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.
목차
Abstract I. Introduction II. Engine Dynamics III. Adaptive neural network model A. Data Collection B. Engine Modelling IV. mpc of air fuel ratio A. Control System Structure B. Single-Dimensional Optimization Approach V. conclusions 참고문헌
키워드
Air-fuel ratio controlSI engineAdaptive neural networksNonlinear programmingModel predictive control
저자
Qichen Gu [ Xi'an Jiaotong Liverpool University ]
교신저자
Yujia Zhai [ Xi'an Jiaotong Liverpool University ]