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of 202
pro vyhledávání: '"Keviczky, T."'
In this paper we propose a subgradient method for solving coupled optimization problems in a distributed way given restrictions on the communication topology. The iterative procedure maintains local variables at each node and relies on local subgradi
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-80707
We propose a primal-dual interior-point (PDIP) method for solving quadratic programming problems with linear inequality constraints that typically arise form MPC applications. We show that the solver converges (locally) quadratically to a suboptimal
Externí odkaz:
http://arxiv.org/abs/1709.06362
Autor:
Rostampour, V., Keviczky, T.
This paper presents a distributed stochastic model predictive control (SMPC) approach for large-scale linear systems with private and common uncertainties in a plug-and-play framework. Using the so-called scenario approach, the centralized SMPC invol
Externí odkaz:
http://arxiv.org/abs/1703.06273
Autor:
Rostampour, V., Keviczky, T.
This paper presents an energy management framework for building climate comfort (BCC) systems interconnected in a grid via aquifer thermal energy storage (ATES) systems in the presence of two types of uncertainty (private and common). ATES can be use
Externí odkaz:
http://arxiv.org/abs/1611.03206
This paper presents an algorithm to solve the infinite horizon constrained linear quadratic regulator (CLQR) problem using operator splitting methods. First, the CLQR problem is reformulated as a (finite-time) model predictive control (MPC) problem w
Externí odkaz:
http://arxiv.org/abs/1609.05799
Autor:
Kerkhof, L., Keviczky, T.
Publikováno v:
Proceedings of the European Control Conference (ECC 2021)
In the past, many greenhouse control algorithms have been developed. However, the majority of these algorithms rely on an explicit parametric model description of the greenhouse. These models are often based on physical laws such as conservation of m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c572dec899d2b6074f5d3b8a9ed112c1
https://doi.org/10.23919/ecc54610.2021.9655228
https://doi.org/10.23919/ecc54610.2021.9655228
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