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

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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.

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References

  1. E. Camponogara, D. Jia, B.H. Krogh, S. Talukdar, Distributed model predictive control. IEEE Control Syst. Mag. 22(1), 44–52 (2002)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  7. W.B. Dunbar, R.M. Murray, Distributed receding horizon control for multi-vehicle formation stabilization. Automatica 42(4), 549–558 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  9. J. Gao, Y. Xu, R. Lu, Output regulation of linear singular multi-agent systems. Circuits Syst. Signal Process 36(3), 1–16 (2016)

    MathSciNet  Google Scholar 

  10. M.V. Kothare, V. Balakrishnan, M. Morari, Robust constrained model predictive control using linear matrix inequalities. Automatica 32(10), 1361–1379 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  11. H. Li, Y. Shi, Event-triggered robust model predictive control of continuous-time nonlinear systems. Automatica 50(5), 1507–1513 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  16. H. Pei, S. Chen, Q. Lai, A local flocking algorithm of multi-agent dynamic systems. Int. J. Control 88(11), 2242–2249 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  17. A. Richards, J.P. How, Robust distributed model predictive control. Int. J. Control 80(9), 1517–1531 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  18. C. Wang, C.J. Ong, Distributed model predictive control of dynamically decoupled systems with coupled cost. Automatica 46(12), 2053–2058 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  21. M. Zhao, B. Ding, Distributed model predictive control for constrained nonlinear systems with decoupled local dynamics. ISA Trans. 55(11), 1–12 (2015)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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 

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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).

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  1. Department of Automation, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

    Yuanqing Yang & Baocang Ding

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  1. Yuanqing Yang
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  2. Baocang Ding
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Correspondence to Baocang Ding.

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Yang, Y., Ding, B. Tracking and Formation of Multi-agent Systems with Collision and Obstacle Avoidance Based on Distributed RHC. Circuits Syst Signal Process 38, 2951–2970 (2019). https://doi.org/10.1007/s00034-018-1003-5

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  • DOI: https://doi.org/10.1007/s00034-018-1003-5

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