Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Pedagogiczna Biblioteka Wojewódzka im. Komisji Edukacji Narodowej w Lublinie Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaPedagogiczna Biblioteka Wojewódzka im. Komisji Edukacji Narodowej w Lublinie
Zmień bibliotekę

Wyszukujesz frazę "Model predictive control" wg kryterium: Temat

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-8 z 8
    Tytuł:
    Effectiveness of Dynamic Matrix Control algorithm with Laguerre functions
    Autorzy:
    Tatjewski, Piotr
    Tematy:
    process control
    model predictive control
    DMC algorithm
    Laguerre functions
    Pokaż więcej
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Powiązania:
    https://bibliotekanauki.pl/articles/2083461.pdf  Link otwiera się w nowym oknie
    Opis:
    The paper is concerned with the presentation and analysis of the Dynamic Matrix Control (DMC) model predictive control algorithm with the representation of the process input trajectories by parametrised sums of Laguerre functions. First the formulation of the DMCL (DMC with Laguerre functions) algorithm is presented. The algorithm differs from the standard DMC one in the formulation of the decision variables of the optimization problem - coefficients of approximations by the Laguerre functions instead of control input values are these variables. Then the DMCL algorithm is applied to two multivariable benchmark problems to investigate properties of the algorithm and to provide a concise comparison with the standard DMC one. The problems with difficult dynamics are selected, which usually leads to longer prediction and control horizons. Benefits from using Laguerre functions were shown, especially evident for smaller sampling intervals.
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A computationally efficient stable dual-mode type nonlinear predictive control algorithm
    Autorzy:
    Ławryńczuk, M.
    Tadej, W.
    Tematy:
    linearyzacja
    optymalizacja
    stabilność
    nonlinear model predictive control
    dual-mode model predictive control
    process control
    linearisation
    optimisation
    quadratic programming
    stability
    constraints
    terminal set
    Pokaż więcej
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Powiązania:
    https://bibliotekanauki.pl/articles/971003.pdf  Link otwiera się w nowym oknie
    Opis:
    This paper describes a computationally efficient (sub-optimal) nonlinear predictive control algorithm. The algorithm uses a modified dual-mode approach which guarantees closed-loop stability. In order to reduce the computational burden, instead of online nonlinear optimisation used in the classical dual-mode control scheme, a nonlinear model of the plant is linearised on-line and a quadratic programming problem is solved. Calculation of the terminal set and implementation steps of the algorithm are detailed, especially for input-output models, which are widely used in practice.
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Elman neural network for modeling and predictive control of delayed dynamic systems
    Autorzy:
    Wysocki, A.
    Ławryńczuk, M.
    Tematy:
    dynamic models
    process control
    model predictive control
    neural networks
    Elman neural network
    delayed systems
    Pokaż więcej
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Powiązania:
    https://bibliotekanauki.pl/articles/229646.pdf  Link otwiera się w nowym oknie
    Opis:
    The objective of this paper is to present a modified structure and a training algorithm of the recurrent Elman neural network which makes it possible to explicitly take into account the time-delay of the process and a Model Predictive Control (MPC) algorithm for such a network. In MPC the predicted output trajectory is repeatedly linearized on-line along the future input trajectory, which leads to a quadratic optimization problem, nonlinear optimization is not necessary. A strongly nonlinear benchmark process (a simulated neutralization reactor) is considered to show advantages of the modified Elman neural network and the discussed MPC algorithm. The modified neural model is more precise and has a lower number of parameters in comparison with the classical Elman structure. The discussed MPC algorithm with on-line linearization gives similar trajectories as MPC with nonlinear optimization repeated at each sampling instant.
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Nonlinear model predictive control for processes with complex dynamics: A parameterisation approach using Laguerre functions
    Autorzy:
    Ławryńczuk, Maciej
    Tematy:
    process control
    nonlinear model predictive control
    Laguerre functions
    linearisation
    sterowanie procesami
    model nieliniowy
    sterowanie predykcyjne
    funkcje Laguerre’a
    Pokaż więcej
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Powiązania:
    https://bibliotekanauki.pl/articles/329999.pdf  Link otwiera się w nowym oknie
    Opis:
    Classical model predictive control (MPC) algorithms need very long horizons when the controlled process has complex dynamics. In particular, the control horizon, which determines the number of decision variables optimised on-line at each sampling instant, is crucial since it significantly affects computational complexity. This work discusses a nonlinear MPC algorithm with on-line trajectory linearisation, which makes it possible to formulate a quadratic optimisation problem, as well as parameterisation using Laguerre functions, which reduces the number of decision variables. Simulation results of classical (not parameterised) MPC algorithms and some strategies with parameterisation are thoroughly compared. It is shown that for a benchmark system the MPC algorithm with on-line linearisation and parameterisation gives very good quality of control, comparable with that possible in classical MPC with long horizons and nonlinear optimisation.
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Nonlinear predictive control based on neural multi-models
    Autorzy:
    Ławryńczuk, M.
    Tatjewski, P.
    Tematy:
    sterowanie procesami
    sterowanie predykcyjne
    sieć neuronowa
    optymalizacja
    linearyzacja
    process control
    model predictive control
    neural networks
    optimisation
    linearisation
    Pokaż więcej
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Powiązania:
    https://bibliotekanauki.pl/articles/907773.pdf  Link otwiera się w nowym oknie
    Opis:
    This paper discusses neural multi-models based on Multi Layer Perceptron (MLP) networks and a computationally efficient nonlinear Model Predictive Control (MPC) algorithm which uses such models. Thanks to the nature of the model it calculates future predictions without using previous predictions. This means that, unlike the classical Nonlinear Auto Regressive with eXternal input (NARX) model, the multi-model is not used recurrently in MPC, and the prediction error is not propagated. In order to avoid nonlinear optimisation, in the discussed suboptimal MPC algorithm the neural multi-model is linearised on-line and, as a result, the future control policy is found by solving of a quadratic programming problem.
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Efficient nonlinear predictive control based on structured neural models
    Autorzy:
    Ławryńczuk, M.
    Tematy:
    sterowanie procesami
    sterowanie predykcyjne
    sieć neuronowa
    optymalizacja
    linearyzacja
    process control
    model predictive control
    neuron network
    optimisation
    linearisation
    Pokaż więcej
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Powiązania:
    https://bibliotekanauki.pl/articles/907652.pdf  Link otwiera się w nowym oknie
    Opis:
    This paper describes structured neural models and a computationally efficient (suboptimal) nonlinear Model Predictive Control (MPC) algorithm based on such models. The structured neural model has the ability to make future predictions of the process without being used recursively. Thanks to the nature of the model, the prediction error is not propagated. This is particularly important in the case of noise and underparameterisation. Structured models have much better long-range prediction accuracy than the corresponding classical Nonlinear Auto Regressive with eXternal input (NARX) models. The described suboptimal MPC algorithm needs solving on-line only a quadratic programming problem. Nevertheless, it gives closed-loop control performance similar to that obtained in fully-fledged nonlinear MPC, which hinges on online nonconvex optimisation. In order to demonstrate the advantages of structured models as well as the accuracy of the suboptimal MPC algorithm, a polymerisation reactor is studied.
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Nonlinear state-space predictive control with on-line linearisation and state estimation
    Autorzy:
    Ławryńczuk, M.
    Tematy:
    process control
    model predictive control
    nonlinear state space model
    extended Kalman filter
    online linearization
    proces sterowania
    model sterowania predykcyjnego
    model przestrzeni stanów
    rozszerzony filtr Kalmana
    Pokaż więcej
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Powiązania:
    https://bibliotekanauki.pl/articles/330330.pdf  Link otwiera się w nowym oknie
    Opis:
    This paper describes computationally efficient model predictive control (MPC) algorithms for nonlinear dynamic systems represented by discrete-time state-space models. Two approaches are detailed: in the first one the model is successively linearised on-line and used for prediction, while in the second one a linear approximation of the future process trajectory is directly found on-line. In both the cases, as a result of linearisation, the future control policy is calculated by means of quadratic optimisation. For state estimation, the extended Kalman filter is used. The discussed MPC algorithms, although disturbance state observers are not used, are able to compensate for deterministic constant-type external and internal disturbances. In order to illustrate implementation steps and compare the efficiency of the algorithms, a polymerisation reactor benchmark system is considered. In particular, the described MPC algorithms with on-line linearisation are compared with a truly nonlinear MPC approach with nonlinear optimisation repeated at each sampling instant.
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Soft computing in model-based predictive control
    Autorzy:
    Tatjewski, P.
    Ławryńczuk, M.
    Tematy:
    sterowanie procesami
    sterowanie predykcyjne
    system nieliniowy
    system rozmyty
    sieć neuronowa
    process control
    model predictive control
    nonlinear systems
    fuzzy systems
    neural networks
    Pokaż więcej
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Powiązania:
    https://bibliotekanauki.pl/articles/908473.pdf  Link otwiera się w nowym oknie
    Opis:
    The application of fuzzy reasoning techniques and neural network structures to model-based predictive control (MPC) is studied. First, basic structures of MPC algorithms are reviewed. Then, applications of fuzzy systems of the Takagi-Sugeno type in explicit and numerical nonlinear MPC algorithms are presented. Next, many techniques using neural network modeling to improve structural or computational properties of MPC algorithms are presented and discussed, from a neural network model of a process in standard MPC structures to modeling parts or entire MPC controllers with neural networks. Finally, a simulation example and conclusions are given.
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-8 z 8

      Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies