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Data Driven Game Theoretic Control for Constrained Systems

Project description

Game theory to control complex, self-interested systems

Managing a power grid or a fleet of robots is a constant trade-off between individual freedom and safety. However, autonomous robots, intelligent machines, and humans all act in their own self-interest. The ERC-funded ARGON project aims to design a new way to coordinate these self-interested systems. It will use game theory. Using mathematics with real-time data, ARGON will ensure the entire network remains safe and efficient even when sub-systems act for themselves. The result is a scalable way to manage distributed networks that is robust enough to handle uncertainty without sacrificing fairness or performance.

Objective

The European society relies heavily on large-scale infrastructures, e.g. power and gas grids, transportation networks, with interconnected and self-interested sub-systems, e.g. networked control units, semi-autonomous robots, intelligent machines and humans. Due to high complexity, variability and uncertainty of their real-life operations, accurate dynamic, behavioral and interference models are hardly available. Nevertheless, the deployed control systems are either simplistic regulators or advanced controllers based on unreliable models. Model-based game theory and distributed optimization have attracted extraordinary research attention but in real-life practice are ineffective for control design due to the unavailability of reliable models and the limited information exchange between sub-systems. Data-driven optimal control has emerged as a practical control approach to mitigate model awareness, but only for cooperative systems. A paradigm shift is thus necessary to ensure safe, fair and efficient operation in systems with self-interested sub-systems. With this aim, ARGON will initiate the whole new area of data-driven dynamic game theory for constrained systems and in this scientific context it will pursue the twofold objective to: (i) conceive a general data-driven game-theoretic control framework; (ii) provide fast and scalable computational methods for solving data-driven decision and control problems. Towards these objectives, ARGON will develop novel methodologies based on distributionally robust operator theory and feedback control for equilibrium seeking, and integrate new ideas across monotone game theory, Lyapunov stability theory for constrained systems, data-driven control of switched systems. The expected outcomes are a mathematical theory and computational algorithms to support control synthesis in systems with self-interested, constrained sub-systems, robustly to model uncertainty, with high impact on fundamental sciences and system engineering.

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) ERC-2025-COG

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Host institution

TECHNISCHE UNIVERSITEIT DELFT
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 999 388,00
Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 1 999 388,75

Beneficiaries (1)

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