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  • 1.
    Farjadnia, Mahsa
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Applied Thermodynamics and Refrigeration.
    Alanwar, Amr
    Jacobs University Bremen, Bremen, Germany.
    Niazi, Muhammad Umar B.
    Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, USA.
    Molinari, Marco
    KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Applied Thermodynamics and Refrigeration.
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Robust Data-Driven Predictive Control of Unknown Nonlinear Systems Using Reachability Analysis2023Conference paper (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.

  • 2.
    Farjadnia, Mahsa
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Applied Thermodynamics and Refrigeration.
    Alanwar, Amr
    Niazi, Muhammad Umar B.
    Molinari, Marco
    KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Applied Thermodynamics and Refrigeration.
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Robust data-driven predictive control of unknown nonlinear systems using reachability analysis2023In: European Journal of Control, ISSN 09473580Article in journal (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 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.

  • 3.
    Farjadnia, Mahsa
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Applied Thermodynamics and Refrigeration.
    Fontan, Angela
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Russo, Alessio
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Molinari, Marco
    KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Applied Thermodynamics and Refrigeration.
    What influences occupants' behavior in residential buildings: An experimental study on window operation in the KTH Live-In Lab2023In: 2023 IEEE Conference on Control Technology and Applications, CCTA 2023, 2023, p. 752-758Conference 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. 

  • 4.
    Fontan, Angela
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Farjadnia, Mahsa
    KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Applied Thermodynamics and Refrigeration. KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Llewellyn, Joseph
    KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering, Strategic Sustainability Studies.
    Katzeff, Cecilia
    KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering, Strategic Sustainability Studies.
    Molinari, Marco
    KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Applied Thermodynamics and Refrigeration.
    Cvetkovic, Vladimir
    KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering, Resources, Energy and Infrastructure.
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Social interactions for a sustainable lifestyle: The design of an experimental case study2023Conference 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.

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