- Tytuł:
- Flatness-based adaptive fuzzy control of spark-ignited engines
- Autorzy:
-
Rigatos, G.G.
Siano, P. - Powiązania:
- https://bibliotekanauki.pl/articles/91727.pdf
- Data publikacji:
- 2014
- Wydawca:
- Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
- Tematy:
-
adaptive fuzzy controller
spark-ignited engines
SI engines
performance criterion
neuro-fuzzy networks
neuro-fuzzy approximator
Lyapunov stability analysis
simulation experiment - Opis:
- An adaptive fuzzy controller is designed for spark-ignited (SI) engines, under the constraint that the system’s model is unknown. The control algorithm aims at satisfying the H∞ tracking performance criterion, which means that the influence of the modeling errors and the external disturbances on the tracking error is attenuated to an arbitrary desirable level. After transforming the SI-engine model into the canonical form, the resulting control inputs are shown to contain nonlinear elements which depend on the system’s parameters. The nonlinear terms which appear in the control inputs are approximated with the use of neuro-fuzzy networks. It is shown that a suitable learning law can be defined for the aforementioned neuro-fuzzy approximators so as to preserve the closed-loop system stability. With the use of Lyapunov stability analysis it is proven that the proposed adaptive fuzzy control scheme results in H∞ tracking performance. The efficiency of the proposed adaptive fuzzy control scheme is checked through simulation experiments.
- Źródło:
-
Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 4; 231-242
2083-2567
2449-6499 - Pojawia się w:
- Journal of Artificial Intelligence and Soft Computing Research
- Dostawca treści:
- Biblioteka Nauki
Artykuł