- Tytuł:
- A family of model predictive control algorithms with artificial neural networks
- Autorzy:
- Ławryńczuk, M.
- Powiązania:
- https://bibliotekanauki.pl/articles/929631.pdf
- Data publikacji:
- 2007
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
sterowanie predykcyjne
sieć neuronowa
optymalizacja
linearyzacja
programowanie kwadratowe
predictive control
neural networks
optimisation
linearisation
quadratic programming - Opis:
- This paper details nonlinear Model-based Predictive Control (MPC) algorithms for MIMO processes modelled by means of neural networks of a feedforward structure. Two general MPC techniques are considered: the one with Nonlinear Optimisation (MPC-NO) and the one with Nonlinear Prediction and Linearisation (MPC-NPL). In the first case a nonlinear optimisation problem is solved in real time on-line. In order to reduce the computational burden, in the second case a neural model of the process is used on-line to determine local linearisation and a nonlinear free trajectory. Single-point and multi-point linearisation methods are discussed. The MPC-NPL structure is far more reliable and less computationally demanding in comparison with the MPC-NO one because it solves a quadratic programming problem, which can be done efficiently within a foreseeable time frame. At the same time, closed-loop performance of both algorithm classes is similar. Finally, a hybrid MPC algorithm with Nonlinear Prediction, Linearisation and Nonlinear optimisation (MPC-NPL-NO) is discussed.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2007, 17, 2; 217-232
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł