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Direct fuel injection for Sparc Ignition engines (GDI) has found its entrance in passenger car series production. These engines run lean at low loads and stoichiometric for the other operating area as displayed in figure 1 [2]. In this paper we present a neural net based adaptive injection control strategy for this type of engines. A specific kind of neural network (normalized RBF-net) is used to generate the injection commands for the injection valves. The net is used as a mapping from the driver's wish and operating condition of the engine on injection commands to also achieve a defined (fig. 1) air-fuel-ratio in transient operation. This neural network is trainable online to represent an intelligent feedforward control-structure implementing the artificial intelligence's ability of learning.

In order to reduce emissions of spare ignition engines using a three way catalyst, a stoichiometric air-fuel ratio must be guaranteed in stationary and transient operation of the engine. This aim can be reached by using a specific feed-forward structure for the control of the paths of air and fuel based on identification abilities of Artificial Intelligence. As approximators for multidimensional nonlinear static functions we will use specific Neural Networks (NN) together with sophisticated stability-proven learning structures. The acquired knowledge within the NN determines our control action mainly through using feed-forward structures. Our investigations are based on the so-called mean-value-modelling approach of SI engines; it is our aim to present this strategy.

Existing electronic combustion engine control systems only guarantee a desired air-to-fuel-ratio λ in stationary operation. In order to achieve the desired λ also in in-stationary use of the engine, it is necessary to use new-technology-based control systems. Artificial Intelligence provides methods to cope with difficulties like wide operation range, unknown nonlinearities and time delay. We will propose a strategy for control of a Spark Ignition Engine to determine the mass of air inside the combustion chambers with the highest accuracy. Since Neural Networks are universal approximators for multidimensional nonlinear static functions they can be used effectively for identification and compensation purposes of unknown nonlinearities in closed control loops.