Robust air/fuel ratio control with adaptive DRNN model and AD tuning
Engineering Applications of Artificial Intelligence（2010）
摘要
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.
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关键词
ad tuning,feedback control,recurrent neural networks,well-known engine benchmark,drnn model,engine time,si engines,adaptive drnn model,control performance,robust air,current production engine,model predictive control,pi control,air/fuel ratio control,fuel ratio control,adaptive neural networks,engine parameter uncertainty,model-based predictive control,simulated mean value engine,look up table,recurrent neural network,value engineering
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