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

@article{Albi:2022,
author = {Albi, G and Herty, M and Kalise, D and Segala, C},
journal = {SIAM Journal on Control and Optimization},
title = {Moment-driven predictive control of mean-field collective dynamics},
url = {https://epubs.siam.org/doi/10.1137/21M1391559},
volume = {60},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The synthesis of control laws for interacting agent-based dynamics and their mean-field limit is studied. A linearization-based approach is used for the computation of sub-optimal feedback laws obtained from the solution of differential matrix Riccati equations. Quantification of dynamic performance of such control laws leads to theoretical estimates on suitable linearizationpoints of the nonlinear dynamics. Subsequently, the feedback laws are embedded into nonlinear model predictive control framework where the control is updated adaptively in time according to dynamic information on moments of linear mean-field dynamics. The performance and robustness ofthe proposed methodology is assessed through different numerical experiments in collective dynamics.
AU  - Albi,G
AU  - Herty,M
AU  - Kalise,D
AU  - Segala,C
PY  - 2022///
SN  - 0363-0129
TI  - Moment-driven predictive control of mean-field collective dynamics
T2  - SIAM Journal on Control and Optimization
UR  - https://epubs.siam.org/doi/10.1137/21M1391559
UR  - http://hdl.handle.net/10044/1/94612
VL  - 60
ER  -
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