A Neural Network Model Based MPC of Engine AFR with Single-Dimensional Optimization

Authors:
Yu-Jia Zhai;Ding-Li 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:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

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Adaptive neural network model based predictive control for air-fuel ratio of SI engines

Engineering Applications of Artificial Intelligence

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Abstract

This paper presents a model predictive control (MPC) based on a neural network (NN) model for air/fuel ration (AFR) control of automotive engines. The novelty of the paper is that the severe nonlinearity of the engine dynamics are modelled by a NN to a high precision, and adaptation of the NN model can cope with system uncertainty and time varying effects. A single dimensional optimization algorithm is used in the paper to speed up the optimization so that it can be implemented to the engine fast dynamics. Simulations on a widely used mean value engine model (MVEM) demonstrate effectiveness of the developed method.