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Observer-based leader-following formation control of uncertain multiple agents by integral sliding mode

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper investigates the formation control problem of multiple agents. The formation control is founded on leader-following approaches. The method of integral sliding mode control is adopted to achieve formation maneuvers of the agents based on the concept of graph theory. Since the agents are subject to uncertainties, the uncertainties also challenge the formation-control design. Under a mild assumption that the uncertainties have an unknown bound, the technique of nonlinear disturbance observer is utilized to tackle the issue. According to a given communication topology, formation stability conditions are investigated by the observer-based integral sliding mode formation control. From the perspective of Lyapunov, not only is the formation stability guaranteed, but the desired formation of the agents is also realized. Finally, some simulation results are presented to show the feasibility and validity of the proposed control scheme through a multi-agent platform.
Rocznik
Strony
35--44
Opis fizyczny
Bibliogr. 35 poz., rys., wykr.
Twórcy
autor
  • School of Control and Computer Engineering, North China Electric Power University, Beijing, 102206, China
autor
  • College of Automation, Beijing Union University, Beijing, 100101, China
autor
  • School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan, 250101, China
Bibliografia
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  • [3] L. Cheng, Y.Wang,W. Ren, Z.G. Hou and M. Tan, “On convergence rate of leader-following consensus of linear multi-agent systems with communication noises”, IEEE Trans. Autom. Control 61(11), 3586‒3592 (2016).
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  • [5] D. Pazderski and K. Kozłowski, “Control of a planar robot in the flight phase using transverse function approach”, Bull. Pol. Ac.: Tech. 63(3) 759-770 (2015)
  • [6] Y. Dai, Y. Kim, S. Wee, D. Lee and S. Lee, “Symmetric caging formation for convex polygonal object transportation by multiple mobile robots based on fuzzy sliding mode control”, ISA Trans. 60, 321‒332 (2016).
  • [7] T.F. Liu and Z.P. Jiang, “Distributed nonlinear control of mobile autonomous multi-agents”, Automatica, 50(4), 1075‒1086 (2014).
  • [8] L. Cheng, Y. Wang, W. Ren, Z.G. Hou and M. Tan, “Containment control of multi-agent systems with dynamic leaders based on a PIn-type approach”, IEEE Trans. Cybern, 46(12), 3004‒3017, 2016.
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  • [12] D.W. Qian, S. Tong and C. Li, “Leader-following formation control of multiple robots with uncertainties through sliding mode and nonlinear disturbance observer”, ETRI Journal 38(5), 1008‒1018 (2016).
  • [13] W. Ren and R. Beard, Distributed Consensus in Multi-Vehicle Cooperative Controls. Springer-Verlag, London, 2007.
  • [14] Y. Dai, Y. Kim, S. Wee, D. Lee and S. Lee, “A switching formation strategy for obstacle avoidance of a multi-robot system based on robot priority model”, ISA Trans. 56, 123‒134 (2015).
  • [15] Y. Chang, C. Chang, C. Chen and C. Tao, “Fuzzy sliding-mode formation control for multirobot systems: design and implementation”, IEEE Trans. Syst. Man Cybern. Part B-Cybern. 42(2), 444‒457 (2012).
  • [16] Y. Chang, C. Yang, W. Chan, H. Lin and C. Chang, “Adaptive fuzzy sliding-mode formation controller design for multi-robot dynamic aystems”, Int. J. Fuzzy Syst. 16(1), 121‒131 (2014)
  • [17] M. Biglarbegian, “A novel robust leader-following control design for mobile robots”, J. Intell. Robot. Syst. 71, 391‒402 (2013).
  • [18] H. Du, S. Li and X. Lin, “Finite-time formation control of multiagent systems via dynamic output feedback”, Int. J. Robust Nonlinear Control 23(14), 1609‒1628 (2013).
  • [19] B. Ranjbar-Sahraei, F. Shabaninia, A. Nemati and S. Stan, “A novel robust decentralized adaptive fuzzy control for swarm formation of multiagent systems” IEEE Trans. Ind. Electron. 59(8), 3124‒3134 (2012).
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  • [26] L. Dong, S. Chai, B. Zhang and S. Nguang, “Sliding mode control for multi-agent systems under a time-varying topology”, Int. J. Syst. Sci. 47(9) 2193‒2200 (2016).
  • [27] A. Zou, K. Kumar and Z. Hou, “Distributed consensus control for multi-agent systems using terminal sliding mode and Chebyshev neural networks”, Int. J. Robust Nonlinear Control 23(3), 334‒357 (2013).
  • [28] D. Zhao, T. Zou, S. Li and Q. Zhu, “Adaptive backstepping sliding mode control for leader-follower multi-agent systems”, IET Contr. Theory Appl. 6(8), 1109‒1117 (2012).
  • [29] D. Qian, S. Tong, H. Liu and X. Liu, “Load frequency control by neural-network-based integral sliding mode for nonlinear power systems with wind turbines”, Neurocomputing 173, 875‒885 (2016).
  • [30] H. Sun, S. Li and C. Sun, “Robust adaptive integral-slidingmode fault-tolerant control for airbreathing hypersonic vehicles”, Proc. Inst. Mech. Eng. Part I-J Syst Control Eng. 226(10), 1344‒1355 (2012).
  • [31] Y. Jiang, J. Liu and S. Wang, “Robust integral sliding-mode consensus tracking for multi-agent systems with time-varying delay”, Asian J. Control 18(1) 224‒235 (2016).
  • [32] S. Yu and X. Long, “Finite-time consensus for second-order multi-agent systems with disturbances by integral sliding mode”, Automatica 54, 158‒165 (2015).
  • [33] W. Chen, J. Yang, L. Guo and S. Li, “Disturbance-observerbased control and related methods-an overview”, IEEE Trans. Ind. Electron. 63(2), 1083‒1095 (2016).
  • [34] M. Kim and W. Chung, “Disturbance-observer-based PD control of flexible joint robots for asymptotic convergence”, IEEE Trans. Robot. 31(6), 1508‒1516 (2015).
  • [35] K.D. Do and J. Pan, Control of Ships and Underwater Vehicles, Springer-Verlag, Berlin, 2009.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6358c16c-134f-4512-aa4f-6b50df2550b8
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