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A novel control paradigm for large scale hybrid networks

I will develop efficient on-line control methods for large-scale Networks with Hybrid Dynamics (NHDs) in the presence of uncertainties, where hybrid dynamics refers to a combination of continuous dynamics, mode switches, and/or to... ver más

30/09/2026

TU Delft

3M€

Presupuesto del proyecto: 3M€

Líder del proyecto

TECHNISCHE UNIVERSITEIT DELFT No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

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Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2021-05-05

ERC-2020-ADG: ERC ADVANCED GRANT

I+D

Cerrada hace 4 años

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No hay información privada compartida para este proyecto. Habla con el coordinador.

2 Participantes

TU Delft

2.50M€ | Lider

AMAYA SPORT

267.16K€ | Participante

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Duración del proyecto: 64 meses Fecha Inicio: 2021-05-05
Fecha Fin: 2026-09-30

Líder del proyecto

TECHNISCHE UNIVERSITEIT DELFT No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 3M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto I will develop efficient on-line control methods for large-scale Networks with Hybrid Dynamics (NHDs) in the presence of uncertainties, where hybrid dynamics refers to a combination of continuous dynamics, mode switches, and/or topology changes. This topic is one of the core fundamental open problems in the field of systems and control. It is also important from a societal point of view as today’s society depends heavily on the reliable and efficient operation of road, railway, electricity, gas, and water networks, all of which are examples of large-scale NHDs. Control of large-scale NHDs is a very complex problem due to the large size of the networks, the presence of disturbances, and the hybrid dynamics, while a limited computation time is available. State-of-the-art control methods are not suited for large-scale NHDs as they either suffer from computational tractability issues or impose additional restrictions, resulting in a significantly reduced performance. To address this problem, I will create a new on-line control paradigm for large-scale NHDs based on an innovative integration of multi-agent optimization-based and learning-based control, allowing to unite the optimality of optimization-based control with the on-line tractability of learning-based control. I will bridge the gap between optimization-based and learning-based control for NHDs through the use of multi-scale multi-resolution piecewise affine models, explicit consideration of the graph structure of the network, the unique knowledge and experience I have in both optimization-based control and learning-based decision making, and an interdisciplinary integration of approaches from systems and control, computer science, and optimization. This will result in systematic, very reliable, highly scalable, high-performance on-line control methods for large-scale NHDs. I will demonstrate their feasibility, benefits, and impact for green multi-modal transportation networks and smart multi-energy networks.

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