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Circuits, Systems, and Signal Processing

, Volume 38, Issue 7, pp 2951–2970 | Cite as

Tracking and Formation of Multi-agent Systems with Collision and Obstacle Avoidance Based on Distributed RHC

  • Yuanqing Yang
  • Baocang DingEmail author
Article

Abstract

This paper proposes a distributed receding horizon control approach for the formation and tracking problems of multi-agent systems with collision and obstacle avoidance. We design an algorithm to enlarge the terminal position sets of the agents in sequential order. Since the proposed approach is based on the synchronous framework, each agent must utilize the assumed predictive information of its neighbors. A compatibility constraint is reformulated for the local optimization, which restricts the deviation between the assumed and true predictive states. To ensure the safety of each agent, the deviation-dependent collision-avoidance constraint and the obstacle-avoidance constraint are designed. Moreover, the closed-loop multi-agent systems are guaranteed to be exponentially stable, and the control performance is improved compared with the previous approaches. A simulation example is provided to illustrate the advantages of the proposed approach.

Keywords

Receding horizon control (RHC) Distributed control Multi-agent systems Collision avoidance Obstacle avoidance 

Notes

Acknowledgements

This work is supported by National Key R&D Program of China (No. 2017YFA0700300), by Natural Science Foundation of China (No. 61573269), by NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization (No. U1509209).

References

  1. 1.
    E. Camponogara, D. Jia, B.H. Krogh, S. Talukdar, Distributed model predictive control. IEEE Control Syst. Mag. 22(1), 44–52 (2002)CrossRefGoogle Scholar
  2. 2.
    P.D. Christofides, R. Scattolini, D.M. de la Peña, J. Liu, Distributed model predictive control: a tutorial review and future research directions. Comput. Chem. Eng. 51(14), 21–41 (2013)CrossRefGoogle Scholar
  3. 3.
    L. Dai, Q. Cao, Y. Xia, Y. Gao, Distributed MPC for formation of multi-agent systems with collision avoidance and obstacle avoidance. J. Frankl. Inst. 354(4), 2068–2085 (2017)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    L. Dai, Y. Xia, Y. Gao, M. Cannon, Distributed stochastic MPC of linear systems with additive uncertainty and coupled probabilistic constraints. IEEE Trans. Autom. Control 62(7), 3474–3481 (2017)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    B. Ding, L. Xie, W. Cai, Distributed model predictive control for constrained linear systems. Int. J. Robust Nonlinear Control 20(11), 1285–1298 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    W.B. Dunbar, D.S. Caveney, Distributed receding horizon control of vehicle platoons: stability and string stability. IEEE Trans. Autom. Control 57(3), 620–633 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    W.B. Dunbar, R.M. Murray, Distributed receding horizon control for multi-vehicle formation stabilization. Automatica 42(4), 549–558 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    G. Franzè, A. Casavola, D. Famularo, W. Lucia, Distributed receding horizon control of constrained networked leader-follower formations subject to packet dropouts. IEEE Trans. Control Syst. Technol. 26(5), 1798–1809 (2017)CrossRefGoogle Scholar
  9. 9.
    J. Gao, Y. Xu, R. Lu, Output regulation of linear singular multi-agent systems. Circuits Syst. Signal Process 36(3), 1–16 (2016)MathSciNetGoogle Scholar
  10. 10.
    M.V. Kothare, V. Balakrishnan, M. Morari, Robust constrained model predictive control using linear matrix inequalities. Automatica 32(10), 1361–1379 (1996)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    H. Li, Y. Shi, Event-triggered robust model predictive control of continuous-time nonlinear systems. Automatica 50(5), 1507–1513 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    H. Li, Y. Shi, W. Yan, On neighbour information utilization in distributed receding horizon control for consensus-seeking. IEEE Trans. Cybern. 46(9), 2019–2027 (2016)CrossRefGoogle Scholar
  13. 13.
    T. Li, F. Wu, J.F. Zhang, Multi-agent consensus with relative-state-dependent measurement noises. IEEE Trans. Autom. Control 59(9), 2463–2468 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    C. Liu, J. Gao, H. Li, D. Xu, Aperiodic robust model predictive control for constrained continuous-time nonlinear systems: an event-triggered approach. IEEE Trans. Cybern. 48(5), 1397–1405 (2018)CrossRefGoogle Scholar
  15. 15.
    C. Liu, H. Li, J. Gao, D. Xu, Robust self-triggered min–max model predictive control for discrete-time nonlinear systems. Automatica 48, 333–339 (2018)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    H. Pei, S. Chen, Q. Lai, A local flocking algorithm of multi-agent dynamic systems. Int. J. Control 88(11), 2242–2249 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    A. Richards, J.P. How, Robust distributed model predictive control. Int. J. Control 80(9), 1517–1531 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    C. Wang, C.J. Ong, Distributed model predictive control of dynamically decoupled systems with coupled cost. Automatica 46(12), 2053–2058 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    A. Wächter, L.T. Biegler, On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Math. Program. 106(1), 25–57 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    P. Wang, B. Ding, A synthesis approach of distributed model predictive control for multi-agent system with collision avoidance. Int. J. Control 87(1), 52–63 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    M. Zhao, B. Ding, Distributed model predictive control for constrained nonlinear systems with decoupled local dynamics. ISA Trans. 55(11), 1–12 (2015)CrossRefGoogle Scholar
  22. 22.
    Z. Zhong, L. Sun, J. Wang, P. Lv, H. Zheng, Consensus for first- and second-order discrete-time multi-agent systems with delays based on model predictive control schemes. Circuits Syst. Signal Process 34(1), 127–152 (2017)CrossRefzbMATHGoogle Scholar
  23. 23.
    L. Zhou, S. Li, Distributed model predictive control for consensus of sampled-data multi-agent systems with double-integrator dynamics. IET Control Theory Appl. 9(12), 1774–1780 (2015)MathSciNetCrossRefGoogle Scholar
  24. 24.
    B. Zhu, A.H.B. Zaini, L. Xie, Distributed guidance for interception by using multiple rotary-wing unmanned aerial vehicles. IEEE Trans. Control Syst. Technol. 64(7), 5648–5656 (2017)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Automation, School of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’anChina

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