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Tytuł: Suboptimal nonlinear predictive controllers
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Abstract:
Predictive control based on linear models has become a mature technology in the last decade. Many successful real-time applications can be found in almost every sector of industry. Nonlinear predictive control can further increase the performance of this easy-to-understand control strategy. ; One of the main problems of implementing nonlinear predictive control is the computational aspect, which is of most importance in real-life applications. In this paper, suboptimal nonlinear predictive control strategies are proposed and compared. The nonlinear predictors are built based on neural identification methods or by white modelling. ; The use of diophantine equations, which is a common technique to calculate the optimal contribution of the noise model, is avoided by using a more natural method. The comparison between the control algorithms is made based on a simulated discrete multivariable nonlinear system and a continuous stirred tank reactor.
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Zielona Góra: Uniwersytet Zielonogórski
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AMCS, volume 9, number 1 (1999) ; kliknij tutaj, żeby przejść
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License CC BY 4.0:
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Biblioteka Uniwersytetu Zielonogórskiego
Kolekcje, do których przypisany jest obiekt:
- Digital Library of Zielona Góra > Repository > Faculties > Faculty of Computer, Electrical and Control Engineering
- Digital Library of Zielona Góra > Repository > Types of work > Articles
- Digital Library of Zielona Góra > Repository > Scientific journals and UZ publishing series > International Journal of Applied Mathematics and Computer Science (AMCS) > International Journal of Applied Mathematics and Computer Science (AMCS) (1999)
Data ostatniej modyfikacji:
Jul 14, 2025
Data dodania obiektu:
Jan 19, 2021
Liczba wyświetleń treści obiektu:
221
Wszystkie dostępne wersje tego obiektu:
https://zbc.uz.zgora.pl/publication/64777
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| Nazwa wydania | Data |
|---|---|
| Suboptimal nonlinear predictive controllers | Jul 14, 2025 |
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