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    Tytuł:
    Efficient MPC algorithms with variable trajectories of parameters weighting predicted control errors
    Autorzy:
    Nebeluk, Robert
    Marusak, Piotr
    Powiązania:
    https://bibliotekanauki.pl/articles/230077.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    model predictive control
    nonlinear systems
    nonlinear models
    nonlinear control
    simulation
    optimization
    Opis:
    Model predictive control (MPC) algorithms brought increase of the control system performance in many applications thanks to relatively easily solving issues that are hard to solve without these algorithms. The paper is focused on investigating how to further improve the control system performance using a trajectory of parameters weighting predicted control errors in the performance function of the optimization problem. Different shapes of trajectories are proposed and their influence on control systems is tested. Additionally, experiments checking the influence of disturbances and of modeling uncertainty on control system performance are conducted. The case studies were done in control systems of three control plants: a linear non-minimumphase plant, a nonlinear polymerization reactor and a nonlinear thin film evaporator. Three types of MPC algorithms were used during research: linear DMC, nonlinear DMC with successive linearization (NDMC–SL), nonlinear DMC with nonlinear prediction and linearization (NDMC–NPL). Results of conducted experiments are presented in greater detail for the control system of the polymerization reactor, whereas for the other two control systems only the most interesting results are presented, for the sake of brevity. The experiments in the control system of the linear plant were done as preliminary experiments with the modified optimization problem. In the case of control system of the thin film evaporator the researched mechanisms were used in the control system of a MIMO plant showing possibilities of improving the control system performance.
    Źródło:
    Archives of Control Sciences; 2020, 30, 2; 325-363
    1230-2384
    Pojawia się w:
    Archives of Control Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A numerically efficient fuzzy MPC algorithm with fast generation of the control signal
    Autorzy:
    Marusak, Piotr M.
    Powiązania:
    https://bibliotekanauki.pl/articles/1838187.pdf
    Data publikacji:
    2021
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    model predictive control
    fuzzy system
    fuzzy control
    nonlinear control
    sterowanie predykcyjne
    system rozmyty
    sterowanie rozmyte
    sterowanie nieliniowe
    Opis:
    Model predictive control (MPC) algorithms are widely used in practical applications. They are usually formulated as optimization problems. If a model used for prediction is linear (or linearized on-line), then the optimization problem is a standard, i.e., quadratic, one. Otherwise, it is a nonlinear, in general, nonconvex optimization problem. In the latter case, numerical problems may occur during solving this problem, and the time needed to calculate control signals cannot be determined. Therefore, approaches based on linear or linearized models are preferred in practical applications. A novel, fuzzy, numerically efficient MPC algorithm is proposed in the paper. It can offer better performance than the algorithms based on linear models, and very close to that of the algorithms based on nonlinear optimization. Its main advantage is the short time needed to calculate the control value at each sampling instant compared with optimization-based numerical algorithms; it is a combination of analytical and numerical versions of MPC algorithms. The efficiency of the proposed approach is demonstrated using control systems of two nonlinear control plants: the first one is a chemical CSTR reactor with a van de Vusse reaction, and the second one is a pH reactor.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2021, 31, 1; 59-71
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Nieliniowe sterowanie predykcyjne ramion manipulatorów
    Nonlinear Predictive Control of Manipulator Arms
    Autorzy:
    Tatjewski, Piotr
    Powiązania:
    https://bibliotekanauki.pl/articles/27312429.pdf
    Data publikacji:
    2023
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    sterowanie manipulatorów
    sterowanie nieliniowe
    sterowanie predykcyjne
    opóźnienie sterowania
    szybkie próbkowanie
    manipulator control
    nonlinear control
    model predictive control
    control delay
    fast sampling
    Opis:
    Przedmiotem artykułu są algorytmy sterowania predykcyjnego (typu MPC - Model Predictive Control) ramion manipulatorów sztywnych. Zastosowano MPC z modelem w przestrzeni stanów i wykorzystano najnowszą technikę tłumienia zakłóceń i błędów modelowania, pozwalającą uniknąć dynamicznego modelowania zakłóceń lub uciekania się do dodatkowych technik ich eliminowania, takich jak SMC. Rozważane są przede wszystkim najbardziej efektywne obliczeniowo algorytmy MPC-NPL (NPL - Nonlinear Prediction and Linearization), w dwóch wersjach: z optymalizacją QP (Quadratic Programming) z ograniczeniami i z jawną optymalizacją bez ograniczeń i spełnieniem ograniczeń nierównościowych a posteriori. Dla wszystkich rozważanych algorytmów przeprowadzono kompleksową analizę symulacyjną sterowania manipulatorem z napędem bezpośrednim, przy dwóch rodzajach zakłócenia: zewnętrznym i parametrycznym. Wyniki porównano z uzyskanymi dla znanego algorytmu CTC-PID (CTC - Computer Torque Control), uzyskując lepszą jakość regulacji algorytmami MPC. Zbadano wpływ długości okresu próbkowania i obliczeniowego opóźnienia sterowania na jakość regulacji, co jest istotne dla algorytmów z szybkim próbkowaniem opartych na modelach.
    The subject of the article are predictive control algorithms (of MPC type - Model Predictive Control) for rigid manipulator arms. MPC with a state-space model and with the latest disturbance and modeling error suppression technique was applied, which avoids dynamic disturbance modeling or resorting to additional disturbance cancellation techniques, such as SMC. First of all, the most computationally efficient MPC-NPL (Nonlinear Prediction and Linearization) algorithms are considered, in two versions: the first with constrained QP (Quadratic Programming) optimization and the second with explicit (analytical) optimization without constraints and satisfying a posteriori inequality constraints. For all considered algorithms, a comprehensive simulation analysis was carried out for a direct drive manipulator, with two kinds of disturbances: external and parametric. The obtained results were compared with those for the well-known CTC-PID algorithm (CTC - Computer Torque Control), showing better control quality with MPC algorithms. In addition, the influence of the length of the sampling period and of the computational delay on control quality was investigated, which is important for model-based algorithms with fast sampling.
    Źródło:
    Pomiary Automatyka Robotyka; 2023, 27, 2; 47--58
    1427-9126
    Pojawia się w:
    Pomiary Automatyka Robotyka
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Offset-free nonlinear Model Predictive Control with state-space process models
    Autorzy:
    Tatjewski, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/229638.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    nonlinear control
    predictive control
    offset-free control
    state-space model
    state estimation
    Opis:
    Offset-free model predictive control (MPC) algorithms for nonlinear state-space process models, with modeling errors and under asymptotically constant external disturbances, is the subject of the paper. The main result of the paper is the presentation of a novel technique based on constant state disturbance prediction. It was introduced originally by the author for linear state-space models and is generalized to the nonlinear case in the paper. First the case with measured state is considered, in this case the technique allows to avoid disturbance estimation at all. For the cases with process outputs measured only and thus the necessity of state estimation, the technique allows the process state estimation only - as opposed to conventional approach of extended process-and-disturbance state estimation. This leads to simpler design with state observer/filter of lower order and, moreover, without the need of a decision of disturbance placement in the model (under certain restrictions), as in the conventional approach. A theoretical analysis of the proposed algorithm is provided, under applicability conditions which are weaker than in the conventional approach. The presented theory is illustrated by simulation results of nonlinear processes, showing competitiveness of the proposed algorithms.
    Źródło:
    Archives of Control Sciences; 2017, 27, 4; 595-615
    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 1-5 z 5

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