Control to flocking of the kinetic Cucker-Smale model
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Abstract (arXiv)
The well-known Cucker-Smale model is a macroscopic system reflecting flocking, i.e. the alignment of velocities in a group of autonomous agents having mutual interactions. In the present paper, we consider the mean-field limit of that model, called the kinetic Cucker-Smale model, which is a transport partial differential equation involving nonlocal terms. It is known that flocking is reached asymptotically whenever the initial conditions of the group of agents are in a favorable configuration. For other initial configurations, it is natural to investigate whether flocking can be enforced by means of an appropriate external force, applied to an adequate time-varying subdomain. In this paper we prove that we can drive to flocking any group of agents governed by the kinetic Cucker-Smale model, by means of a sparse centralized control strategy, and this, for any initial configuration of the crowd. Here, "sparse control" means that the action at each time is limited over an arbitrary proportion of the crowd, or, as a variant, of the space of configurations; "centralized" means that the strategy is computed by an external agent knowing the configuration of all agents. We stress that we do not only design a control function (in a sampled feedback form), but also a time-varying control domain on which the action is applied. The sparsity constraint reflects the fact that one cannot act on the whole crowd at every instant of time. Our approach is based on geometric considerations on the velocity field of the kinetic Cucker-Smale PDE, and in particular on the analysis of the particle flow generated by this vector field. The control domain and the control functions are designed to satisfy appropriate constraints, and such that, for any initial configuration, the velocity part of the support of the measure solution asymptotically shrinks to a singleton, which means flocking.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Nov 2014.
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Citing papers per year: 2023: 2; 2024: 1; 2025: 1; 2026: 2.
6 papers.
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- 2026Training Infinitely Deep and Wide Transformers17 May 2026
- 2025Transformers through the lens of support-preserving maps between measures30 Sep 2025
- 2024Convex Regularization and Convergence of Policy Gradient Flows under Safety Constraints28 Nov 2024
- 2023Law of Large Numbers for Bayesian two-layer Neural Network trained with Variational Inference10 Jul 2023
- 2023The emergence of clusters in self-attention dynamics9 May 2023