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Semi-decentralized nonlinear cooperative control strategies for a network of heterogeneous autonomous underwater vehicles. (English) Zbl 1377.93016

Summary: In this paper, we develop nonlinear distributed or semi-decentralized cooperative control schemes for a team of heterogeneous Autonomous Underwater Vehicles (AUVs). The objective is to have the network of AUVs follow a desired trajectory, while the agents maintain a desired formation when there is a virtual leader whose position information is only available and known to a very small subset of the agents. The virtual leader does not receive any feedback and information from the other agents and the agents only communicate with their nearest neighboring agents. It is assumed that the model parameters associated with each vehicle/agent are different, although the order of the agents is the same. The developed and proposed nonlinear distributed cooperative control schemes are based on the dynamic surface control methodology for a network of heterogeneous autonomous vehicles with uncertainties. The development and investigation of the dynamic surface control methodology for a team of cooperative heterogeneous multi-agent nonlinear systems is accomplished for the first time in the literature. Simulation results corresponding to a team of six AUVs are provided to demonstrate and illustrate the advantages and superiority of our proposed cooperative control strategies as compared to the methods that are available in the literature.

MSC:

93A14 Decentralized systems
93C10 Nonlinear systems in control theory
93C85 Automated systems (robots, etc.) in control theory
68T42 Agent technology and artificial intelligence
Full Text: DOI

References:

[1] WenG, DuanZ, YuW, ChenG. Consensus of multi‐agent systems with nonlinear dynamics and sampled‐data information: a delayed‐input approach. International Journal of Robust and Nonlinear Control2013; 23(6): 602-619. · Zbl 1273.93012
[2] LiH, LiaoX, LeiX, HuangT, ZhuW. Second‐order consensus seeking in multi‐agent systems with nonlinear dynamics over random switching directed networks. IEEE Transactions onCircuits and Systems I: Regular Papers2013; 60(6): 1595-1607. · Zbl 1468.93011
[3] YuW, RenW, ZhengWX, ChenG, LüJ. Distributed control gains design for consensus in multi‐agent systems with second‐order nonlinear dynamics. Automatica2013; 49(7): 2107-2115. · Zbl 1364.93039
[4] LiW, ChenZ, LiuZ. Leader‐following formation control for second‐order multiagent systems with time‐varying delay and nonlinear dynamics. Nonlinear Dynamics2013; 72(4): 803-812. · Zbl 1284.93018
[5] MengD, JiaY, DuJ, ZhangJ. High‐precision formation control of nonlinear multi‐agent systems with switching topologies: a learning approach. International Journal of Robust and Nonlinear Control2015; 25(13): 1993-2018. · Zbl 1328.93026
[6] KimH, ShimH, SeoJH. Output consensus of heterogeneous uncertain linear multi‐agent systems. IEEE Transactions on Automatic Control2011; 56(1): 200-206. · Zbl 1368.93378
[7] ZhengY, WangL. Consensus of heterogeneous multi‐agent systems without velocity measurements. International Journal of Control2012; 85(7): 906-914. · Zbl 1282.93031
[8] DingZ. Consensus output regulation of a class of heterogeneous nonlinear systems. IEEE Transactions on Automatic Control2013; 58(10): 2648-2653. · Zbl 1369.93020
[9] ChopraN, SpongMW. Output synchronization of nonlinear systems with relative degree one. In Recent Advances in Learning and Control, BlondelVD (ed.), BoydSP (ed.), KimuraH (ed.) (eds)., Lecture Notes in Control and Information SciencesSpringer‐Verlag: Berlin Heidelberg, 2008; 51-64. · Zbl 1201.93060
[10] PeymaniE, GripHF, SaberiA, WangX, FossenTI. H‐inf almost output synchronization for heterogeneous networks of introspective agents under external disturbances. Automatica2014; 50(4): 1026-1036. · Zbl 1298.93031
[11] WielandP, SepulchreR, AllgöwerF. An internal model principle is necessary and sufficient for linear output synchronization. Automatica2011; 47(5): 1068-1074. · Zbl 1233.93011
[12] ZhengY, ZhuY, WangL. Consensus of heterogeneous multi‐agent systems. IET Control Theory & Applications2011; 5(16): 1881-1888.
[13] MehrabianAR, KhorasaniK. Distributed formation recovery control of heterogeneous multiagent euler-lagrange systems subject to network switching and diagnostic imperfections. IEEE Transactions on Control Systems Technology2016; 24(6): 2158-2166.
[14] MehrabianAR, KhorasaniK. Constrained distributed cooperative synchronization and reconfigurable control of heterogeneous networked euler‐lagrange multi‐agent systems. Information Sciences2016; 370‐371: 578-597. · Zbl 1429.93026
[15] MehrabianAR, KhorasaniK. Distributed and cooperative quaternion‐based attitude synchronization and tracking control for a network of heterogeneous spacecraft formation flying mission. Journal of the Franklin Institute2015; 352(9): 3885-3913. · Zbl 1395.93426
[16] MehrabianAR, KhorasaniK. Cooperative optimal synchronization of networked uncertain nonlinear heterogeneous multi‐agent systems with switching topologies. ASME Journal of Dynamic Systems, Measurement and Control2015; 137(4): 12.
[17] SongB, HedrickJK. Dynamic Surface Control of Uncertain Nonlinear Systems: An LMI Approach. Springer, 2011. · Zbl 1216.93002
[18] GomesRM, SousaJB, PereiraFL. Integrated maneuver and control design for rov operations. In Oceans Proceedings, Vol. 2IEEE: San Diego, California, 2003; 703-710.
[19] GirardA, HedrickJK. Dynamic positioning of ships using nonlinear dynamic surface control. Proceedings of the Fifth IFAC Symposium on Nonlinear Control Systems, Saint Petersburg, Russia, 2001; 1134-1140.
[20] ChenM, JiangB. Adaptive control and constrained control allocation for overactuated ocean surface vessels. International Journal of Systems Science, Taylor & Francis, Inc. Bristol, PA, USA2013; 44(12): 2295-2309. · Zbl 1307.93195
[21] WangH, WangD, PengZ. Adaptive dynamic surface control for cooperative path following of marine surface vehicles with input saturation. Nonlinear Dynamics2014; 77(1‐2): 107-117. · Zbl 1314.93017
[22] WangY, WuQ, WangY. Distributed cooperative control for multiple quadrotor systems via dynamic surface control. Nonlinear Dynamics2014; 75(3): 513-527. · Zbl 1282.70046
[23] RenW, CaoY. Distributed Coordination of Multi‐Agent Networks: Emergent Problems, Models, and Issues. Springer, 2010.
[24] FossenTI. Guidance and Control of Ocean Vehicles, Vol. 199. Wiley: New York, 1994.
[25] CaoY, RenW, MengZ. Decentralized finite‐time sliding mode estimators and their applications in decentralized finite‐time formation tracking. Systems & Control Letters2010; 59(9): 522-529. · Zbl 1207.93103
[26] FjerdingenSA, KyrkjebøE, TransethAA. Auv pipeline following using reinforcement learning. 41st International Symposium on Robotics (ISR) and 6th German Conference on Robotics (ROBOTIK): VDE, Munich, Germany, 2010; 1-8.
[27] YangH, ZhangF. Geometric formation control for autonomous underwater vehicles. IEEE International Conference on Robotics and Automation (ICRA), Anchorage, AK, USA, 2010; 4288-4293.
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