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    Wyświetlanie 1-3 z 3
    Tytuł:
    Application of adaptive multivariable Generalized Predictive Control to a HVAC system in real time
    Autorzy:
    Gulan, M.
    Salaj, M.
    Rohal’-Ilkiv, B
    Powiązania:
    https://bibliotekanauki.pl/articles/229355.pdf
    Data publikacji:
    2014
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    predictive control
    multivariable systems
    constrained optimization
    HVAC system
    cascade control
    Opis:
    This paper presents the application of a Multivariable Generalized Predictive Controller (MGPC) for simultaneous temperature and humidity control in a Heating, Ventilating and Air- Conditioning (HVAC) system. The multivariable controlled process dynamics is modeled using a set of MISO models on-line identified from measured input-output process data. The controller synthesis is based on direct optimization of selected quadratic cost function with respect to amplitude and rate input constraints. Efficacy of the proposed adaptive MGPC algorithm is experimentally demonstrated on a laboratory-scale model of HVAC system. To control the airconditioning part of system the designed multivariable predictive controller is considered in a cascade dual-rate control scheme with PID auxiliary controllers.
    Źródło:
    Archives of Control Sciences; 2014, 24, 1; 67-84
    1230-2384
    Pojawia się w:
    Archives of Control Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Maximizing performance of linear model predictive control of glycemia for T1DM subjects
    Autorzy:
    Dodek, Martin
    Miklovicová, Eva
    Powiązania:
    https://bibliotekanauki.pl/articles/2134885.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    diabetes mellitus
    artificial pancreas
    glycemia control
    predictive control
    constrained optimization
    control feasibility
    Opis:
    The primary objective of this paper is the custom design of an effective, yet relatively easy-to-implement, predictive control algorithm to maintain normoglycemia in patients with type 1 diabetes. The proposed patient-tailorable empirical model featuring the separated feedback dynamics to model the effect of insulin administration and carbohydrate intake was proven to be suitable for the synthesis of a high-performance predictive control algorithm for artificial pancreas. Within the introduced linear model predictive control law, the constraints were applied to the manipulated variable in order to reflect the technical limitations of insulin pumps and the typical nonnegative nature of the insulin administration. Similarly, inequalities constraints for the controlled variable were also assumed while anticipating suppression of hypoglycemia states during the automated insulin treatment. However, the problem of control infeasibility has emerged, especially if one uses too tight constraints of the manipulated and the controlled variable concurrently. To this end, exploiting the Farkas lemma, it was possible to formulate the helper linear programming problem based on the solution of which this infeasibility could be identified and the optimality of the control could be restored by adapting the constraints. This adaptation of constraints is asymmetrical, thus one can force to fully avoid hypoglycemia at the expense of mild hyperglycemia. Finally, a series of comprehensive in-silico experiments were carried out to validate the presented control algorithm and the proposed improvements. These simulations also addressed the control robustness in terms of the intersubject variability and the meal announcements uncertainty.
    Źródło:
    Archives of Control Sciences; 2022, 32, 2; 305--333
    1230-2384
    Pojawia się w:
    Archives of Control Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Supervisory predictive control and on-line set-point optimization
    Autorzy:
    Tatjewski, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/929583.pdf
    Data publikacji:
    2010
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    sterowanie predykcyjne
    sterowanie nieliniowe
    linearyzacja
    model niepewności
    sterowność wymuszona
    optymalizacja
    predictive control
    nonlinear control
    linearisation
    model uncertainty
    constrained control
    set-point optimization
    Opis:
    The subject of this paper is to discuss selected effective known and novel structures for advanced process control and optimization. The role and techniques of model-based predictive control (MPC) in a supervisory (advanced) control layer are first shortly discussed. The emphasis is put on algorithm efficiency for nonlinear processes and on treating uncertainty in process models, with two solutions presented: the structure of nonlinear prediction and successive linearizations for nonlinear control, and a novel algorithm based on fast model selection to cope with process uncertainty. Issues of cooperation between MPC algorithms and on-line steady-state set-point optimization are next discussed, including integrated approaches. Finally, a recently developed two-purpose supervisory predictive set-point optimizer is discussed, designed to perform simultaneously two goals: economic optimization and constraints handling for the underlying unconstrained direct controllers.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2010, 20, 3; 483-495
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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