Robust air/fuel ratio control with adaptive DRNN model and AD tuning

Authors:
Yu-Jia Zhai;Ding-Wen Yu;Hong-Yu Guo;D. L. Yu
Affiliations:
Control Systems Research Group, Liverpool John Moores University, UK;Northeast University at Qinhuangdao, China;Zhejiang Gong Shang University, China;Control Systems Research Group, Liverpool John Moores University, UK
Venue:
Engineering Applications of Artificial Intelligence
Year:
2010

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Abstract

Current production engines use look-up table and proportional and integral (PI) feedback control to regulate air/fuel ratio (AFR), which is time-consuming for calibration and is not robust to engine parameter uncertainty and time varying dynamics. This paper investigates engine modelling with the diagonal recurrent neural network (DRNN) and such a model-based predictive control for AFR. The DRNN model is made adaptive on-line to deal with engine time varying dynamics, so that the robustness in control performance is greatly enhanced. The developed strategy is evaluated on a well-known engine benchmark, a simulated mean value engine model (MVEM). The simulation results are also compared with the PI control.