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Wyszukujesz frazę "nonlinear system control" wg kryterium: Temat


Wyświetlanie 1-3 z 3
Tytuł:
Effective dual-mode fuzzy DMC algorithms with on-line quadratic optimization and guaranteed stability
Autorzy:
Marusak, P. M.
Tatjewski, P.
Powiązania:
https://bibliotekanauki.pl/articles/907866.pdf
Data publikacji:
2009
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
system nieliniowy
system rozmyty
sterowanie predykcyjne
stabilność
sterowność wymuszona
nonlinear system
fuzzy system
model predictive control
stability
constrained control
dual-mode control
Opis:
Dual-mode fuzzy dynamic matrix control (fuzzy DMC-FDMC) algorithms with guaranteed nominal stability for constrained nonlinear plants are presented. The algorithms join the advantages of fuzzy Takagi-Sugeno modeling and the predictive dual-mode approach in a computationally efficient version. Thus, they can bring an improvement in control quality compared with predictive controllers based on linear models and, at the same time, control performance similar to that obtained using more demanding algorithms with nonlinear optimization. Numerical effectiveness is obtained by using a successive linearization approach resulting in a quadratic programming problem solved on-line at each sampling instant. It is a computationally robust and fast optimization problem, which is important for on-line applications. Stability is achieved by appropriate introduction of dual-mode type stabilization mechanisms, which are simple and easy to implement. The effectiveness of the proposed approach is tested on a control system of a nonlinear plant-a distillation column with basic feedback controllers.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2009, 19, 1; 127-141
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Actuator fault tolerance in control systems with predictive constrained set-point optimizers
Autorzy:
Marusak, P. M.
Tatjewski, P.
Powiązania:
https://bibliotekanauki.pl/articles/929879.pdf
Data publikacji:
2008
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
sterowanie odporne na błędy
sterowanie predykcyjne
optymalizacja
system nieliniowy
fault tolerant control
model predictive control
set-point optimization
nonlinear system
Opis:
Mechanisms of fault tolerance to actuator faults in a control structure with a predictive constrained set-point optimizer are proposed. The structure considered consists of a basic feedback control layer and a local supervisory set-point optimizer which executes as frequently as the feedback controllers do with the aim to recalculate the set-points both for constraint feasibility and economic performance. The main goal of the presented reconfiguration mechanisms activated in response to an actuator blockade is to continue the operation of the control system with the fault, until it is fixed. This may be even long-term, if additional manipulated variables are available. The mechanisms are relatively simple and consist in the reconfiguration of the model structure and the introduction of appropriate constraints into the optimization problem of the optimizer, thus not affecting the numerical effectiveness. Simulation results of the presented control system for a multivariable plant are provided, illustrating the efficiency of the proposed approach.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2008, 18, 4; 539-551
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Soft computing in model-based predictive control
Autorzy:
Tatjewski, P.
Ławryńczuk, M.
Powiązania:
https://bibliotekanauki.pl/articles/908473.pdf
Data publikacji:
2006
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
sterowanie procesami
sterowanie predykcyjne
system nieliniowy
system rozmyty
sieć neuronowa
process control
model predictive control
nonlinear systems
fuzzy systems
neural networks
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.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2006, 16, 1; 7-26
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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