Kullback-Leibler-Quadratic Optimal Control in a Stochastic Environment
| Autor: | Neil Cammardella, Ana Busic, Sean Meyn |
|---|---|
| Přispěvatelé: | Department of Electrical and Computer Engineering [Gainesville] (UF|ECE), University of Florida [Gainesville] (UF), Dynamics of Geometric Networks (DYOGENE), Département d'informatique - ENS Paris (DI-ENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Laboratory of Information, Network and Communication Sciences (LINCS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Mines-Télécom [Paris] (IMT)-Sorbonne Université (SU), ANR-16-CE05-0008,PARI,Approche probabiliste pour l'intégration des énergies renouvelables : le stockage virtuel en utilisant la flexibilité de la demande(2016) |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Proceedings of The 60th IEEE conference on Decision and Control CDC 2021-60th IEEE conference on Decision and Control CDC 2021-60th IEEE conference on Decision and Control, Dec 2021, Austin (online), United States |
| DOI: | 10.1109/cdc45484.2021.9682943 |
| Popis: | International audience; This paper presents advances in Kullback-Leibler-Quadratic (KLQ) optimal control: a stochastic control framework for Markovian models. The motivation is distributed control of large networked systems. The objective function is composed of a control cost in the form of Kullback-Leibler divergence plus a quadratic cost on the sequence of marginal distributions. With this choice of objective function, the optimal probability distribution of a population of agents over a finite time horizon is shown to be an exponential tilting of the nominal probability distribution. The same is true for the controlled transition matrices that induce the optimal probability distribution. However, one limitation of the previous work is that randomness can only be introduced via the control policy; all uncontrolled processes must be modeled as deterministic to render them immutable under an exponential tilting. In this work, only the controlled dynamics are subject to tilting, allowing for more general probabilistic models. Numerical experiments are conducted in the context of power networks. The distributed control techniques described in this paper can transform a large collection of flexible loads into a 'virtual battery' capable of delivering the same grid services as traditional batteries. Additionally, quality of service to the load owner is guaranteed, privacy is preserved, and computation and communication requirements are reduced, relative to alternative centralized control techniques. |
| Databáze: | OpenAIRE |
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