6 papers
Time-optimal neural feedback control of nilpotent systems as a binary classification problem
Sara Bicego, Samuel Gue, Dante Kalise +1
A computational method for the synthesis of time-optimal feedback control laws for linear nilpotent systems is proposed. The method is based on the use of the bang-bang theorem, wh…
Computation and Control of Unstable Steady States for Mean Field Multiagent Systems
Sara Bicego, Dante Kalise, Grigorios A. Pavliotis
We study interacting particle systems driven by noise, modeling phenomena such as opinion dynamics. We are interested in systems that exhibit phase transitions i.e. non-uniqueness…
Control of high-dimensional collective dynamics by deep neural feedback laws and kinetic modelling
Giacomo Albi, Sara Bicego, Dante Kalise
Modeling and control of agent-based models is twice cursed by the dimensionality of the problem, as both the number of agents and their state space dimension can be large. Even tho…
Data/moment-driven approaches for fast predictive control of collective dynamics
Giacomo Albi, Sara Bicego, Michael Herty +3
Feedback control synthesis for large-scale particle systems is reviewed in the framework of model predictive control (MPC). The high-dimensional character of collective dynamics ha…
Supervised learning for kinetic consensus control
Giacomo Albi, Sara Bicego, Dante Kalise
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 f…
Gradient-augmented Supervised Learning of Optimal Feedback Laws Using State-dependent Riccati Equations
Giacomo Albi, Sara Bicego, Dante Kalise
A supervised learning approach for the solution of large-scale nonlinear stabilization problems is presented. A stabilizing feedback law is trained from a dataset generated from St…