Adaptive RBF network for parameter estimation and stable air-fuel ratio control

Authors:
Shiwei Wang;D. L. Yu
Affiliations:
Control Systems Research Group, School of Engineering, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK;Control Systems Research Group, School of Engineering, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK
Venue:
Neural Networks
Year:
2008

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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.