Imperial College London
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Dr Dante Kalise

Faculty of Natural Sciences, Department of Mathematics

Reader in Computational Optimisation and Control
 
 
 
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Contact

 

d.kalise-balza Website CV

 
 
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Location

 

742Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Albi:2022:10.1016/j.ifacol.2022.11.036,
author = {Albi, G and Bicego, S and Kalise, D},
doi = {10.1016/j.ifacol.2022.11.036},
pages = {103--108},
publisher = {Elsevier BV},
title = {Supervised learning for kinetic consensus control},
url = {http://dx.doi.org/10.1016/j.ifacol.2022.11.036},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - In this paper, how to successfully and efficiently condition a target population of agents towards consensus is discussed. To overcome the curse of dimensionality, the mean field formulation of the consensus control problem is considered. Although such formulation is designed to be independent of the number of agents, it is feasible to solve only for moderate intrinsic dimensions of the agents space. For this reason, the solution is approached by means of a Boltzmann procedure, i.e. quasi-invariant limit of controlled binary interactions as approximation of the mean field PDE. The need for an efficient solver for the binary interaction control problem motivates the use of a supervised learning approach to encode a binary feedback map to be sampled at a very high rate. A gradient augmented feedforward neural network for the Value function of the binary control problem is considered and compared with direct approximation of the feedback law.
AU  - Albi,G
AU  - Bicego,S
AU  - Kalise,D
DO  - 10.1016/j.ifacol.2022.11.036
EP  - 108
PB  - Elsevier BV
PY  - 2022///
SN  - 2405-8963
SP  - 103
TI  - Supervised learning for kinetic consensus control
UR  - http://dx.doi.org/10.1016/j.ifacol.2022.11.036
UR  - https://www.sciencedirect.com/science/article/pii/S2405896322026647?via%3Dihub
UR  - http://hdl.handle.net/10044/1/101854
ER  -
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