Temat: adaptive neuro-fuzzy controller - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Analysis of adaptive neuro-fuzzy PD controller with competitive Petri layers in speed control system for DC motor
Autorzy:: Derugo, P.
Szabat, K.
Powiązania:: https://bibliotekanauki.pl/articles/97666.pdf
Data publikacji:: 2013
Wydawca:: Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
Tematy:: adaptive neuro-fuzzy controller
Peri Layers
competitive layers
MRAS
Opis:: In the paper the issues related to the application of adaptive neuro-fuzzy controller for speed controller of an electrical motor are considered. Adaptive control structure with reference model (MRAS) is used. The standard controller is modified by the implementation of competitive Petri layers into its internal structure. The proposed modification improves the properties of the drive compared to the control structure with standard neuro-fuzzy controller. Theoretical considerations are confirmed by simulation studies experimental tests done on the laboratory stand.
Źródło:: Computer Applications in Electrical Engineering; 2013, 11; 267-280
1508-4248
Pojawia się w:: Computer Applications in Electrical Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

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

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