A survey on recent progress in control of swarm systems

Abstract

It has been witnessed that swarm systems are superior to individual agents in performing complicated tasks. In recent years, new results in some branches of control for swarm systems have developed and investigated with respect to various objectives and scenarios. This survey is to take a glimpse into some newly developed control techniques for swarm systems, especially those presented after 2013. The covered topics include some up-to-date progress in the areas of consensus, formation, flocking, containment, optimal coverage/mission planning, and sensor networks. Contributions and connections of the mentioned references are discussed briefly. Based on the new results in control of swarm systems, some possible new future research topics are suggested.

This is a preview of subscription content, access via your institution.

References

  1. 1

    Vicsek T, Czirók A, Ben-Jacob E, et al. Novel type of phase transition in a system of self-driven particles. Phys Rev Lett, 1995, 75: 1226

    MathSciNet  Article  Google Scholar 

  2. 2

    Jadbabaie A, Lin J, Morse A S. Coordination of groups of mobile autonomous agents using nearest neighbor rules. IEEE Trans Automat Control, 2003, 48: 988–1001

    MathSciNet  Article  Google Scholar 

  3. 3

    Gazi V. Stability analysis of swarms. Dissertation for Ph.D. Degree. Columbus: The Ohio State University, 2002

    Google Scholar 

  4. 4

    Ren W, Cao Y. Distributed Coordination of Multi-Agent Networks: Emergent Problems, Models, and Issues. Berlin: Springer Science Business Media, 2010

    Google Scholar 

  5. 5

    Olfati-Saber R, Murray R M. Consensus problems in networks of agents with switching topology and time-delays. IEEE Trans Automat Control, 2004, 49: 1520–1533

    MathSciNet  Article  Google Scholar 

  6. 6

    Moreau L. Stability of multiagent systems with time-dependent communication links. IEEE Trans Automat Control, 2005, 50: 169–182

    MathSciNet  Article  Google Scholar 

  7. 7

    Mesbahi M. On state-dependent dynamic graphs and their controllability properties. IEEE Trans Automat Control, 2005, 50: 387–392

    MathSciNet  Article  Google Scholar 

  8. 8

    Liu Y Y, Slotine J J, Barabási A L. Controllability of complex networks. Nature, 2011, 473: 167–173

    Article  Google Scholar 

  9. 9

    Ren W, Beard R W. Consensus seeking in multiagent systems under dynamically changing interaction topologies. IEEE Trans Automat Control, 2005, 50: 655–661

    MathSciNet  Article  Google Scholar 

  10. 10

    Tian Y P, Liu C L. Consensus of multi-agent systems with diverse input and communication delays. IEEE Trans Automat Control, 2008, 53: 2122–2128

    MathSciNet  Article  Google Scholar 

  11. 11

    Hatano Y, Mesbahi M. Agreement over random networks. IEEE Trans Automat Control, 2005, 50: 1867–1872

    MathSciNet  Article  Google Scholar 

  12. 12

    Fax J A, Murray R M. Information flow and cooperative control of vehicle formations. IEEE Trans Automat Control, 2004, 49: 1465–1476

    MathSciNet  Article  Google Scholar 

  13. 13

    Porfiri M, Roberson D G, Stilwell D J. Tracking and formation control of multiple autonomous agents: a two-level consensus approach. Automatica, 2007, 43: 1318–1328

    MathSciNet  MATH  Article  Google Scholar 

  14. 14

    Olfati-Saber R. Flocking for multi-agent dynamic systems: algorithms and theory. IEEE Trans Automat Control, 2006, 51: 401–420

    MathSciNet  Article  Google Scholar 

  15. 15

    Liu Y, Passino K M. Cohesive behaviors of multiagent systems with information flow constraints. IEEE Trans Automat Control, 2006, 51: 1734–1748

    MathSciNet  Article  Google Scholar 

  16. 16

    Hong Y, Hu J, Gao L. Tracking control for multi-agent consensus with an active leader and variable topology. Automatica, 2006, 42: 1177–1182

    MathSciNet  MATH  Article  Google Scholar 

  17. 17

    Duan H, Liu S. Non-linear dual-mode receding horizon control for multiple unmanned air vehicles formation flight based on chaotic particle swarm optimisation. IET Control Theory Appl, 2010, 4: 2565–2578

    Article  Google Scholar 

  18. 18

    Semsar-Kazerooni E, Khorasani K. Optimal consensus algorithms for cooperative team of agents subject to partial information. Automatica, 2008, 44: 2766–2777

    MathSciNet  MATH  Article  Google Scholar 

  19. 19

    Semsar-Kazerooni E, Khorasani K. An optimal cooperation in a team of agents subject to partial information. Int J Control, 2009, 82: 571–583

    MathSciNet  MATH  Article  Google Scholar 

  20. 20

    Ji M, Ferrari-Trecate G, Egerstedt M, et al. Containment control in mobile networks. IEEE Trans Automat Control, 2008, 53: 1972–1975

    MathSciNet  Article  Google Scholar 

  21. 21

    Cortes J, Martinez S, Karatas T, et al. Coverage control for mobile sensing networks. In: Proceedings of IEEE International Conference on Robotics and Automation, Washington, 2002. 1327–1332

    Google Scholar 

  22. 22

    Doherty P, Heintz F, Kvarnström J. High-level mission specification and planning for collaborative unmanned aircraft systems using delegation. Unmanned Syst, 2013, 1: 75–119

    Article  Google Scholar 

  23. 23

    Azuma S I, Yoshimura R, Sugie T. Broadcast control of multi-agent systems. Automatica, 2013, 49: 2307–2316

    MathSciNet  MATH  Article  Google Scholar 

  24. 24

    Igarashi Y, Hatanaka T, Fujita M, et al. Passivity-based attitude synchronization in SE(3). IEEE Trans Control Syst Tech, 2009, 17: 1119–1134

    Article  Google Scholar 

  25. 25

    Pimenta L C, Pereira G A, Michael N, et al. Swarm coordination based on smoothed particle hydrodynamics technique. IEEE Trans Robotics, 2013, 29: 383–399

    Article  Google Scholar 

  26. 26

    Cao Y, Stuart D, Ren W, et al. Distributed containment control for multiple autonomous vehicles with doubleintegrator dynamics: algorithms and experiments. IEEE Trans Control Syst Tech, 2011, 19: 929–938

    Article  Google Scholar 

  27. 27

    Vig L, Adams J A. Multi-robot coalition formation. IEEE Trans Robotics, 2006, 22: 637–649

    MATH  Article  Google Scholar 

  28. 28

    Dydek Z T, Annaswamy A M, Lavretsky E. Adaptive configuration control of multiple uavs. Control Eng Practice, 2013, 21: 1043–1052

    Article  Google Scholar 

  29. 29

    Dong X, Yu B, Shi Z, et al. Time-varying formation control for unmanned aerial vehicles: theories and applications. IEEE Trans Control Syst Tech, 2015, 23: 340–348

    Article  Google Scholar 

  30. 30

    Dong X, Zhou Y, Ren Z, et al. Time-varying formation control for unmanned aerial vehicles with switching interaction topologies. Control Eng Practice, 2016, 46: 26–36

    Article  Google Scholar 

  31. 31

    Jia Y, Wang L. Experimental implementation of distributed flocking algorithm for multiple robotic fish. Control Eng Practice, 2014, 30: 1–11

    Article  Google Scholar 

  32. 32

    Giovanini L, Balderud J, Katebi R. Autonomous and decentralized mission planning for clusters of uuvs. Int J Control, 2007, 80: 1169–1179

    MATH  Article  Google Scholar 

  33. 33

    Zhu B, Xie L H, Han D, et al. Recent developments in control and optimization of swarm systems: a brief survey. In: Proceedings of the 12th IEEE International Conference on Control and Automation (ICCA), Kathmandu, 2016. 19–24

    Google Scholar 

  34. 34

    You K Y, Xie L H. Survey of recent progress in networked control systems. Acta Automat Sin, 2013, 39: 101–117

    MathSciNet  Article  Google Scholar 

  35. 35

    Zuo Z, Tie L. A new class of finite-time nonlinear consensus protocols for multi-agent systems. Int J Control, 2014, 87: 363–370

    MathSciNet  MATH  Article  Google Scholar 

  36. 36

    Godsil C, Royle G F. Algebraic Graph Theory. Berlin: Springer Science & Business Media, 2013

    Google Scholar 

  37. 37

    Ren W, Atkins E. Distributed multi-vehicle coordinated control via local information exchange. Int J Robust Nonlinear Control, 2007, 17: 1002–1033

    MathSciNet  MATH  Article  Google Scholar 

  38. 38

    Ren W, Beard R W. Distributed Consensus in Multi-Vehicle Cooperative Control. Berlin: Springer, 2008

    Google Scholar 

  39. 39

    Zhang H T, Zhai C, Chen Z. A general alignment repulsion algorithm for flocking of multi-agent systems. IEEE Trans Automatic Control, 2011, 56: 430–435

    MathSciNet  Article  Google Scholar 

  40. 40

    Ferrari-Trecate G, Galbusera L, Marciandi M P E, et al. Model predictive control schemes for consensus in multi-agent systems with single-and double-integrator dynamics. IEEE Trans Automat Control, 2009, 54: 2560–2572

    MathSciNet  Article  Google Scholar 

  41. 41

    Dimarogonas D V, Frazzoli E, Johansson K H. Distributed event-triggered control for multi-agent systems. IEEE Trans Automat Control, 2012, 57: 1291–1297

    MathSciNet  Article  Google Scholar 

  42. 42

    Ren W. Distributed leaderless consensus algorithms for networked euler–lagrange systems. Int J Control, 2009, 82: 2137–2149

    MathSciNet  MATH  Article  Google Scholar 

  43. 43

    Kumar M, Garg D, Kumar V. Segregation of heterogeneous units in a swarm of robotic agents. IEEE Trans Automat Control, 2010, 55: 743–748

    MathSciNet  Article  Google Scholar 

  44. 44

    Wieland P, Sepulchre R, Allgöwer F. An internal model principle is necessary and sufficient for linear output synchronization. Automatica, 2011, 47: 1068–1074

    MathSciNet  MATH  Article  Google Scholar 

  45. 45

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

    Article  Google Scholar 

  46. 46

    Dunbar WB, Murray RM. Distributed receding horizon control for multi-vehicle formation stabilization. Automatica, 2006, 42: 549–558

    MathSciNet  MATH  Article  Google Scholar 

  47. 47

    Gu D. A differential game approach to formation control. IEEE Trans Control Syst Tech, 2008, 16: 85–93

    Article  Google Scholar 

  48. 48

    Chen Y Y, Tian Y P. A backstepping design for directed formation control of three-coleader agents in the plane. Int J Robust Nonlinear Control, 2009, 19: 729–745

    MathSciNet  MATH  Article  Google Scholar 

  49. 49

    Zhao D, Zou T, Li S, et al. Adaptive backstepping sliding mode control for leader-follower multi-agent systems. IET Control Theory Appl, 2012, 6: 1109–1117

    MathSciNet  Article  Google Scholar 

  50. 50

    Bennet D J, MacInnes C, Suzuki M, et al. Autonomous three-dimensional formation flight for a swarm of unmanned aerial vehicles. J Guidance Control Dynam, 2011, 34: 1899–1908

    Article  Google Scholar 

  51. 51

    Karaman S, Frazzoli E. Linear temporal logic vehicle routing with applications to multi-uav mission planning. Int J Robust Nonlinear Control, 2011, 21: 1372–1395

    MathSciNet  MATH  Article  Google Scholar 

  52. 52

    Wang H. Consensus of networked mechanical systems with communication delays: a unified framework. IEEE Trans Automat Control, 2014, 59: 1571–1576

    MathSciNet  MATH  Article  Google Scholar 

  53. 53

    Abdessameud A, Polushin I G, Tayebi A. Synchronization of lagrangian systems with irregular communication delays. IEEE Trans Automat Control, 2014, 59: 187–193

    MathSciNet  MATH  Article  Google Scholar 

  54. 54

    Abdessameud A, Polushin I G, Tayebi A. Synchronization of nonlinear systems with communication delays and intermittent information exchange. Automatica, 2015, 59: 1–8

    MathSciNet  MATH  Article  Google Scholar 

  55. 55

    Seyboth G S, Dimarogonas D V, Johansson K H. Event-based broadcasting for multi-agent average consensus. Automatica, 2013, 49: 245–252

    MathSciNet  MATH  Article  Google Scholar 

  56. 56

    Liu S, Xie L, Zhang H. Distributed consensus for multi-agent systems with delays and noises in transmission channels. Automatica, 2011, 47: 920–934

    MathSciNet  MATH  Article  Google Scholar 

  57. 57

    Huang J. Nonlinear Output Regulation: Theory and Applications. London: Prentice-Hall, 2004

    Google Scholar 

  58. 58

    Kim H, Shim H, Jin H S. Output consensus of heterogeneous uncertain linear multi-agent systems. IEEE Trans Automat Control, 2011, 56: 200–206

    MathSciNet  Article  Google Scholar 

  59. 59

    Wang X, Hong Y, Huang J, et al. A distributed control approach to a robust output regulation problem for multi-agent linear systems. IEEE Trans Automat Control, 2010, 55: 2891–2895

    MathSciNet  Article  Google Scholar 

  60. 60

    Xiang J, Wei W, Li Y. Synchronized output regulation of linear networked systems. IEEE Trans Automat Control, 2009, 54: 1336–1341

    MathSciNet  Article  Google Scholar 

  61. 61

    Huang J. Remarks on synchronized output regulation of linear networked systems. IEEE Trans Automat Control, 2011, 56: 630–631

    MathSciNet  Article  Google Scholar 

  62. 62

    Ding Z. Consensus output regulation of a class of heterogeneous nonlinear systems. IEEE Trans Automat Control, 2013, 58: 2648–2653

    MathSciNet  Article  Google Scholar 

  63. 63

    Isidori A, Marconi L, Casadei G. Robust output synchronization of a network of heterogeneous nonlinear agents via nonlinear regulation theory. IEEE Trans Automat Control, 2014, 59: 2680–2691

    MathSciNet  MATH  Article  Google Scholar 

  64. 64

    Yi D, Jie H. Cooperative global output regulation for a class of nonlinear multi-agent systems. IEEE Trans Automat Control, 2014, 59: 1348–1354

    MathSciNet  MATH  Article  Google Scholar 

  65. 65

    Su Y, Huang J. Cooperative global robust output regulation for nonlinear uncertain multi-agent systems in lower triangular form. IEEE Trans Automat Control, 2015, 60: 2378–2389

    MathSciNet  MATH  Article  Google Scholar 

  66. 66

    Liu W, Huang J. Cooperative global robust output regulation for a class of nonlinear multi-agent systems with switching network. IEEE Trans Automat Control, 2015, 60: 1963–1968

    MathSciNet  MATH  Article  Google Scholar 

  67. 67

    Tang Y, Hong Y, Wang X. Distributed output regulation for a class of nonlinear multi-agent systems with unknowninput leaders. Automatica, 2015, 62: 154–160

    MathSciNet  MATH  Article  Google Scholar 

  68. 68

    Liu L. Adaptive cooperative output regulation for a class of nonlinear multi-agent systems. IEEE Trans Automat Control, 2015, 60: 1677–1682

    MathSciNet  MATH  Article  Google Scholar 

  69. 69

    Cai H, Lewis F L, Hu G, et al. The adaptive distributed observer approach to the cooperative output regulation of linear multi-agent systems. Automatica, 2017, 75: 299–305

    MathSciNet  MATH  Article  Google Scholar 

  70. 70

    Huang J. The cooperative output regulation problem of discrete-time linear multi-agent systems by the adaptive distributed observer. IEEE Trans Automat Control, 2017, 62: 1979–1984

    MathSciNet  Article  Google Scholar 

  71. 71

    Li Z, Chen M Z Q, Ding Z. Distributed adaptive controllers for cooperative output regulation of heterogeneous agents over directed graphs. Automatica, 2016, 68: 179–183

    MathSciNet  MATH  Article  Google Scholar 

  72. 72

    Wang L, Xiao F. Finite-time consensus problems for networks of dynamic agents. IEEE Trans Automat Control, 2010, 55: 950–955

    MathSciNet  Article  Google Scholar 

  73. 73

    Xiao F, Wang L, Chen T. Finite-time consensus in networks of integrator-like dynamic agents with directional link failure. IEEE Trans Automat Control, 2014, 59: 756–762

    MathSciNet  MATH  Article  Google Scholar 

  74. 74

    Gao W, Hung J C. Variable structure control of nonlinear systems: a new approach. IEEE Trans Ind Electron, 1993, 40: 45–55

    Article  Google Scholar 

  75. 75

    Huang X, Lin W, Yang B. Global finite-time stabilization of a class of uncertain nonlinear systems. Automatica, 2005, 41: 881–888

    MathSciNet  MATH  Article  Google Scholar 

  76. 76

    Wang Y Z, Feng G. On finite-time stability and stabilization of nonlinear port-controlled hamiltonian systems. Sci China Inf Sci, 2013, 56: 108202

    MathSciNet  Google Scholar 

  77. 77

    Ding S H, Li S H. A survey for finite-time control problems. Control Decision, 2011, 26: 161–169

    MathSciNet  MATH  Google Scholar 

  78. 78

    Cortes J. Finite-time convergent gradient flows with applications to network consensus. Automatica, 2006, 42: 1993–2000

    MathSciNet  MATH  Article  Google Scholar 

  79. 79

    Wang X, Hong Y. Finite-time consensus for multi-agent networks with second-order agent dynamics. IFAC Proc Vol, 2008, 41: 15185–15190

    Article  Google Scholar 

  80. 80

    Zhao Y, Duan Z, Wen G, et al. Distributed finite-time tracking control for multi-agent systems: an observer-based approach. Syst Control Lett, 2013, 62: 22–28

    MathSciNet  MATH  Article  Google Scholar 

  81. 81

    Zhao L W, Hua C C. Finite-time consensus tracking of second-order multi-agent systems via nonsingular TSM. Nonlinear Dynam, 2014, 75: 311–318

    MathSciNet  MATH  Article  Google Scholar 

  82. 82

    Liu Y, Geng Z. Finite-time optimal formation control for second-order multiagent systems. Asian J Control, 2014, 16: 138–148

    MathSciNet  MATH  Article  Google Scholar 

  83. 83

    Zuo Z. Nonsingular fixed-time consensus tracking for second-order multi-agent networks. Automatica, 2015, 54: 305–309

    MathSciNet  MATH  Article  Google Scholar 

  84. 84

    Yu H, Shen Y, Xia X. Adaptive finite-time consensus in multi-agent networks. Syst Control Lett, 2013, 62: 880–889

    MathSciNet  MATH  Article  Google Scholar 

  85. 85

    Huang J, Wen C, Wang W, et al. Adaptive finite-time consensus control of a group of uncertain nonlinear mechanical systems. Automatica, 2015, 51: 292–301

    MathSciNet  MATH  Article  Google Scholar 

  86. 86

    Cao Y, Ren W. Finite-time consensus for multi-agent networks with unknown inherent nonlinear dynamics. Automatica, 2014, 50: 2648–2656

    MathSciNet  MATH  Article  Google Scholar 

  87. 87

    Hendrickx J M, Shi G, Johansson K H. Finite-time consensus using stochastic matrices with positive diagonals. IEEE Trans Automat Control, 2015, 60: 1070–1073

    MathSciNet  MATH  Article  Google Scholar 

  88. 88

    Zhao Y, Duan Z,Wen G. Finite-time consensus for second-order multi-agent systems with saturated control protocols. IET Control Theory Appl, 2015, 9: 312–319

    MathSciNet  Article  Google Scholar 

  89. 89

    Zhang B, Jia Y. Fixed-time consensus protocols for multi-agent systems with linear and nonlinear state measurements. Nonlinear Dynam, 2015, 82: 1683–1690

    MathSciNet  MATH  Article  Google Scholar 

  90. 90

    Zuo Z, Tie L. Distributed robust finite-time nonlinear consensus protocols for multi-agent systems. Int J Syst Sci, 2016, 47: 1366–1375

    MathSciNet  MATH  Article  Google Scholar 

  91. 91

    Li Z, Duan Z, Xie L, et al. Distributed robust control of linear multi-agent systems with parameter uncertainties. Int J Control, 2012, 85: 1039–1050

    MathSciNet  MATH  Article  Google Scholar 

  92. 92

    Qiu Z, Hong Y, Xie L. Quantized leaderless and leader-following consensus of high-order multi-agent systems with limited data rate. IEEE Trans Automat Control, 2016, 61: 2432–2447

    MathSciNet  MATH  Article  Google Scholar 

  93. 93

    He W, Cao J. Consensus control for high-order multi-agent systems. IET Control Theory Appl, 2011, 5: 231–238

    MathSciNet  Article  Google Scholar 

  94. 94

    Peng J, Ye X. Cooperative output-synchronisation of networked high-order power integrators. IET Control Theory Appl, 2013, 7: 2143–2152

    MathSciNet  MATH  Article  Google Scholar 

  95. 95

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

    Article  Google Scholar 

  96. 96

    Du H, Wen G, Yu X, et al. Finite-time consensus of multiple nonholonomic chained-form systems based on recursive distributed observer. Automatica, 2015, 62: 236–242

    MathSciNet  MATH  Article  Google Scholar 

  97. 97

    Zuo Z, Cichella V, Xu M, et al. Three-dimensional coordinated path-following control for second-order multi-agent networks. J Franklin Institute, 2015, 352: 3858–3872

    MathSciNet  Article  Google Scholar 

  98. 98

    Meng W, He Z, Teo R, et al. Integrated multi-agent system framework: decentralised search, tasking and tracking. IET Control Theory Appl, 2015, 9: 493–502

    Article  Google Scholar 

  99. 99

    Mei J, Ren W, Ma G. Distributed coordination for second-order multi-agent systems with nonlinear dynamics using only relative position measurements. Automatica, 2013, 49: 1419–1427

    MathSciNet  MATH  Article  Google Scholar 

  100. 100

    Li W, Spong M W. Unified cooperative control of multiple agents on a sphere for different spherical patterns. IEEE Trans Automat Control, 2014, 59: 1283–1289

    MathSciNet  MATH  Article  Google Scholar 

  101. 101

    Li W. Collective motion of swarming agents evolving on a sphere manifold: a fundamental framework and characterization. Sci Reports, 2015, 5

    Google Scholar 

  102. 102

    Kumar G, Kothare M V. Broadcast stochastic receding horizon control of multi-agent systems. Automatica, 2013, 49: 3600–3606

    MathSciNet  MATH  Article  Google Scholar 

  103. 103

    Li W, Zhang J F. Distributed practical output tracking of high-order stochastic multi-agent systems with inherent nonlinear drift and diffusion terms. Automatica, 2014, 50: 3231–3238

    MathSciNet  MATH  Article  Google Scholar 

  104. 104

    Dai L, Xia Y, Gao Y, et al. Cooperative distributed stochastic mpc for systems with state estimation and coupled probabilistic constraints. Automatica, 2015, 61: 89–96

    MathSciNet  MATH  Article  Google Scholar 

  105. 105

    Ding D, Wang Z, Shen B, et al. Event-triggered consensus control for discrete-time stochastic multi-agent systems: the input-to-state stability in probability. Automatica, 2015, 62: 284–291

    MathSciNet  MATH  Article  Google Scholar 

  106. 106

    Amelina N, Fradkov A, Jiang Y, et al. Approximate consensus in stochastic networks with application to load balancing. IEEE Trans Inf Theory, 2015, 61: 1739–1752

    MathSciNet  MATH  Article  Google Scholar 

  107. 107

    Chen Y, L¨u J, Lin Z. Consensus of discrete-time multi-agent systems with transmission nonlinearity. Automatica, 2013, 49: 1768–1775

    MathSciNet  MATH  Article  Google Scholar 

  108. 108

    Fan M C, Chen Z, Zhang H T. Semi-global consensus of nonlinear second-order multi-agent systems with measurement output feedback. IEEE Trans Automat Control, 2014, 59: 2222–2227

    MathSciNet  MATH  Article  Google Scholar 

  109. 109

    Wu Y, Meng X, Xie L, et al. An input-based triggering approach to leader-following problems. Automatica, 2017, 75: 221–228

    MathSciNet  MATH  Article  Google Scholar 

  110. 110

    Valcher M E, Misra P. On the consensus and bipartite consensus in high-order multi-agent dynamical systems with antagonistic interactions. Syst Control Lett, 2014, 66: 94–103

    MathSciNet  MATH  Article  Google Scholar 

  111. 111

    Wang P, Hadaegh F Y. Coordination and control of multiple microspacecraft moving in formation. J Astronaut Sci, 1996, 44: 315–355

    Google Scholar 

  112. 112

    Balch T, Arkin R C. Behavior-based formation control for multirobot teams. IEEE Trans Robotics Automat, 1998, 14: 926–939

    Article  Google Scholar 

  113. 113

    Beard R W, Lawton J, Hadaegh F Y, et al. A coordination architecture for spacecraft formation control. IEEE Trans Control Syst Techn, 2001, 9: 777–790

    Article  Google Scholar 

  114. 114

    Oh K K, Park M C, Ahn H S. A survey of multi-agent formation control. Automatica, 2015, 53: 424–440

    MathSciNet  Article  Google Scholar 

  115. 115

    Oh K K, Ahn H S. Formation control and network localization via orientation alignment. IEEE Trans Automat Control, 2014, 59: 540–545

    MathSciNet  MATH  Article  Google Scholar 

  116. 116

    Lin Z Y, Wang L L, Chen Z Y, et al. Necessary and sufficient graphical conditions for affine formation control. IEEE Trans Automat Control 2016, 61: 2877–2891

    MathSciNet  MATH  Article  Google Scholar 

  117. 117

    Lin Z, Ding W, Yan G, et al. Leader–follower formation via complex laplacian. Automatica, 2013, 49: 1900–1906

    MathSciNet  MATH  Article  Google Scholar 

  118. 118

    Lin Z, Wang L, Han Z, et al. Distributed formation control of multi-agent systems using complex laplacian. IEEE Trans Automat Control, 2014, 59: 1765–1777

    MathSciNet  MATH  Article  Google Scholar 

  119. 119

    Lee S M, Kim H, Myung H, et al. Cooperative coevolutionary algorithm-based model predictive control guaranteeing stability of multirobot formation. IEEE Trans Control Syst Tech, 2015, 23: 37–51

    Article  Google Scholar 

  120. 120

    Qiu H, Duan H. Receding horizon control for multiple uav formation flight based on modified brain storm optimization. Nonlinear Dynam, 2014, 78: 1973–1988

    Article  Google Scholar 

  121. 121

    Poonawala H A, Satici A C, Eckert H, et al. Collision-free formation control with decentralized connectivity preservation for nonholonomic-wheeled mobile robots. IEEE Trans Control Netw Syst, 2015, 2: 122–130

    MathSciNet  Article  Google Scholar 

  122. 122

    Rezaee H, Abdollahi F. A decentralized cooperative control scheme with obstacle avoidance for a team of mobile robots. IEEE Trans Ind Electron, 2014, 61: 347–354

    Article  Google Scholar 

  123. 123

    Do K D. Coordination control of multiple ellipsoidal agents with collision avoidance and limited sensing ranges. Syst Control Lett, 2012, 61: 247–257

    MathSciNet  MATH  Article  Google Scholar 

  124. 124

    Xia Y, Na X, Sun Z, et al. Formation control and collision avoidance for multi-agent systems based on position estimation. ISA Trans, 2016, 61: 287–296

    Article  Google Scholar 

  125. 125

    Zheng R, Lin Z, Fu M, et al. Distributed control for uniform circumnavigation of ring-coupled unicycles. Automatica, 2015, 53: 23–29

    MathSciNet  Article  Google Scholar 

  126. 126

    Chen Z, Zhang H T. No-beacon collective circular motion of jointly connected multi-agents. Automatica, 2011, 47: 1929–1937

    MathSciNet  MATH  Article  Google Scholar 

  127. 127

    Lou Y, Hong Y. Distributed surrounding design of target region with complex adjacency matrices. IEEE Trans Automat Control, 2015, 60: 283–288

    MathSciNet  MATH  Article  Google Scholar 

  128. 128

    Zhang Y, Hong Y. Distributed control design for leader escort of multi-agent systems. Int J Control, 2015, 88: 935–945

    MathSciNet  MATH  Google Scholar 

  129. 129

    Cai H, Huang J. The leader-following attitude control of multiple rigid spacecraft systems. Automatica, 2014, 50: 1109–1115

    MathSciNet  MATH  Article  Google Scholar 

  130. 130

    Mou S, Cao M, Morse A S. Target-point formation control. Automatica, 2015, 61: 113–118

    MathSciNet  MATH  Article  Google Scholar 

  131. 131

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

    MathSciNet  MATH  Article  Google Scholar 

  132. 132

    Cai X, de Queiroz M. Adaptive rigidity-based formation control for multirobotic vehicles with dynamics. IEEE Trans Control Syst Tech, 2015, 23: 389–396

    Article  Google Scholar 

  133. 133

    Dong X, Xi J, Lu G, et al. Formation control for high-order linear time-invariant multiagent systems with time delays. IEEE Trans Control Netw Syst, 2014, 1: 232–240

    MathSciNet  Article  Google Scholar 

  134. 134

    Han T, Lin Z, Fu M. Three-dimensional formation merging control under directed and switching topologies. Automatica, 2015, 58: 99–105

    MathSciNet  MATH  Article  Google Scholar 

  135. 135

    Reynolds C W. Flocks, herds and schools: a distributed behavioral model. SIGGRAPH Comput Graph, 1987, 21: 25–34

    Article  Google Scholar 

  136. 136

    Dong Y, Huang J. Flocking with connectivity preservation of multiple double integrator systems subject to external disturbances by a distributed control law. Automatica, 2015, 55: 197–203

    MathSciNet  Article  Google Scholar 

  137. 137

    Wen G, Duan Z, Su H, et al. A connectivity-preserving flocking algorithm for multi-agent dynamical systems with bounded potential function. IET Control Theory Appl, 2012, 6: 813–821

    MathSciNet  Article  Google Scholar 

  138. 138

    Zhang Q, Li P, Yang Z, et al. Adaptive flocking of non-linear multi-agents systems with uncertain parameters. IET Control Theory Appl, 2014, 9: 351–357

    MathSciNet  Article  Google Scholar 

  139. 139

    Semnani S H, Basir O A. Semi-flocking algorithm for motion control of mobile sensors in large-scale surveillance systems. IEEE Trans Cybernetics, 2015, 45: 129–137

    Article  Google Scholar 

  140. 140

    Zhang H T, Cheng Z, Chen G, et al. Model predictive flocking control for second-order multi-agent systems with input constraints. IEEE Trans Circuits Syst I: Regular Papers, 2015, 62: 1599–1606

    MathSciNet  Article  Google Scholar 

  141. 141

    Zhan J, Li X. Consensus of sampled-data multi-agent networking systems via model predictive control. Automatica, 2013, 49: 2502–2507

    MathSciNet  MATH  Article  Google Scholar 

  142. 142

    Chen Z, Zhang H T, Fan M C, et al. Algorithms and experiments on flocking of multiagents in a bounded space. IEEE Trans Control Syst Tech, 2014, 22: 1544–1549

    Article  Google Scholar 

  143. 143

    Shang Y, Bouffanais R. Influence of the number of topologically interacting neighbors on swarm dynamics. Sci Reports, 2014, 4: 4184

    Article  Google Scholar 

  144. 144

    Punzo G, Young G F, Macdonald M, et al. Using network dynamical influence to drive consensus. Sci Reports, 2016, 6: 26318

    Article  Google Scholar 

  145. 145

    Copenhagen K, Quint D A, Gopinathan A. Self-organized sorting limits behavioral variability in swarms. Sci Reports, 2016, 6: 31808

    Article  Google Scholar 

  146. 146

    Reyes L A V, Tanner H G. Flocking, formation control, and path following for a group of mobile robots. IEEE Trans Control Syst Tech, 2015, 23: 1268–1282

    Article  Google Scholar 

  147. 147

    Rubenstein M, Cornejo A, Nagpal R. Programmable self-assembly in a thousand-robot swarm. Science, 2014, 345: 795–799

    Article  Google Scholar 

  148. 148

    Zhan J, Li X. Flocking of multi-agent systems via model predictive control based on position-only measurements. IEEE Trans Industrial Inf, 2013, 9: 377–385

    Article  Google Scholar 

  149. 149

    Han T T, Ge S S. Styled-velocity flocking of autonomous vehicles: a systematic design. IEEE Trans Automat Control, 2015, 60: 2015–2030

    MathSciNet  MATH  Article  Google Scholar 

  150. 150

    Li X, Su H, Chen M Z. Flocking of networked euler–lagrange systems with uncertain parameters and time-delays under directed graphs. Nonlinear Dynam, 2016, 85: 415–424

    MathSciNet  MATH  Article  Google Scholar 

  151. 151

    Martin S, Girard A, Fazeli A, et al. Multiagent flocking under general communication rule. IEEE Trans Control Netw Syst, 2014, 1: 155–166

    MathSciNet  Article  Google Scholar 

  152. 152

    Cao Y, Ren W, Egerstedt M. Distributed containment control with multiple stationary or dynamic leaders in fixed and switching directed networks. Automatica, 2012, 48: 1586–1597

    MathSciNet  MATH  Article  Google Scholar 

  153. 153

    Zheng Y, Wang L. Containment control of heterogeneous multi-agent systems. Int J Control, 2014, 87: 1–8

    MathSciNet  MATH  Article  Google Scholar 

  154. 154

    Liu S, Xie L, Zhang H. Containment control of multi-agent systems by exploiting the control inputs of neighbors. Int J Robust Nonlinear Control, 2014, 24: 2803–2818

    MathSciNet  MATH  Article  Google Scholar 

  155. 155

    Dong X, Xi J, Lu G, et al. Containment analysis and design for high-order linear time-invariant singular swarm systems with time delays. Int J Robust Nonlinear Control, 2014, 24: 1189–1204

    MathSciNet  MATH  Article  Google Scholar 

  156. 156

    Dong X, Shi Z, Lu G, et al. Formation-containment analysis and design for high-order linear time-invariant swarm systems. Int J Robust Nonlinear Control, 2015, 25: 3439–3456

    MathSciNet  MATH  Article  Google Scholar 

  157. 157

    Liu H, Cheng L, Tan M, et al. Containment control of continuous-time linear multi-agent systems with aperiodic sampling. Automatica, 2015, 57: 78–84

    MathSciNet  MATH  Article  Google Scholar 

  158. 158

    Haghshenas H, Badamchizadeh M A, Baradarannia M. Adaptive containment control of nonlinear multi-agent systems with non-identical agents. Int J Control, 2015, 88: 1586–1593

    MathSciNet  MATH  Article  Google Scholar 

  159. 159

    Haghshenas H, Badamchizadeh M A, Baradarannia M. Containment control of heterogeneous linear multi-agent systems. Automatica, 2015, 54: 210–216

    MathSciNet  MATH  Article  Google Scholar 

  160. 160

    Okabe A, Boots B, Sugihara K, et al. Spatial Tessellations: Concepts and Applications of Voronoi Diagrams. Hoboken: John Wiley & Sons, 2009

    Google Scholar 

  161. 161

    Song C, Liu L, Feng G, et al. Persistent awareness coverage control for mobile sensor networks. Automatica, 2013, 49: 1867–1873

    MathSciNet  MATH  Article  Google Scholar 

  162. 162

    Kantaros Y, Thanou M, Tzes A. Distributed coverage control for concave areas by a heterogeneous robot–swarm with visibility sensing constraints. Automatica, 2015, 53: 195–207

    MathSciNet  Article  Google Scholar 

  163. 163

    Stergiopoulos Y, Thanou M, Tzes A. Distributed collaborative coverage-control schemes for non-convex domains. IEEE Trans Automat Control, 2015, 60: 2422–2427

    MathSciNet  MATH  Article  Google Scholar 

  164. 164

    Sharifi F, Mirzaei M, Zhang Y, et al. Cooperative multi-vehicle search and coverage problem in an uncertain environment. Unmanned Syst, 2015, 3: 35–47

    Article  Google Scholar 

  165. 165

    Miah S, Nguyen B, Bourque A, et al. Nonuniform coverage control with stochastic intermittent communication. IEEE Trans Automat Control, 2015, 60: 1981–1986

    MathSciNet  MATH  Article  Google Scholar 

  166. 166

    Xu Y, Salapaka S M, Beck C L. Clustering and coverage control for systems with acceleration-driven dynamics. IEEE Trans Automat Control, 2014, 59: 1342–1347

    MathSciNet  MATH  Article  Google Scholar 

  167. 167

    Jahangir M, Khosravi S, Afkhami H. A robust-adaptive fuzzy coverage control for robotic swarms. Nonlinear Dynam, 2012, 69: 1191–1201

    MathSciNet  MATH  Article  Google Scholar 

  168. 168

    Lee S G, Egerstedt M. Multirobot control using time-varying density functions. IEEE Trans Robotics, 2015, 31: 489–493

    Article  Google Scholar 

  169. 169

    Wongpiromsarn T, Topcu U, Murray R M. Synthesis of control protocols for autonomous systems. Unmanned Syst, 2013, 1: 21–39

    Article  Google Scholar 

  170. 170

    Darrah M, Wilhelm J, Munasinghe T, et al. A flexible genetic algorithm system for multi-uav surveillance: algorithm and flight testing. Unmanned Syst, 2015, 3: 49–62

    Article  Google Scholar 

  171. 171

    Sabo C, Kingston D, Cohen K. A formulation and heuristic approach to task allocation and routing of uavs under limited communication. Unmanned Syst, 2014, 2: 1–17

    Article  Google Scholar 

  172. 172

    Geng L, Zhang Y, Wang J, et al. Cooperative mission planning with multiple uavs in realistic environments. Unmanned Syst, 2014, 2: 73–86

    Article  Google Scholar 

  173. 173

    Miah S, Nguyen B, Bourque F A, et al. Nonuniform deployment of autonomous agents in harbor-like environments. Unmanned Syst, 2014, 2: 377–389

    Article  Google Scholar 

  174. 174

    Mo Y, Sinopoli B. Kalman filtering with intermittent observations: Tail distribution and critical value. IEEE Trans Automat Control, 2012, 57: 677–689

    MathSciNet  Article  Google Scholar 

  175. 175

    Sui T, You K, Fu M, et al. Stability of mmse state estimators over lossy networks using linear coding. Automatica, 2015, 51: 167–174

    MathSciNet  MATH  Article  Google Scholar 

  176. 176

    You K. Recursive algorithms for parameter estimation with adaptive quantizer. Automatica, 2015, 52: 192–201

    MathSciNet  MATH  Article  Google Scholar 

  177. 177

    Marelli D, You K, Fu M. Identification of arma models using intermittent and quantized output observations. Automatica, 2013, 49: 360–369

    MathSciNet  MATH  Article  Google Scholar 

  178. 178

    Han D, Cheng P, Chen J, et al. An online sensor power schedule for remote state estimation with communication energy constraint. IEEE Trans Automat Control, 2014, 59: 1942–1947

    MathSciNet  MATH  Article  Google Scholar 

  179. 179

    Han D, Mo Y, Wu J, et al. Stochastic event-triggered sensor schedule for remote state estimation. IEEE Trans Automat Control, 2015, 60: 2661–2675

    MathSciNet  MATH  Article  Google Scholar 

  180. 180

    Shi D, Chen T, Shi L. Event-triggered maximum likelihood state estimation. Automatica, 2014, 50: 247–254

    MathSciNet  MATH  Article  Google Scholar 

  181. 181

    Liu Q, Wang Z, He X, et al. Event-based recursive distributed filtering over wireless sensor networks. IEEE Trans Automat Control, 2015, 60: 2470–2475

    MathSciNet  MATH  Article  Google Scholar 

  182. 182

    Zhao S, Zhou R. Cooperative guidance for multimissile salvo attack. Chinese J Aeronaut, 2008, 21: 533–539

    Article  Google Scholar 

  183. 183

    Hou D, Wang Q, Sun X, et al. Finite-time cooperative guidance laws for multiple missiles with acceleration saturation constraints. IET Control Theory Appl, 2015, 9: 1525–1535

    MathSciNet  Article  Google Scholar 

  184. 184

    Wei X, Wang Y, Dong S, et al. A three-dimensional cooperative guidance law of multimissile system. Int J Aerospace Eng, 2015, 2015: 479427

    Google Scholar 

  185. 185

    Jeon I S, Lee J I, Tahk M J. Homing guidance law for cooperative attack of multiple missiles. J Guidance Control Dynam, 2010, 33: 275–280

    Article  Google Scholar 

  186. 186

    Liu S, Quevedo D E, Xie L. Event-triggered distributed constrained consensus. Int J Robust Nonlinear Control, 2016, in press. doi: 10.1002/rnc.3724

    Google Scholar 

  187. 187

    Liu S, Xie L, Quevedo D E. Event-triggered quantized communication based distributed convex optimization. IEEE Trans Control Netw Syst, 2016, in press. doi: 10.1109/TCNS.2016.2585305

    Google Scholar 

  188. 188

    Meng X, Chen T. Event based agreement protocols for multi-agent networks. Automatica, 2013, 49: 2125–2132

    MathSciNet  MATH  Article  Google Scholar 

  189. 189

    Xiao F, Meng X, Chen T. Sampled-data consensus in switching networks of integrators based on edge events. Int J Control, 2015, 88: 391–402

    MathSciNet  MATH  Article  Google Scholar 

  190. 190

    Vahidalizadehdizaj M, Jadav J, Tao L. Security challenges in swarm intelligence. In: Proceedings of the 6th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Dallas-Fortworth, 2015. 1–4

    Google Scholar 

  191. 191

    Winfield A F, Nembrini J. Safety in numbers: fault-tolerance in robot swarms. Int J Modelling Identification Control, 2006, 1: 30–37

    Article  Google Scholar 

  192. 192

    Petnga L, Xu H. Security of unmanned aerial vehicles: dynamic state estimation under cyber-physical attacks. In: Proceedings of International Conference on Unmanned Aircraft Systems (ICUAS), Arlington, 2016. 811–819

    Google Scholar 

  193. 193

    Han D, Mo Y, Xie L. Towards a unified resilience analysis: state estimation against integrity attacks. arXiv:1604.07549, 2016

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Bing Zhu.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhu, B., Xie, L., Han, D. et al. A survey on recent progress in control of swarm systems. Sci. China Inf. Sci. 60, 070201 (2017). https://doi.org/10.1007/s11432-016-9088-2

Download citation

Keywords

  • formation
  • swarm systems
  • consensus
  • containment
  • flocking
  • sensor networks