American Institute of Mathematical Sciences

Advanced
December  2013, 3(4): 447-466. doi: 10.3934/mcrf.2013.3.447

Sparse stabilization and optimal control of the Cucker-Smale model

Marco Caponigro 1, , Massimo Fornasier 2, , Benedetto Piccoli 3, and  Emmanuel Trélat 4,

1. 

Conservatoire National des Arts et Métiers, Département Ingénierie Mathématique (IMATH), Équipe M2N, 292 rue Saint-Martin, 75003, Paris,, France
2. 

Technische Universität München, Facultät Mathematik, Boltzmannstrasse 3, D-85748, Garching bei München, Germany
3. 

Rutgers University, Department of Mathematics, Business & Science Building Room 325, Camden, NJ 08102, United States
4. 

Université Pierre et Marie Curie (Univ. Paris 6) and Institut Universitaire de France and Team GECO Inria Saclay, CNRS UMR 7598, Laboratoire Jacques-Louis Lions, F-75005, Paris, France

Received  November 2012 Revised  June 2013 Published  September 2013

This article is mainly based on the work [7], and it is dedicated to the 60th anniversary of B. Bonnard, held in Dijon in June 2012.
    We focus on a controlled Cucker--Smale model in finite dimension. Such dynamics model self-organization and consensus emergence in a group of agents. We explore how it is possible to control this model in order to enforce or facilitate pattern formation or convergence to consensus. In particular, we are interested in designing control strategies that are componentwise sparse in the sense that they require a small amount of external intervention, and also time sparse in the sense that such strategies are not chattering in time. These sparsity features are desirable in view of practical issues.
    We first show how very simple sparse feedback strategies can be designed with the use of a variational principle, in order to steer the system to consensus. These feedbacks are moreover optimal in terms of decay rate of some functional, illustrating the general principle according to which ``sparse is better''. We then combine these results with local controllability properties to get global controllability results. Finally, we explore the sparsity properties of the optimal control minimizing a combination of the distance from consensus and of a norm of the control.
Keywords: Cucker--Smale model, $l_1$-norm minimization, local controllability, sparse optimal control., consensus emergence, sparse stabilization.
Mathematics Subject Classification: Primary: 49J15, 93D15; Secondary: 65K10, 93B0.
Citation: Marco Caponigro, Massimo Fornasier, Benedetto Piccoli, Emmanuel Trélat. Sparse stabilization and optimal control of the Cucker-Smale model. Mathematical Control & Related Fields, 2013, 3 (4) : 447-466. doi: 10.3934/mcrf.2013.3.447
References:
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M. Caponigro, M. Fornasier, B. Piccoli and E. Trélat, Sparse stabilization and control of the Cucker-Smale model,, , ().   Google Scholar

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show all references

References:
[1]

M. Ballerini, N. Cabibbo, R. Candelier, A. Cavagna, E. Cisbani, L. Giardina, L. Lecomte, A. Orlandi, G. Parisi, A. Procaccini, M. Viale and V. Zdravkovic, Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study,, PNAS, 105 (2008), 1232.  doi: 10.1073/pnas.0711437105.  Google Scholar

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[3]

A. Blanchet, E. A. Carlen and J. A. Carrillo, Functional inequalities, thick tails and asymptotics for the critical mass Patlak-Keller-Segel model,, J. Funct. Anal., 262 (2012), 2142.  doi: 10.1016/j.jfa.2011.12.012.  Google Scholar

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[10]

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[13]

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[14]

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[15]

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[16]

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[20]

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[21]

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[22]

F. Cucker and S. Smale, Emergent behavior in flocks,, IEEE Trans. Automat. Control, 52 (2007), 852.  doi: 10.1109/TAC.2007.895842.  Google Scholar

[23]

F. Cucker and S. Smale, On the mathematics of emergence,, Jpn. J. Math., 2 (2007), 197.  doi: 10.1007/s11537-007-0647-x.  Google Scholar

[24]

F. Cucker, S. Smale and D. Zhou, Modeling language evolution,, Found. Comput. Math., 4 (2004), 315.  doi: 10.1007/s10208-003-0101-2.  Google Scholar

[25]

D. L. Donoho, Compressed sensing,, IEEE Trans. Inform. Theory, 52 (2006), 1289.  doi: 10.1109/TIT.2006.871582.  Google Scholar

[26]

B. Düring, D. Matthes and G. Toscani, Kinetic equations modelling wealth redistribution: A comparison of approaches,, Phys. Rev. E, 78 (2008).  doi: 10.1103/PhysRevE.78.056103.  Google Scholar

[27]

Y. Eldar and H. Rauhut, Average case analysis of multichannel sparse recovery using convex relaxation,, IEEE Trans. Inform. Theory, 56 (2010), 505.  doi: 10.1109/TIT.2009.2034789.  Google Scholar

[28]

M. Fornasier and H. Rauhut, Recovery algorithms for vector-valued data with joint sparsity constraints,, SIAM J. Numer. Anal., 46 (2008), 577.  doi: 10.1137/0606668909.  Google Scholar

[29]

M. Fornasier and H. Rauhut, "Handbook of Mathematical Methods in Imaging,", chapter Compressive Sensing, (2010).   Google Scholar

[30]

S.-Y. Ha, T. Ha and J.-H. Kim, Emergent behavior of a Cucker-Smale type particle model with nonlinear velocity couplings,, IEEE Trans. Automat. Control, 55 (2010), 1679.  doi: 10.1109/TAC.2010.2046113.  Google Scholar

[31]

R. Herzog, G. Stadler and G. Wachsmuth, Directional sparsity in optimal control of partial differential equations,, SIAM J. Control and Optimization, 50 (2012), 943.  doi: 10.1137/100815037.  Google Scholar

[32]

D. Horstmann, From 1970 until present: The Keller-Segel model in chemotaxis and its consequences. I,, Jahresber. Dtsch. Math.-Ver., 105 (2003), 103.   Google Scholar

[33]

D. Horstmann, From 1970 until present: The Keller-Segel model in chemotaxis and its consequences. II,, Jahresber. Dtsch. Math.-Ver., 106 (2004), 51.   Google Scholar

[34]

A. Jadbabaie, J. Lin and A. S. Morse, Correction to: "Coordination of groups of mobile autonomous agents using nearest neighbor rules,'', [IEEE Trans. Automat. Control 48 (2003), 48 (2003), 988.   Google Scholar

[35]

J. Ke, J. Minett, C.-P. Au and W.-Y. Wang, Self-organization and selection in the emergence of vocabulary,, Complexity, 7 (2002), 41.  doi: 10.1002/cplx.10030.  Google Scholar

[36]

E. F. Keller and L. A. Segel, Initiation of slime mold aggregation viewed as an instability,, J. Theor. Biol., 26 (1970), 399.  doi: 10.1016/0022-5193(70)90092-5.  Google Scholar

[37]

A. Lachapelle and M. T. Wolfram, On a mean field game approach modeling congestion and aversion in pedestrian crowds,, Trans. Res.: Part B: Methodological, 45 (2011), 1572.  doi: 10.1016/j.trb.2011.07.011.  Google Scholar

[38]

J.-M. Lasry and P.-L. Lions, Mean field games,, Jpn. J. Math. (3), 2 (2007), 229.  doi: 10.1007/s11537-007-0657-8.  Google Scholar

[39]

S. Lemercier, A. Jelic, R. Kulpa, J. Hua, J. Fehrenbach, P. Degond, C. Appert-Rolland, S. Donikian and J. Pettré, Realistic following behaviors for crowd simulation,, Computer Graphics Forum, 31 (2012), 489.  doi: 10.1111/j.1467-8659.2012.03028.x.  Google Scholar

[40]

N. Leonard and E. Fiorelli, Virtual leaders, artificial potentials and coordinated control of groups,, in, (2001), 2968.   Google Scholar

[41]

S. Mallat, "A Wavelet Tour of Signal Processing. The Sparse Way,", Third edition, (2009).   Google Scholar

[42]

M. Moussaïd, E. G. Guillot, M. Moreau, J. Fehrenbach, O. Chabiron, S. Lemercier, J. Pettré, C. Appert-Rolland, P. Degond and G. Theraulaz, Traffic instabilities in self-organized pedestrian crowds,, PLoS Computational Biology, 8 (2012).   Google Scholar

[43]

H. Niwa, Self-organizing dynamic model of fish schooling,, J. Theor. Biol., 171 (1994), 123.  doi: 10.1006/jtbi.1994.1218.  Google Scholar

[44]

J. Parrish and L. Edelstein-Keshet, Complexity, pattern, and evolutionary trade-offs in animal aggregation,, Science, 294 (1999), 99.  doi: 10.1126/science.284.5411.99.  Google Scholar

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