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  • 1.
    Ahmadi, Seyed Alireza
    et al.
    KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.
    Shames, Iman
    KTH, Skolan för elektro- och systemteknik (EES), Reglerteknik. KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.
    Scotton, Francesco
    KTH, Skolan för elektro- och systemteknik (EES), Reglerteknik. KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.
    Huang, Lirong
    KTH, Skolan för elektro- och systemteknik (EES), Reglerteknik. KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.
    Sandberg, Henrik
    KTH, Skolan för elektro- och systemteknik (EES), Reglerteknik. KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.
    Johansson, Karl Henrik
    KTH, Skolan för elektro- och systemteknik (EES), Reglerteknik. KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.
    Wahlberg, Bo
    KTH, Skolan för elektro- och systemteknik (EES), Reglerteknik. KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.
    Towards more efficient building energy management systems2012Inngår i: Proceedings - 2012 7th International Conference on Knowledge, Information and Creativity Support Systems, KICSS 2012, IEEE , 2012, s. 118-125Konferansepaper (Fagfellevurdert)
    Abstract [en]

    As a first step towards developing efficient building energy management techniques, in this paper, we first study the energy consumption patterns of heating, ventilation and cooling (HVAC) systems across the KTH Royal Institute of Technology campus and we identify some possible areas where energy consumption can be made less wasteful. Later, we describe a test-bed where wireless sensor networks are used to collect data and eventually control the HVAC system in a distributed way. We present some of the data, temperature, humidity, and CO2 measurements, that are collected by the aforementioned network and compare them with the measurements collected by the legacy sensors already in place. In the end we present a preliminary result on modelling the dynamics of the temperature, humidity, and CO2 using the data gather by the sensor network. We check the validity of the model via comparing the out put of the system with measured data. As a future work we identify the possibility of using the models obtained here for model based control, and fault detection and isolation techniques.

  • 2. Gerencser, L.
    et al.
    Hjalmarsson, Håkan
    KTH, Skolan för elektro- och systemteknik (EES), Reglerteknik. KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.
    Huang, Lirong
    KTH, Skolan för elektro- och systemteknik (EES), Reglerteknik. KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre. Institute of Molecular Systems Biology, Switzerland.
    Adaptive Input Design for LTI Systems2017Inngår i: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 62, nr 5, s. 2390-2405, artikkel-id 7574358Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Optimal input design for parameter estimation has obtained extensive coverage in the past. A key problem here is that the optimal input depends on some unknown system parameters that are to be identified. Adaptive design is one of the fundamental routes to handle this problem. Although there exist a rich collection of results on this problem, there are few results that address dynamical systems. This paper presents sufficient conditions for convergence/consistency and asymptotic optimality for a class of adaptive systems consisting of a recursive prediction error estimator and an input generator depending on the time-varying parameter estimates. The results apply to a general family of single input single output linear time-invariant systems. An important application is adaptive input design for which the results imply that, asymptotically in the sample size, the adaptive scheme recovers the same accuracy as the off-line prediction error method that uses data from an experiment where perfect knowledge of the system has been used to design an optimal input spectrum.

  • 3.
    Huang, Lirong
    et al.
    KTH, Skolan för elektro- och systemteknik (EES), Reglerteknik. KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.
    Hjalmarsson, Håkan
    KTH, Skolan för elektro- och systemteknik (EES), Reglerteknik. KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.
    Recursive estimators with Markovian jumps2012Inngår i: Systems & control letters (Print), ISSN 0167-6911, E-ISSN 1872-7956, Vol. 61, nr 10, s. 1009-1016Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Recursive stochastic algorithms have various applications. In the literature, it is assumed that the true value lies in a connected domain. But, in many cases, it is known that the true value is contained in the union of a finite number of pairwise disjoint sets instead of a connected domain. In these situations, the existing algorithms may be not applicable. To cope with this problem, this paper proposes recursive stochastic algorithms with (event-triggered) Markovian jumps and presents sufficient conditions for almost sure convergence of the proposed algorithms. As an example of applications, this paper significantly improves an existing adaptive algorithm with the proposed method for consistent estimation of non-minimum phase zeros.

  • 4.
    Huang, Lirong
    et al.
    KTH, Skolan för elektro- och systemteknik (EES), Reglerteknik. KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.
    Hjalmarsson, Håkan
    KTH, Skolan för elektro- och systemteknik (EES), Reglerteknik. KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.
    Gerencser, L.
    Adaptive experiment design for ARMAX systems?2012Inngår i: 2012 IEEE 51st Annual Conference on Decision and Control (CDC), IEEE , 2012, s. 907-912Konferansepaper (Fagfellevurdert)
    Abstract [en]

    A key problem in optimal input design is that the optimal input depends on some unknown system parameters that are to be identified. Adaptive design is one of the fundamental routes to handle this problem. This paper proposes an adaptive input design method for ARMAX systems based on the general stochastic framework outlined in the reference [10].

  • 5.
    Scotton, Francesco
    et al.
    KTH, Skolan för elektro- och systemteknik (EES), Reglerteknik. KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.
    Huang, Lirong
    KTH, Skolan för elektro- och systemteknik (EES), Reglerteknik. KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.
    Ahmadi, Seyed Alireza
    KTH, Skolan för elektro- och systemteknik (EES), Reglerteknik. KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.
    Wahlberg, Bo
    KTH, Skolan för elektro- och systemteknik (EES), Reglerteknik. KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.
    Physics-based modeling and identification for HVAC systems?2013Inngår i: 2013 European Control Conference, ECC 2013, IEEE , 2013, s. 1404-1409Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Heating, ventilation and air conditioning (HVAC) systems are among the largest energy consumers in many buildings. As is known, modeling and identification play important roles in the study of HVAC systems. A good model is very helpful for improving efficiency of the HVAC system. Very recently, a physics-based model of room temperature was proposed. Motivated by this inspiring work, this paper, based on the physical dynamical systems, proposes, identifies and validates three models for CO2 concentration, temperature and humidity of a test-bed room, respectively. Particularly, our models take into account the effect of occupiers, since the indoor air quality (IAQ) is evidently affected by the number of occupiers. A test-bed has been set up for experiments in a laboratory room on KTH campus. Experimental results verify that our proposed method improves the performance of the physics-based linear parametric models.

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