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pAGe 20 AIR-FUeL RATIO Engine test bench equipped with lambda sensors in the exhaust pipe. Neural networks for Optimal Airfuel Optimal airfuel ratio is a key challenge and keeps coming up as an open issue for the engine control community. Since the 1980s, the transition from carburetors to electronically controlled injection systems has been motivating researchers to concentrate on this topic. Proper control of the airfuel ratio is greatly beneficial to the performance of threeway catalysts in both steady and transient operations. This control task therefore plays a fundamental role in limiting exhaust emissions in SI, GDI and leanburn engines.
airfuel ratio control performed by MicroAutoBox Ratio Requirements For air-fuel Ratio Control Despite the considerable efforts made in limiting exhaust emissions, the increasingly stringent environmental regulations imposed throughout the world still make achieving a satisfactory airfuel ratio an ambitious goal. Furthermore, engine control system designers have to deal with the onboard diagnostics requirements, introduced in 1996 in the US and later in Europe. This represents a more critical goal in the field of automotive control, since it requires the continuous monitoring of all powertrain components to prevent critical faults in exhaust systems. Airfuel ratio (AFR) control presently relies on a mean value engine model representation. But these mean value models have some significant limitations, such as the high level of experiments needed for parameter identification and the intrinsic nonadaptive features. On the other hand, the AFR signal delay is a very critical issue to be mastered to improve the performance of closedloop control strategies. Neural networks are a promising solution for these challenges. They have high mapping capabilities and guarantee a good generalization even with a reduced set of identification data. Moreover, by implementing adaptive training procedures we can consider the influence of exogenous effects on the control performance. Developed Control Strategy The AFR control strategy is based on a recurrent neural network (RNN). The neural network is used as a controller and its output directly determines the control actions. A forward RNN model (FRNNM) of AFR dynamics was developed. This took into account the fact that the dynamic processes affecting the AFR response depend on both air and fuel dynamics. Therefore the output, pAGe 21
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