- Tytuł:
- Input constraints handling in an MPC/feedback linearization scheme
- Autorzy:
-
Deng, J.
Becerra, V. M.
Stobart, R. - Powiązania:
- https://bibliotekanauki.pl/articles/907653.pdf
- Data publikacji:
- 2009
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
sterowanie predykcyjne
sterowanie odwrotne
sieć neuronowa
system nieliniowy
predictive control
feedback linearization
neural network
nonlinear system
constraints - Opis:
- The combination of model predictive control based on linear models (MPC) with feedback linearization (FL) has attracted interest for a number of years, giving rise to MPC+FL control schemes. An important advantage of such schemes is that feedback linearizable plants can be controlled with a linear predictive controller with a fixed model. Handling input constraints within such schemes is difficult since simple bound contraints on the input become state dependent because of the nonlinear transformation introduced by feedback linearization. This paper introduces a technique for handling input constraints within a real timeMPC/FL scheme, where the plant model employed is a class of dynamic neural networks. The technique is based on a simple affine transformation of the feasible area. A simulated case study is presented to illustrate the use and benefits of the technique.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2009, 19, 2; 219-232
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł