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Coordination of Multi-agent Systems with Intermittent Access to a Cloud Repository

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Part of the book series: Lecture Notes in Control and Information Sciences ((LNCIS,volume 474))

Abstract

A cloud-supported multi-agent system is composed of autonomous agents required to achieve a common coordination objective by exchanging data over a shared cloud repository. The repository is accessed asychronously by different agents, and direct inter-agent commuication is not possible. This model is motivated by the problem of coordinating a fleet of autonomous underwater vehicles , with the aim to avoid the use of expensive and power-hungry modems for underwater communication. For the case of agents with integrator dynamics, a control law and a rule for scheduling the cloud access are formally defined and proven to achieve the desired coordination. A numerical simulation corroborate the theoretical results.

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References

  1. Adaldo, A., Liuzza, D., Dimarogonas, D.V., Johansson, K.H.: Control of multi-agent systems with event-triggered cloud access. In: European Control Conference, Linz, Austria (2015)

    Google Scholar 

  2. Adaldo, A., Liuzza, D., Dimarogonas, D.V., Johansson, K.H.: Multi-agent trajectory tracking with event-triggered cloud access. In: IEEE Conference on Decision and Control (2016)

    Google Scholar 

  3. Bullo, F., Cortes, J., Martinez, S.: Distributed Control of Robotic Networks. Princeton University Press, Princeton (2009)

    Google Scholar 

  4. Dimarogonas, D.V., Johansson, K.H., Event-triggered control for multi-agent systems. In: IEEE Conference on Decision and Control (2009)

    Google Scholar 

  5. Durham, J.W., Carli, R., Frasca, P., Bullo, F.: Dynamic partitioning and coverage control with asynchronous one-to-base-station communication. IEEE Trans. Control Netw. Syst. 3(1), 24–33 (2016)

    Google Scholar 

  6. Fiorelli, E., Leonard, N.E., Bhatta, P., Paley, D.A., Bachmayer, R., Fratantoni, D.M.: Multi-AUV control and adaptive sampling in Monterey Bay. IEEE J. Ocean. Eng. 31(4), 935–948 (2006)

    Google Scholar 

  7. Hale, M.T., Egerstedt, M.: Differentially private cloud-based multi-agent optimization with constraints. In: Proceedings of the American Control Conference, Chicago, IL, USA (2015)

    Google Scholar 

  8. Heemels, W.P.M.H., Johansson, K.H., Tabuada, P.: An introduction to event-triggered and self-triggered control. In: IEEE Conference on Decision and Control (2012)

    Google Scholar 

  9. Mesbahi, M., Egerstedt, M.: Graph Theoretic Methods in Multiagent Networks. Princeton Univerisity Press, Princeton (2010)

    Google Scholar 

  10. Newman, M.E.J.: Networks: An Introduction. Oxford University Press, Oxford (2010)

    Google Scholar 

  11. Nowzari, C., Pappas, G.J.: Multi-agent coordination with asynchronous cloud access. In: American Control Conference (2016)

    Google Scholar 

  12. Paull, L., Saeedi, S., Seto, M., Li, H.: AUV navigation and localization: a review. IEEE J. Ocean. Eng. 39(1), 131–149 (2014)

    Article  Google Scholar 

  13. Teixeira, P.V., Dimarogonas, D.V., Johansson, K.H.: Multi-agent coordination with event-based communication. In: American Control Conference, Marriot Waterfront, Baltimore, MD, USA, (2011)

    Google Scholar 

  14. Zeng, Z., Wang, X., Zheng, Z.: Convergence analysis using the edge Laplacian: robust consensus of nonlinear multi-agent systems via ISS method. Int. J. Robust Nonlinear Control 26, 1051–1072 (2015)

    Article  MATH  MathSciNet  Google Scholar 

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Acknowledgements

This work has received funding from the European Union Horizon 2020 Research and Innovation Programme under the Grant Agreement No. 644128, AEROWORKS, from the Swedish Foundation for Strategic Research, from the Swedish Research Council, and from the Knut och Alice Wallenberg foundation.

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Correspondence to Antonio Adaldo .

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Appendix

Appendix

Lemma 4

The agents’ dynamics (15) and the objective function (16) satisfy Assumption 4 with \(\beta =\Vert B_\mathcal {T}\sqrt{P}\Vert \), \(\gamma =\frac{2\min {{\mathrm{eig}}}(Q)}{\max {{\mathrm{eig}}}(P)}\), and \(v_i(x)\) given by (17).

Proof

Taking the derivative of (16), we have

$$\begin{aligned} \frac{\partial V(x)}{\partial x} = (B_\mathcal {T}P B_\mathcal {T}^\top \otimes I_2) (x-b), \end{aligned}$$

which, taking norms of both sides and applying the triangular inequality, gives

$$\begin{aligned} \bigg \Vert \frac{\partial V(x)}{\partial x}\bigg \Vert \le \Vert B_\mathcal {T}\sqrt{P}\Vert \sqrt{V(x)}. \end{aligned}$$

Moreover, if we take the feedback law \(v_i(x)=\sum _{j\in \mathcal {V}_i} (x_j(t)-b_j-x_i(t)+b_i)\), then we have \(v(x)=-(CB^\top \otimes I_2)(x-b)\), and

$$\begin{aligned} \begin{aligned} \frac{\partial V(x)}{\partial x}^\top F(x,v(x),0_{n_d}) =&-(x-b)^\top (B_\mathcal {T}P B_\mathcal {T}^\top CB^\top \otimes I_2)(x-b)\\ =&-(x-b)^\top (B_\mathcal {T}PR B_\mathcal {T}^\top \otimes I_2) (x-b)\\ =&-(x-b)^\top (B_\mathcal {T}Q B_\mathcal {T}^\top \otimes I_2) (x-b)\\ \le&-\min {{\mathrm{eig}}}(Q)\Vert (B_\mathcal {T}\otimes I_2)(x-b)\Vert ^2. \end{aligned} \end{aligned}$$
(40)

Noting that \(V(x)\le \frac{1}{2}\max {{\mathrm{eig}}}(P)\Vert (B_\mathcal {T}\otimes I_2)(x-b)\Vert ^2\), we can further bound (40) as

$$\begin{aligned} \begin{aligned} \frac{\partial V(x)}{\partial x}^\top F(x,v(x),0_{n_d}) \le&-\frac{2\min {{\mathrm{eig}}}(Q)}{\max {{\mathrm{eig}}}(P)}V(x). \end{aligned} \end{aligned}$$

which completes the proof.\(\square \)

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Adaldo, A., Liuzza, D., Dimarogonas, D.V., Johansson, K.H. (2017). Coordination of Multi-agent Systems with Intermittent Access to a Cloud Repository. In: Fossen, T., Pettersen, K., Nijmeijer, H. (eds) Sensing and Control for Autonomous Vehicles. Lecture Notes in Control and Information Sciences, vol 474. Springer, Cham. https://doi.org/10.1007/978-3-319-55372-6_21

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  • DOI: https://doi.org/10.1007/978-3-319-55372-6_21

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