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Coordination control of uncertain topological high-order multi-agent systems: distributed fuzzy adaptive iterative learning approach

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Abstract

This paper demonstrates that the method of T–S fuzzy model can be used to describe the uncertain topological structure for high-order linearly parameterized multi-agent systems (MAS). The dynamic of the leader is only available to a portion of the follower agents; thus, we present a novel distributed adaptive iterative learning control (AILC) protocol without using any global information to deal with the consensus problem of MAS under initial-state learning condition. It is proved that the proposed control protocol ensures all the internal signals in the multi-agent system are bounded, and the follower agents track the leader exactly on the finite time interval [0, T]; a sufficient condition is obtained for the exactly consensus result of the multi-agent system by choosing the appropriate composite energy function. Extensions to the formation control of multi-agent systems are also given. In the end, illustrative examples are shown to verify the availability of the proposed AILC scheme.

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References

  • Ahn H-S, Chen YQ (2009) Iterative learning control for multi-agent formation. In: Iccas-Sice. IEEE, pp 3111–3116

  • Ahn H-S, Moore KL, Chen Y (2010) Trajectory-keeping in satellite formation flying via robust periodic learning control. Int J Robust Nonlinear Control 20(14):1655–1666

    Article  MathSciNet  MATH  Google Scholar 

  • Chen J, Cao X, Cheng P, Xiao Y, Sun Y (2010) Distributed collaborative control for industrial automation with wireless sensor and actuator networks. IEEE Trans Ind Electron 57(12):4219–4230

    Article  Google Scholar 

  • Ho D, Li JM, Niu YG (2005) Adaptive neural control for a class of nonlinearly parametric time-delay systems. IEEE Trans Neural Netw Learn Syst 16(3):625–635

    Article  Google Scholar 

  • Jin X (2016) Adaptive iterative learning control for high-order nonlinear multi-agent systems consensus tracking. Syst Control Lett 89:16–23

    Article  MathSciNet  MATH  Google Scholar 

  • Li J, Li J (2013) Adaptive iterative learning control for consensus of multi-agent systems. IET Control Theory Appl 7(1):136–142

    Article  MathSciNet  Google Scholar 

  • Li J, Li J (2014) Adaptive iterative learning control for coordination of second-order multi-agent systems. Int J Robust Nonlinear Control 24(18):3282–3299

    Article  MathSciNet  MATH  Google Scholar 

  • Li J, Li J (2016) Distributed adaptive fuzzy iterative learning control of coordination problems for higher order multi-agent systems. Int J Syst Sci 47(10):2318–2329

    Article  MathSciNet  MATH  Google Scholar 

  • Lin H, Wei Q, Liu D et al (2016) Adaptive tracking control of leader-following linear multi-agent systems with external disturbances. Int J Syst Sci 47(13):3167–3179

    Article  MathSciNet  MATH  Google Scholar 

  • Liu Y, Jia Y (2012) Robust formation control of discrete-time multi-agent systems by iterative learning approach. Int J Control Autom Syst 10(5):913–919

    Article  Google Scholar 

  • Meng D, Jia Y (2012) Formation control for multi-agent systems through an iterative learning design approach. Int J Robust Nonlinear Control 24(2):340–361

    Article  MathSciNet  MATH  Google Scholar 

  • Meng D, Jia Y, Du J et al (2013) Consensus control for directed networks with multiple higher-order discrete dynamic agents: an ILC-based approach. Decis Control IEEE 23(1):4666–4671

    Google Scholar 

  • Meng D, Jia Y, Du J et al (2014) On iterative learning algorithms for the formation control of nonlinear multi-agent systems. Automatica 50(1):291–295

    Article  MathSciNet  MATH  Google Scholar 

  • Qu Z, Wang J, Hull RA (2008) Cooperative control of dynamical systems with application to autonomous vehicles. IEEE Trans Autom Control 53(4):894–911

    Article  MathSciNet  MATH  Google Scholar 

  • Ren W (2007) Consensus strategies for cooperative control of vehicle formations. IET Control Theory Appl 1(2):505–512

    Article  Google Scholar 

  • Ren W, Beard RW (2008) Distributed consensus in multvehicle cooperative control. Commun Control Eng 27(2):71–82

    Google Scholar 

  • Shahvali M, Shojaei K (2016) Distributed adaptive neural control of nonlinear multi-agent systems with unknown control directions. Nonlinear Dyn 83(4):2213–2228

    Article  MathSciNet  MATH  Google Scholar 

  • Shi J, He X, Wang Z et al (2013) Consensus control for a class of second-order multi-agent systems: an iterative learning approach. In: International conference on unmanned aircraft systems. IEEE, pp 841–849

  • Shi J, He X, Wang Z et al (2014) Iterative consensus for a class of second-order multi-agent systems. J Intell Robot Syst 73(1–4):655–664

    Article  Google Scholar 

  • Sun MX (2009) A Barbalat-like lemma with its application to learning control. IEEE Trans Autom Control 54(9):2222–2225

    Article  MathSciNet  MATH  Google Scholar 

  • Takagi T, Sugeno M (1993) Fuzzy identification of systems and its applications to modeling and control. Read Fuzzy Sets Intell Syst 15(1):387–403

    Article  Google Scholar 

  • Wang J, Duan Z, Wen G (2015) Distributed H \(\infty \) robust control of uncertain linear multi-agent systems. Int J Robust Nonlinear Control 25(13):2162–2179

    Article  MathSciNet  MATH  Google Scholar 

  • Xiong WJ, Yu W, Lu JH, Yu XH (2014) Fuzzy modelling and consensus of nonlinear multiagent systems with variable structure. IEEE Trans Circuits Syst I Regul Pap 61(4):1183–1191

    Article  Google Scholar 

  • Xiong W, Xu L, Huang T et al (2017a) Finite-Iteration tracking of singular coupled systems based on learning control with packet losses. IEEE Trans Syst Man Cybern Syst 99:1–11

    Article  Google Scholar 

  • Xiong W, Xu L, Ho D et al (2017b) Synchronous and asynchronous iterative learning strategies of T–S fuzzy systems with measurable and unmeasurable state information. IEEE Trans Fuzzy Syst PP(99): 1–1

  • Yang S, Xu JX (2012) Adaptive iterative learning control for multi-agent systems consensus tracking. IEEE Int Conf Syst Man Cybernet 8350(2):4672–4677

    Google Scholar 

  • Yang S, Xu JX, Yu M (2013) An iterative learning control approach for synchronization of multi-agent systems under iteration-varying graph. Decision and Control. IEEE, pp 6682–6687

  • Yang S, Xu J, Huang D et al (2015) Synchronization of heterogeneous multi-agent systems by adaptive iterative learning control. Asian J Control 17(6):2091–2104

    Article  MathSciNet  MATH  Google Scholar 

  • Yu W, Chen G, Lü J (2012) Pinning control of general multi-agent systems. In: Intelligent control and automation. IEEE, pp 1930–1935

  • Zhang HW, Lewis FL (2012) Adaptive cooperative tracking control of higher-order nonlinear systems with unknown dynamics. Automatica 48(7):1432–1439

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

This study was funded by the National Nature Science Foundation of China under Grant 61573013, 61603286.

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Authors and Affiliations

  1. School of Mathematics and Statistics, Xidian University, Xi’an, 710126, People’s Republic of China

    Hui Wu & Junmin Li

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  1. Hui Wu
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  2. Junmin Li
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Correspondence to Junmin Li.

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Wu, H., Li, J. Coordination control of uncertain topological high-order multi-agent systems: distributed fuzzy adaptive iterative learning approach. Soft Comput 23, 6183–6196 (2019). https://doi.org/10.1007/s00500-018-3271-1

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