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Farjadnia, M., Alanwar, A., Niazi, M. U., Molinari, M. & Johansson, K. H. (2023). Robust Data-Driven Predictive Control of Unknown Nonlinear Systems Using Reachability Analysis. In: : . Paper presented at European Control Conference 2023, 13 - 16 June, 2023, Bucharest, Romania.
Open this publication in new window or tab >>Robust Data-Driven Predictive Control of Unknown Nonlinear Systems Using Reachability Analysis
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2023 (English)Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

This work proposes a robust data-driven predictive control approach for unknown nonlinear systems in the presence of bounded process and measurement noise. Data-driven reachable sets are employed for the controller design instead of using an explicit nonlinear system model. Although the process and measurement noise are bounded, the statistical properties of the noise are not required to be known. By using the past noisy input-output data in the learning phase, we propose a novel method to over-approximate reachable sets of an unknown nonlinear system. Then, we propose a data-driven predictive control approach to compute safe and robust control policies from noisy online data. The constraints are guaranteed in the control phase with robust safety margins through the effective use of the predicted output reachable set obtained in the learning phase. Finally, a numerical example validates the efficacy of the proposed approach and demonstrates comparable performance with a model-based predictive control approach.

Keywords
Predictive control for nonlinear systems, Robust control
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-336528 (URN)
Conference
European Control Conference 2023, 13 - 16 June, 2023, Bucharest, Romania
Projects
Cost- and Energy-Efficient Control Systems for BuildingsCLAS—Cybersäkra lärande reglersystemHiSS—Humanizing the Sustainable Smart CityMarie Skłodowska- Curie
Funder
Swedish Energy Agency, 47859-1Swedish Foundation for Strategic Research, RIT17-0046EU, Horizon Europe, 101062523EU, Horizon Europe, 830927
Note

QC 20230918

Available from: 2023-09-12 Created: 2023-09-12 Last updated: 2023-09-18Bibliographically approved
Farjadnia, M., Alanwar, A., Niazi, M. U., Molinari, M. & Johansson, K. H. (2023). Robust data-driven predictive control of unknown nonlinear systems using reachability analysis. European Journal of Control
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2023 (English)In: European Journal of Control, ISSN 09473580Article in journal (Refereed) Published
Abstract [en]

This work proposes a robust data-driven predictive control approach for unknown nonlinear systems in the presence of bounded process and measurement noise. Data-driven reachable sets are employed for the controller design instead of using an explicit nonlinear system model. Although the process and measurement noise are bounded, the statistical properties of the noise are not required to be known. By using the past noisy input-output data in the learning phase, we propose a novel method to over-approximate exact reachable sets of an unknown nonlinear system. Then, we propose a data-driven predictive control approach to compute safe and robust control policies from noisy online data. The constraints are guaranteed in the control phase with robust safety margins by effectively using the predicted output reachable set obtained in the learning phase. Finally, a numerical example validates the efficacy of the proposed approach and demonstrates comparable performance with a model-based predictive control approach.

Place, publisher, year, edition, pages
Elsevier BV, 2023
Keywords
Data-driven methods, Nonlinear systems, Predictive control, Reachability analysis, Zonotopes
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-332094 (URN)10.1016/j.ejcon.2023.100878 (DOI)2-s2.0-85165280389 (Scopus ID)
Projects
Cost-and Energy-Efficient Control Systems for BuildingsCLAS—Cybersäkra lärande reglersystemHiSS - Humanizing the Sustainable Smart City, Digital Futures, contract number VF-2020-0260European Union, Horizon Research and Innovation Programme, Marie Skłodowska-Curie grant agreement No. 101062523.
Funder
Swedish Energy Agency, 47859-1Swedish Foundation for Strategic Research, RIT17-0046
Note

QC 20231215

Available from: 2023-07-19 Created: 2023-07-19 Last updated: 2023-12-15Bibliographically approved
Fontan, A., Farjadnia, M., Llewellyn, J., Katzeff, C., Molinari, M., Cvetkovic, V. & Johansson, K. H. (2023). Social interactions for a sustainable lifestyle: The design of an experimental case study. In: : . Paper presented at IFAC World Congress 2023 - The 22nd World Congress of the International Federation of Automatic Control.
Open this publication in new window or tab >>Social interactions for a sustainable lifestyle: The design of an experimental case study
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2023 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Every day we face numerous lifestyle decisions, some dictated by habits and somemore conscious, which may or may not promote sustainable living. Aided by digital technology,sustainable behaviors can diffuse within social groups and inclusive communities. This paperoutlines a longitudinal experimental study of social influence in behavioral changes towardsustainability, in the context of smart residential homes. Participants are students residing inthe housing on campus referred to as KTH Live-In Lab, whose behaviors will be observedw.r.t. key lifestyle choices, such as food, resources, mobility, consumption, and environmentalcitizenship. The focus is on the preparatory phase of the case study and the challengesand limitations encountered during its setup. In particular, this work proposes a definitionof sustainability indicators for environmentally significant behaviors, and hypothesizes that,through digitalization of a household into a social network of interacting tenants, sustainableliving can be promoted.

Keywords
Sustainable behavior, experimental study, Live-In Lab, smart homes, cyber-physical-human systems, social networks.
National Category
Control Engineering Information Systems, Social aspects
Identifiers
urn:nbn:se:kth:diva-336667 (URN)
Conference
IFAC World Congress 2023 - The 22nd World Congress of the International Federation of Automatic Control
Projects
HiSS - Humanizing the Sustainable Smart CityCost-and Energy-Efficient Control Systems for BuildingsCLAS - Cybersäkra lärande reglersystem
Funder
Swedish Energy Agency, 47859-1Swedish Foundation for Strategic Research, RIT17-0046
Note

QC 20231123

Available from: 2023-09-15 Created: 2023-09-15 Last updated: 2023-11-23Bibliographically approved
Farjadnia, M., Fontan, A., Russo, A., Johansson, K. H. & Molinari, M. (2023). What influences occupants' behavior in residential buildings: An experimental study on window operation in the KTH Live-In Lab. In: 2023 IEEE Conference on Control Technology and Applications, CCTA 2023: . Paper presented at 2023 IEEE Conference on Control Technology and Applications, CCTA 2023, Bridgetown, Barbados, Aug 16 2023 - Aug 18 2023 (pp. 752-758).
Open this publication in new window or tab >>What influences occupants' behavior in residential buildings: An experimental study on window operation in the KTH Live-In Lab
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2023 (English)In: 2023 IEEE Conference on Control Technology and Applications, CCTA 2023, 2023, p. 752-758Conference paper, Published paper (Refereed)
Abstract [en]

 Window-opening and window-closing behaviors play an important role in indoor environmental conditions and therefore have an impact on building energy efficiency. On the other hand, the same environmental conditions drive occupants to interact with windows. Understanding this mutual relationship of interaction between occupants and the residential building is thus crucial to improve energy efficiency without disregarding occupants' comfort. This paper investigates the influence of physical environmental variables (i.e., indoor and outside climate parameters) and categorical variables (i.e., time of the day) on occupants' behavior patterns related to window operation, utilizing a multivariate logistic regression analysis. The data considered in this study are collected during winter months, when the effect on the energy consumption of the window operation is the highest, at a Swedish residential building, the KTH Live-In Lab, accommodating four occupants in separate studio apartments. Although all the occupants seem to share a sensitivity to some common factors, such as air quality and time of the day, we can also observe individual variability with respect to the most significant drivers influencing window operation behaviors. 

National Category
Control Engineering Energy Systems
Identifiers
urn:nbn:se:kth:diva-336515 (URN)10.1109/CCTA54093.2023.10253188 (DOI)
Conference
2023 IEEE Conference on Control Technology and Applications, CCTA 2023, Bridgetown, Barbados, Aug 16 2023 - Aug 18 2023
Projects
Cost-and Energy-Efficient Control Systems for BuildingsCLAS–Cybersäkra lärande reglersystemSwedish Research Council Distinguished Professor GrantHiSS - Humanizing the Sustainable Smart CityKnut and Alice Wallenberg Foundation Wallenberg Scholar Grant
Funder
Swedish Energy Agency, 47859-1Swedish Foundation for Strategic Research, RIT17-0046Swedish Research Council, 2017-0107
Note

Part of ISBN 9798350335446

QC 20230913

Available from: 2023-09-12 Created: 2023-09-12 Last updated: 2023-11-01Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0009-0002-3546-8933

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