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Kernel Design and Distributed, Self-Triggered Control for Coordination of Autonomous Multi-Agent Configurations

Published online by Cambridge University Press:  27 March 2018

Levi DeVries ,
Aaron Sims  and
Michael D. M. Kutzer
Levi DeVries*
Affiliation:
Department of Weapons and Systems Engineering, United States Naval Academy, Annapolis, Maryland, 21402, USA. E-mails: [email protected], [email protected]
Aaron Sims
Affiliation:
Department of Weapons and Systems Engineering, United States Naval Academy, Annapolis, Maryland, 21402, USA. E-mails: [email protected], [email protected]
Michael D. M. Kutzer
Affiliation:
Department of Weapons and Systems Engineering, United States Naval Academy, Annapolis, Maryland, 21402, USA. E-mails: [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

Autonomous multi-agent systems show promise in countless applications, but can be hindered in environments where inter-agent communication is limited. In such cases, this paper considers a scenario where agents communicate intermittently through a cloud server. We derive a graph transformation mapping the kernel of a graph's Laplacian to a desired configuration vector while retaining graph topology characteristics. The transformation facilitates derivation of a self-triggered controller driving agents to prescribed configurations while regulating instances of inter-agent communication. Experimental validation of the theoretical results shows the self-triggered approach drives agents to a desired configuration using fewer control updates than traditional periodic implementations.

Keywords

Consensus-based controlSelf-triggered controlMulti-agent controlGraph transformationAsynchronous control
Type
Articles
Information
Robotica , Volume 36 , Issue 7 , July 2018 , pp. 1077 - 1097
Creative Commons
This is a work of the U.S. Government and is not subject to copyright protection in the United States.
Copyright
Copyright © Cambridge University Press 2018

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References

1. Nowzari, C. and Pappas, G. J., “Multi-Agent Coordination with Asynchronous Cloud Access,” Proceedings of the American Control Conference, Boston, MA, (2016) pp. 4649–4654.Google Scholar
2. Bowman, S. L., Nowzari, C. and Pappas, G. J., “Coordination of Multi-Agent Systems via Asynchronous Cloud Communication,” Proceedings of the 55th IEEE Conference on Decision and Control, Las Vegas, NV (IEEE, 2016) pp. 2215–2220.CrossRefGoogle Scholar
3. Wit, J. S., Vector Pursuit Path Tracking for Autonomous Ground Vehicles, Ph.D. Thesis, (Department of Mechanical Engineering, University of Florida, 2000).Google Scholar
4. Nowzari, C. and Cortes, J., “Team-triggered coordination for real-time control of networked cyber-physical systems,” IEEE Trans. Autom. Control 61 (1), 34–47 (2016).Google Scholar
5. Degroot, M. H., “Reaching a consensus,” J. Am. Stat. Assoc. 69 (345), 118–121 (1974).CrossRefGoogle Scholar
6. Qi, X. and Cai, Z.-J., “Three-dimensional formation control based on filter backstepping method for multiple underactuated underwater vehicles,” Robotica 35 (8), 1690–1711 (2017).CrossRefGoogle Scholar
7. Tahir, M. and Mazumder, S. K., “Self-triggered communication enabled control of distributed generation in microgrids,” IEEE Trans. Ind. Inform. 11 (2), 441–449 (2015).Google Scholar
8. Cao, Y., Yu, W., Ren, W., and Chen, G., “An overview of recent progress in the study of distributed multi-agent coordination,” IEEE Trans. Ind. Inform. 9 (1), 427–438 (2013).CrossRefGoogle Scholar
9. Baikerikar, J., Surve, S. and Prabhu, S., “Consensus Based Dynamic Load Balancing for a Network of Heterogeneous Workstations,” In: Advances in Computing, Communication and Control (Unnikrishnan, S., Surve, S. and Bhoir, D., eds.) (Springer, Berlin, Heidelberg, 2011) pp. 116–124.Google Scholar
10. Ren, W., “Multi-vehicle consensus with a time-varying reference state,” Syst. Control Lett. 56, 474–483 (2007).Google Scholar
11. Chapman, A., Advection on Graphs (Springer International Publishing, Cham, 2015) pp. 3–16.Google Scholar
12. Falconi, R., Sabattini, L., Secchi, C., Fantuzzi, C. and Melchiorri, C., “Edge-weighted consensus-based formation control strategy with collision avoidance,” Robotica 33 (2), 332–347 (2015).CrossRefGoogle Scholar
13. Seng, W. L., Barca, J. C. and Sekercioǧlu, Y. A., “Distributed formation control of networked mobile robots in environments with obstacles,” Robotica 34 (34), 1403–1415 (2016).Google Scholar
14. Heemels, W., Johansson, K. and Tabuada, P., “An Introduction to Event-Triggered and Self-Triggered Control,” Proceedings of the 51st Conference on Decision and Control, Maui, HI (2012) pp. 3270–3285.Google Scholar
15. Mesbahi, M. and Egerstedt, M., Graph Theoretic Methods in Multiagent Networks (Princeton University Press, Princeton, New Jersey, USA, 2010).CrossRefGoogle Scholar
16. Olfati-Saber, R., Fax, J. A. and Murray, R. M., “Consensus and cooperation in networked multi-agent systems,” Proc. IEEE 95 (1), 215–233 (Jan. 2007).Google Scholar
17. Fax, J. A. and Murray, R. M., “Information flow and cooperative control of vehicle formations,” IEEE Trans. Autom. Control 49 (9), 1465–1476 (Sep. 2004).Google Scholar
18. Ren, W. and Beard, R., Distributed Consensus in Multivehicle Cooperative Control: Theory and Applications (Springer-Verlag, London, 2008).Google Scholar
19. Yamchi, M. H. and Esfanjani, R. M., “Formation control of networked mobile robots with guaranteed obstacle and collision avoidance,” Robotica 35 (6), 1365–1377 (Jun. 2017).CrossRefGoogle Scholar
20. Dimarogonas, D. V., Frazzoli, E. and Johansson, K. H., “Distributed event-triggered control for multi-agent systems,” IEEE Trans. Autom. Control 57 (5), 1291–1297 (2012).CrossRefGoogle Scholar
21. Tabuada, P., “Event-triggered real-time scheduling of stabilizing control tasks,” IEEE Trans. Autom. Control 52 (9), 1680–1685 (2007).Google Scholar
22. Araújo, J., Anta, A., Mazo, M., Faria, J., Hernandez, A., Tabuada, P. and Johansson, K. H., “Self-Triggered Control Over Wireless Sensor and Actuator Networks,” Proceedings of the International Conference on Distributed Computing in Sensor Systems and Workshops DCOSS, Barcelona, Spain (2011) pp. 1–9.Google Scholar
23. Liu, T. and Jiang, Z. P., “A small-gain approach to robust event-triggered control of nonlinear systems,” IEEE Trans. Autom. Control 60 (8), 2072–2085 (2015).Google Scholar
24. Teixeira, P. V., Dimarogonas, D. V., Johansson, K. H. and Sousa, J., “Event-Based Motion Coordination of Multiple Underwater Vehicles Under Disturbances,” Proceedings of the OCEANS Conference OCEANS 2010, Sydney, Australia (2010) pp. 1–6.Google Scholar
25. Adaldo, A., Liuzza, D., Dimarogonas, D. V. and Johansson, K. H., “Control of Multi-Agent Systems with Event-Triggered Cloud Access,” Proceedings of the European Control Conference, Linz, Austria (2015) pp. 954–961.Google Scholar
26. Bondy, A. and Murty, U., Graph Theory, 1st ed. (Springer-Verlag, London, 2008).Google Scholar
27. Moreau, L., “Stability of multiagent systems with time-dependent communication links,” IEEE Trans. Autom. Control 50 (2), 169–182 (2005).CrossRefGoogle Scholar
28. Horn, R., Matrix Analysis (Cambridge University Press, New York, NY, 1985).Google Scholar
29. DeVries, L. and Kutzer, M. D. M., “Kernel Design for Coordination of Autonomous, Time-Varying Multi-Agent Configurations,” Proceedings of the American Control Conference, Boston, MA (2016) pp. 1975–1980.Google Scholar
30. Sepulchre, R., Paley, D. A. and Leonard, N. E., “Stabilization of planar collective motion: All-to-all communication,” IEEE Trans. Autom. Control 52, 811–824 (May 2007).Google Scholar
31. Khalil, H. K., Nonlinear Systems, 3rd ed. (Prentice Hall, Upper Saddle River, New Jersey, USA, 2002).Google Scholar
32. Nise, N., Control Systems Engineering, 7th ed. (Wiley & Sons, Hoboken, NJ, 2015).Google Scholar
33. Xinghua, C. and Juan, L., “AUV Planner Tracking Control Based on the Line of Sight Guidance Method,” Proceedings of the IEEE Conference on Mechatronics and Automation, Tianjin, China (2014) pp. 1204–1208.Google Scholar
34. Santos, M. C. P., Sarcinelli-Filho, M. and Carelli, R., “Trajectory Tracking for UAV with Saturation of Velocities,” Proceedings of the International Conference on Unmanned Aircraft Systems ICUAS, Arlington, VA (2016) pp. 643–648.Google Scholar
35. Meenakshi, A. and Rajian, C., “On a product of positive semidefinite matrices,” Linear Algebra Appl. 295 (1-3), 3–6 (1999).CrossRefGoogle Scholar
36. Mellinger, D., Trajectory Generation and Control for Quadrotors Ph.D. Dissertation (Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, 2012).Google Scholar
37. Acikmese, B., “Spectrum of Laplacians for graphs with self-loops,” ArXiv:1505.08133 [math.OC] (2015).Google Scholar
38. Acikmese, B., Mandić, M. and Speyer, J. L., “Decentralized observers with consensus filters for distributed discrete-time linear systems,” Automatica 50 (4), 1037–1052 (2014).Google Scholar