# Data/moment-driven approaches for fast predictive control of collective dynamics

arXiv: https://arxiv.org/abs/2402.15611 · HTML: https://mlsanity.com/paper/2402.15611/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 80 references that are in the map.
- First posted on arXiv 23 Feb 2024.

## Builds on (most cited first, 21 of 21)

- [Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments](https://mlsanity.com/paper/1705.01693/) (2017)
- [A Neural Network Architecture to Learn Explicit MPC Controllers from Data](https://mlsanity.com/paper/1911.10789/) (2019)
- [Mean-Field Optimal Control](https://mlsanity.com/paper/1306.5913/) (2013)
- [Invisible control of self-organizing agents leaving unknown environments](https://mlsanity.com/paper/1504.04064/) (2015)
- [Boltzmann type control of opinion consensus through leaders](https://mlsanity.com/paper/1405.0736/) (2014)
- [Gradient-augmented Supervised Learning of Optimal Feedback Laws Using State-dependent Riccati Equations](https://mlsanity.com/paper/2103.04091/) (2021)
- [Optimal consensus control of the Cucker-Smale model](https://mlsanity.com/paper/1802.01529/) (2018)
- [Consensus Convergence with Stochastic Effects](https://mlsanity.com/paper/1508.07313/) (2015)
- [Mean-field optimal control as Gamma-limit of finite agent controls](https://mlsanity.com/paper/1803.04689/) (2018)
- [Mean field control hierarchy](https://mlsanity.com/paper/1608.01728/) (2016)
- [A collisionless singular Cucker-Smale model with decentralized formation control](https://mlsanity.com/paper/1807.05177/) (2018)
- [Linear tracking MPC for nonlinear systems Part I: The model-based case](https://mlsanity.com/paper/2105.08560/) (2021)
- [State-dependent Riccati equation feedback stabilization for nonlinear PDEs](https://mlsanity.com/paper/2106.07163/) (2021)
- [Data-driven Tensor Train Gradient Cross Approximation for Hamilton-Jacobi-Bellman Equations](https://mlsanity.com/paper/2205.05109/) (2022)
- [Interacting particles with L'evy strategies: limits of transport equations for swarm robotic systems](https://mlsanity.com/paper/1807.10124/) (2018)
- [Kinetic-controlled hydrodynamics for traffic models with driver-assist vehicles](https://mlsanity.com/paper/1807.11476/) (2018)
- [Instantaneous control of interacting particle systems in the mean-field limit](https://mlsanity.com/paper/1903.12407/) (2019)
- [PDE-based multi-agent formation control using flatness and backstepping: analysis, design and robot experiments](https://mlsanity.com/paper/1912.10539/) (2019)
- [Moment-Driven Predictive Control of Mean-Field Collective Dynamics](https://mlsanity.com/paper/2101.01970/) (2021)
- [Optimizing semilinear representations for State-dependent Riccati Equation-based feedback control](https://mlsanity.com/paper/2202.11801/) (2022)
- [Supervised learning for kinetic consensus control](https://mlsanity.com/paper/2207.09171/) (2022)

## Abstract (arXiv)

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 hampers the performance of traditional MPC algorithms based on fast online dynamic optimization at every time step. Two alternatives to MPC are proposed. First, the use of supervised learning techniques for the offline approximation of optimal feedback laws is discussed. Then, a procedure based on sequential linearization of the dynamics based on macroscopic quantities of the particle ensemble is reviewed. Both approaches circumvent the online solution of optimal control problems enabling fast, real-time, feedback synthesis for large-scale particle systems. Numerical experiments assess the performance of the proposed algorithms.