Adaptive RBF network for parameter estimation and stable air-fuel ratio control.
Neural Netw
; 21(1): 102-12, 2008 Jan.
Article
em En
| MEDLINE
| ID: mdl-18166378
ABSTRACT
In the application of variable structure control to engine air-fuel ratio, the ratio is subjected to chattering due to system uncertainty, such as unknown parameters or time varying dynamics. This paper proposes an adaptive neural network method to estimate two immeasurable physical parameters on-line and to compensate for the model uncertainty and engine time varying dynamics, so that the chattering is substantially reduced and the air-fuel ratio is regulated within the desired range of the stoichiometric value. The adaptive law of the neural network is derived using the Lyapunov method, so that the stability of the whole system and the convergence of the networks are guaranteed. Computer simulations based on a mean value engine model demonstrate the effectiveness of the technique.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Emissões de Veículos
/
Reconhecimento Automatizado de Padrão
/
Técnicas de Apoio para a Decisão
/
Redes Neurais de Computação
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Neural Netw
Ano de publicação:
2008
Tipo de documento:
Article