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Event-Triggered Communication Mechanism for Distributed Flocking Control of Nonholonomic Multi-agent System

  • Weiwei Xun
  • Wei YiEmail author
  • Xi Liu
  • Xiaodong Yi
  • Yanzhen Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10942)

Abstract

As the scale of multi-agent systems (MAS) increases, communication becomes a bottleneck. In this paper, we propose an event-triggered mechanism to reduce the inter-agent communication cost for the distributed control of MAS. Communication of an agent with others only occurs when event triggering condition (ETC) is met. In the absence of communication, other agents adopt an estimation process to acquire the required information about the agent. Each agent has an above estimation process for itself and another estimation based on Kalman Filter, the latter can represent its actual state considering the measurement value and error from sensors. The error between the two estimators indicates whether the estimator in other agents can maintain a relatively accurate state estimation for this agent, and decides whether the communication is triggered. Simulations demonstrate the effectiveness and advantages of the proposed method for the distributed control of flocking in both Matlab and Gazebo.

Keywords

Event-triggered communication scheme Distributed control Multi-agent systems Flocking 

Notes

Acknowledgements

This work was supported by NSFC under Grant 91648204 and 61303185 and HPCL Grants under 201502-01.

References

  1. 1.
    Reynolds, C.W.: Flocks, herds and schools: a distributed behavioral model. ACM SIGGRAPH Comput. Graph. 21(4), 25–34 (1987)CrossRefGoogle Scholar
  2. 2.
    Zavlanos, M.M., Jadbabaie, A., Pappas, G.J.: Flocking while preserving network connectivity. In: 2007 46th IEEE Conference on Decision and Control, pp. 2919–2924. IEEE (2007)Google Scholar
  3. 3.
    Olfati-Saber, R.: Flocking for multi-agent dynamic systems: algorithms and theory. IEEE Trans. Autom. Control 51, 401–420 (2006)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Varga, M., Basiri, M., Heitz, G., Floreano, D.: Distributed formation control of fixed wing micro aerial vehicles for area coverage. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 669–674. IEEE (2015)Google Scholar
  5. 5.
    Cai, Z., Chang, X., Wang, Y., Yi, X., Yang, X.J.: Distributed control for flocking and group maneuvering of nonholonomic agents. Comput. Animat. Virtual Worlds 28(3–4) (2017)CrossRefGoogle Scholar
  6. 6.
    Shang, Y., Ye, Y.: Leader-follower fixed-time group consensus control of multiagent systems under directed topology. Complexity 2017 (2017)Google Scholar
  7. 7.
    Yazdani, S., Haeri, M.: Robust adaptive fault-tolerant control for leader-follower flocking of uncertain multi-agent systems with actuator failure. ISA Trans. 71, 227–234 (2017)CrossRefGoogle Scholar
  8. 8.
    Rezaee, H., Abdollahi, F.: Pursuit formation of double-integrator dynamics using consensus control approach. IEEE Trans. Ind. Electron. 62(7), 4249–4256 (2015)CrossRefGoogle Scholar
  9. 9.
    Pan, W., Jiang, D., Pang, Y., Qi, Y., Luo, D.: Distributed formation control of autonomous underwater vehicles based on flocking and consensus algorithms. In: Huang, Y.A., Wu, H., Liu, H., Yin, Z. (eds.) ICIRA 2017, Part I. LNCS (LNAI), vol. 10462, pp. 735–744. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-65289-4_68CrossRefGoogle Scholar
  10. 10.
    Heemels, W.P.M.H., Johansson, K.H., Tabuada, P.: An introduction to event-triggered and self-triggered control. In: 2012 IEEE 51st Annual Conference on Decision and Control (CDC), pp. 3270–3285. IEEE (2012)Google Scholar
  11. 11.
    Dimarogonas, D.V., Frazzoli, E., Johansson, K.H.: Distributed event-triggered control for multi-agent systems. IEEE Trans. Autom. Control 57(5), 1291–1297 (2012)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Sun, Z., Liu, Q., Yu, C., Anderson, B.D.: Generalized controllers for rigid formation stabilization with application to event-based controller design. In: 2015 European Control Conference (ECC), pp. 217–222. IEEE (2015)Google Scholar
  13. 13.
    Ge, X., Han, Q.: Distributed formation control of networked multi-agent systems using a dynamic event-triggered communication mechanism. IEEE Trans. Ind. Electron. 64, 8118–8127 (2017)CrossRefGoogle Scholar
  14. 14.
    Jain, R.P., Aguiar, A.P., Sousa, J.: Self-triggered cooperative path following control of fixed wing unmanned aerial vehicles. In: 2017 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1231–1240. IEEE (2017)Google Scholar
  15. 15.
    Zhou, L., Tokekar, P.: Active target tracking with self-triggered communications. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 2117–2123. IEEE (2017)Google Scholar
  16. 16.
    Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Basic Eng. Trans. 82, 35–45 (1960)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Weiwei Xun
    • 1
  • Wei Yi
    • 1
    • 2
    Email author
  • Xi Liu
    • 3
  • Xiaodong Yi
    • 1
    • 2
  • Yanzhen Wang
    • 1
    • 2
  1. 1.State Key Laboratory of High Performance Computing (HPCL), School of ComputerNational University of Defense TechnologyChangshaChina
  2. 2.Artificial Intelligence Research CenterNational Innovation Institute of Defense TechnologyChangshaChina
  3. 3.PLA Army Engineering UniversityNanjingChina

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