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

• URL: http://arxiv.org/abs/2402.15611v1
• Date: Fri, 23 Feb 2024 21:21:16 GMT
• Title: Data/moment-driven approaches for fast predictive control of collective dynamics
• Authors: Giacomo Albi, Sara Bicego, Michael Herty, Yuyang Huang, Dante Kalise, Chiara Segala
• Abstract summary: Two alternatives to model predictive control (MPC) are proposed. First, the use of supervised learning techniques for the offline approximation of optimal feedback laws is discussed. Second, a procedure based on sequential linearization of the dynamics based on macroscopic quantities of the particle ensemble is reviewed.
• Score: 1.0557437060274468
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Receding Hamiltonian-Informed Optimal Neural Control and State Estimation for Closed-Loop Dynamical Systems [4.05766189327054]
  Hamiltonian-Informed Optimal Neural (Hion) controllers are a novel class of neural network-based controllers for dynamical systems. Hion controllers estimate future states and compute optimal control inputs using Pontryagin's Principle.
  arXiv  Detail & Related papers  (2024-11-02T16:06:29Z)
• Dropout MPC: An Ensemble Neural MPC Approach for Systems with Learned Dynamics [0.0]
  We propose a novel sampling-based ensemble neural MPC algorithm that employs the Monte-Carlo dropout technique on the learned system model. The method aims in general at uncertain systems with complex dynamics, where models derived from first principles are hard to infer.
  arXiv  Detail & Related papers  (2024-06-04T17:15:25Z)
• Online Variational Sequential Monte Carlo [49.97673761305336]
  We build upon the variational sequential Monte Carlo (VSMC) method, which provides computationally efficient and accurate model parameter estimation and Bayesian latent-state inference. Online VSMC is capable of performing efficiently, entirely on-the-fly, both parameter estimation and particle proposal adaptation.
  arXiv  Detail & Related papers  (2023-12-19T21:45:38Z)
• Socio-cognitive Optimization of Time-delay Control Problems using Evolutionary Metaheuristics [89.24951036534168]
  Metaheuristics are universal optimization algorithms which should be used for solving difficult problems, unsolvable by classic approaches. In this paper we aim at constructing novel socio-cognitive metaheuristic based on castes, and apply several versions of this algorithm to optimization of time-delay system model.
  arXiv  Detail & Related papers  (2022-10-23T22:21:10Z)
• Towards Robust and Accurate Myoelectric Controller Design based on Multi-objective Optimization using Evolutionary Computation [0.22835610890984162]
  We have proposed an approach to design an energy-efficient EMG-based controller by considering a kernelized SVM classifier. In order to achieve the optimized performance of the EMG-based controller, our main strategy of classifier design is to reduce the false movements of the overall system. An elitist multi-objective evolutionary algorithm $-$ the non-dominated sorting genetic algorithm II (NSGA-II) has been used to tune the hyper parameters of SVM.
  arXiv  Detail & Related papers  (2022-04-02T06:13:01Z)
• Comparative analysis of machine learning methods for active flow control [60.53767050487434]
  Genetic Programming (GP) and Reinforcement Learning (RL) are gaining popularity in flow control. This work presents a comparative analysis of the two, bench-marking some of their most representative algorithms against global optimization techniques.
  arXiv  Detail & Related papers  (2022-02-23T18:11:19Z)
• Quadratic mutual information regularization in real-time deep CNN models [51.66271681532262]
  Regularization method motivated by the Quadratic Mutual Information is proposed. Experiments on various binary classification problems are performed, indicating the effectiveness of the proposed models.
  arXiv  Detail & Related papers  (2021-08-26T13:14:24Z)
• Policy Analysis using Synthetic Controls in Continuous-Time [101.35070661471124]
  Counterfactual estimation using synthetic controls is one of the most successful recent methodological developments in causal inference. We propose a continuous-time alternative that models the latent counterfactual path explicitly using the formalism of controlled differential equations.
  arXiv  Detail & Related papers  (2021-02-02T16:07:39Z)
• Contextual Latent-Movements Off-Policy Optimization for Robotic Manipulation Skills [41.140532647789456]
  We propose a novel view on handling the demonstrated trajectories for acquiring low-dimensional, non-linear latent dynamics. We introduce a new contextual off-policy RL algorithm, named LAtent-Movements Policy Optimization (LAMPO) LAMPO provides sample-efficient policies against common approaches in literature.
  arXiv  Detail & Related papers  (2020-10-26T17:53:30Z)
• Single-step deep reinforcement learning for open-loop control of laminar and turbulent flows [0.0]
  This research gauges the ability of deep reinforcement learning (DRL) techniques to assist the optimization and control of fluid mechanical systems. It combines a novel, "degenerate" version of the prototypical policy optimization (PPO) algorithm, that trains a neural network in optimizing the system only once per learning episode.
  arXiv  Detail & Related papers  (2020-06-04T16:11:26Z)
• Information Theoretic Model Predictive Q-Learning [64.74041985237105]
  We present a novel theoretical connection between information theoretic MPC and entropy regularized RL. We develop a Q-learning algorithm that can leverage biased models.
  arXiv  Detail & Related papers  (2019-12-31T00:29:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.