Objective
"Despite wide ranging progress on both the theory and applications of optimal control for more than half a century, considerable challenges remain when it comes to applying the resulting methods to large scale systems. The difficulties become even greater when one moves outside the classical realm of model based optimal control to address problems where models are replaced by data, or macroscopic behaviours emerge out of microscopic interactions of large populations of agents. To address these challenges, we propose here to develop a framework for approximating optimal control problems using randomised optimisation. The starting point will be formulations of optimal control problems as infinite dimensional linear programs. Our recent work suggests that randomised methods on the one hand can serve as a basis for algorithms to approximate such infinite programs and on the other enjoy close connections to statistical learning theory, providing a direct link to data driven approaches. Turning these intuitions into an approximation framework for optimal control that rests on solid theoretical foundations and provides explicit accuracy guarantees will be the methodological contribution of the proposed research. The resulting methods can find a range of applications in engineering and beyond; here we will investigate two such applications. One is motivated by our work on energy management in buildings and districts. The challenge here is the dimensionality of the system, especially if one would like to include weather and other forecast information and the corresponding uncertainty. The second application will be to so-called population systems, that involve the interaction of many agents with local decision-making capabilities coupled through the use of common resources. Here the main challenges are defining suitable ""features"" to abstract the individual states and the integration of uncertainty due to the presence of non-participating agents.
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
ERC-ADG - Advanced Grant
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2017-ADG
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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.
8092 Zuerich
Switzerland
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