Google Scholar

[10] Peng Z H, Wang D, Chen Z Y, et al. Adaptive dynamic surface control for formations of autonomous surface vehicles with uncertain dynamics. IEEE Trans Control Syst Tech, } 2013, 21: 513-520 CrossRef Google Scholar

[11] Arrichiello F, Chiaverini S, Fossen T I. Formation control of underactuated surface vessels using the Null-Space-Based behavioral control. {In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems}, Beijing, 2006. 5942--5947. Google Scholar

[12] Ren W. Consensus strategies for cooperative control of vehicle formations. IET Control Theory Appl, } 2007, 1: 505-512 CrossRef Google Scholar

[13] Ren W, Sorensen N. Distributed coordination architecture for multi-robot formation control. Robot Auton Syst, } 2008, 56: 324-333 CrossRef Google Scholar

[14] Cao Y C, Ren W. Distributed formation control for fractional-order systems: dynamic interaction and absolute/relative damping. Syst Control Lett, } 2010, 59: 233-240 CrossRef Google Scholar

[15] Xiao F, Wang L, Chen J, et al. Finite-time formation control for multi-agent systems. Automatica, 2009, 45: 2605-2611 CrossRef Google Scholar

[16] Hong Y G, Hu J P, Gao L X. Tracking control for multi-agent consensus with an active leader and variable topology. Automatica, } 2006, 42: 1177-1182 CrossRef Google Scholar

[17] Hu G Q. Robust consensus tracking of a class of second-order multi-agent dynamic systems. Syst Control Lett, } 2012, 61: 134-142 CrossRef Google Scholar

[18] 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, 2013, 49: 2236-2242 CrossRef Google Scholar

[19] Zhang H W, Lewis F L. Adaptive cooperative tracking control of higher-order nonlinear systems with unknown dynamics. Automatica, } 2012, 48: 1432-1439 CrossRef Google Scholar

[20] Cui R X, Ren B B, Ge S S. Synchronised tracking control of multi-agent system with high-order dynamics. IET Control Theory Appl, } 2011, 6: 603-614 Google Scholar

[21] Chen W S, Li X B. Observer-based consensus of second-order multi-agent systems with fixed and stochastically switching topology via sampled data. Int J Robust Nonlin Control, } 2014, 24: 567-584 CrossRef Google Scholar

[22] Wang X L, Hong Y G, Huang J, et al. A distributed control approach to a robust output regulation problem for multi-agent linear systems. IEEE Trans Automatic Control, } 2010, 55: 2891-2895 CrossRef Google Scholar

[23] Hong Y G, Wang , X L, Jiang Z P. Distributed output regulation of leader-follower multi-agent systems. Int J Robust Nonlin Control, } 2013, 23: 48-66 CrossRef Google Scholar

[24] Li Z K, Duan Z S, Chen G R, et al. Consensus of multiagent systems and synchronization of complex networks: a unified viewpoint. IEEE Trans Circ Syst, } 2010, 57: 213-224 Google Scholar

[25] Wen G H, Duan Z S, Yu W W, et al. Consensus in multi-agent systems with communication constraints. Int J Robust Nonlin Control, } 2012, 22: 170-182 CrossRef Google Scholar

[26] Zhang H W, Lewis F L, Das A. Optimal design for synchronization of cooperative systems: state feedback, observer and output feedback. IEEE Trans Autom Control, } 2011, 56: 1948-1952 CrossRef Google Scholar

[27] 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, } 2012, 59: 3026-3041 CrossRef Google Scholar

[28] Li Z K, Liu X D, Ren W, et al. Distributed tracking control for linear multi-agent systems with a leader of bounded unknown input. IEEE Trans Autom Control, } 2012, 58: 518-523 Google Scholar

[29] Wen G H, Duan Z S, Chen G R, et al. Consensus tracking of multi-agent systems with lipschitz-type node dynamics and switching topologies. IEEE Trans Circ Syst I: Regular Papers, } 2014, 61: 499-511 CrossRef Google Scholar

[30] Chen W S, Li X B, Ren W, et al. Adaptive consensus of multi-agent systems with an unknown control directions based on a novel nussbaum-type function. IEEE Trans Autom Control, } 2014, 59: 1887-1892 CrossRef Google Scholar

[31] Li Z K, Duan Z S, Lewis F L. Distributed robust consensus control of multi-agent systems with heterogeneous matching uncertainties. Automatica, 2014, 50: 883-889 CrossRef Google Scholar

[32] Peng Z H, Wang D, Zhang H W, et al. Distributed neural network control for adaptive synchronization of uncertain dynamical multiagent systems. IEEE Trans Neural Netw Learn Syst, } 2014, 25: 1508-1519 CrossRef Google Scholar

[33] Du H B, Li S H. Attitude synchronization control for a group of flexible spacecraft. Automatica, 2014, 50: 646-651 CrossRef Google Scholar

[34] Liu T F, Jiang Z P. Distributed formation control of nonholonomic mobile robots without global position measurements. Automatica, } 2013, 49: 592-600 CrossRef Google Scholar

[35] Cui R X, Yan W S, Xu D M. Synchronization of multiple autonomous underwater vehicles without velocity measurements. Sci China Inf Sci, } 2012, 55: 1693-1703 CrossRef Google Scholar

[36] Krsti$\acute{\rm c}$ M, Kokotovic P V, Kanellakopoulos I. {Nonlinear and Adaptive Control Design.} New York: John Wiley & Sons, 1995. Google Scholar

[37] Li J H, Lee P M, Jun B H, et al. Point-to-point navigation of underactuated ships. Automatica, 2008, 44: 3201-3205 CrossRef Google Scholar

[38] Tee K P, Ge S S. Control of fully actuated ocean surface vessels using a class of feedforward approximators. IEEE Trans Control Syst Tech, 2006, 14: 750-756 CrossRef Google Scholar

[39] Chen M, Ge S S, How B V E, et al. Robust adaptive position mooring control for marine vessels. IEEE Trans Control Syst Tech, } 2013, 21: 395-409 CrossRef Google Scholar

[40] How B V E, Ge S S, Choo Y S. Dynamic load positioning for subsea installation via adaptive neural control. IEEE J Ocean Eng, } 2013, 35: 366-375 Google Scholar

[41] Dai S L, Wang C, Luo F. Identification and learning control of ocean surface ship using neural networks. IEEE Trans Ind Inf, } 2012, 8: 801-810 CrossRef 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, } 2005, 41: 289-298 CrossRef Google Scholar

[43] Swaroop D, Hedrick J K, Yip P P, et al. Dynamic surface control for a class of nonlinear systems. IEEE Trans Autom Control, } 2000, 45: 1893-1899 CrossRef Google Scholar

[44] 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, } 2005, 6: 195-202 Google Scholar

[45] Wang D. Neural network-based adaptive dynamic surface control of uncertain nonlinear pure-feedback systems. Int J Robust Nonlin Control, } 2011, 21: 527-541 CrossRef Google Scholar

[46] Li T S, Wang D, Feng G, et al. A DSC approach to robust adaptive NN tracking control for strict-feedback nonlinear systems. IEEE Trans Syst Man Cybernetics, Part B: Cybernetics, } 2010, 40: 915-927 Google Scholar

[47] Tong S C, Li Y M, Feng G, et al. Observer-based adaptive fuzzy backstepping dynamic surface control for a class of MIMO nonlinear systems. IEEE Trans Syst Man Cybernetics, Part B: Cybernetics, } 2011, 41: 1124-1135 Google Scholar

[48] Zhang T P, Ge S S. Adaptive dynamic surface control of nonlinear systems with unknown dead zone in pure feedback form. Automatica, } 2008, 44: 1895-1903 CrossRef Google Scholar

[49] Wang M, Liu X, Shi P. Adaptive neural control of pure-feedback nonlinear time-delay systems via dynamic surface technqiue. IEEE Trans Syst Man Cybernetics, Part B: Cybernetics, } 2011, 41: 1681-1692 Google Scholar

[50] Xu B, Shi Z K, Yang C G, et al. Composite neural dynamic surface control of a class of uncertain nonlinear systems in strict-feedback form. IEEE Trans Cybernetics, } 2014, 44: 2626-2660 CrossRef Google Scholar

[51] Yoo S J. Distributed consensus tracking for multiple uncertain nonlinear strict-feedback systems under a directed graph. IEEE Trans Neural Netw Learn Syst, } 2013, 24: 666-672 CrossRef Google Scholar

[52] Cao C Y, Hovakimyan N. Novel $L_1$ neural network adaptive control architecture with guaranteed transient performance. IEEE Trans Neural Netw, } 2007, 18: 1160-1171 CrossRef Google Scholar

[53] Nguyen N T, Ishihara A K. Robust adaptive optimal control modification with large adaptive gain. {In: American Control Conference,} St Louis, 2009. 2581--2586. Google Scholar

[54] Yucelen T, Haddad W M. Low-frequency learning and fast adaptation in model reference adaptive control. IEEE Trans Autom Control, } 2013, 58: 1080-1085 CrossRef Google Scholar

[55] Fossen T I. {Marine Control System, Guidance, Navigation and Control of Ships, Rigs and Underwater Vehicles.} Trondheim, Norway, Marine Cyernetics, 2002. Google Scholar

[56] Franceschelli M, Gasparri A. On agreement problems with gossip algorithms in absence of common reference frames. {In: International Conference on Robotics and Automation,} Anchorage, 2010. 4481--4486. Google Scholar

[57] Chen M, Chen W H, Wu Q X. Adaptive fuzzy tracking control for a class of uncertain MIMO nonlinear systems using disturbance observer. Sci China Inf Sci, } 2014, 57: 012207-1085 Google Scholar

[58] 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, } 2014, 22: 1-15 CrossRef Google Scholar

[59] Khalil H K.{ Nonlinear Systems.} Upper Saddle River: Prentice Hall, 2002. Google Scholar

[60] Stepanyan V, Krishnakumary K, Nguyenz N, et al. Stability and performance metrics for adaptive flight control. {In: AIAA Guidance, Navigation, and Control Conference,} Chicago, 2009. 1--19. Google Scholar

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