Fig. 1. Limits on exhaust emissions imposed by the European community in years 1970-1992. Cesare Alippi, IEEE Senior Member, Cosimo de Russis, and Vincenzo Piuri, IEEE Fellow In this paper, we consider a fuel-injection system composed of a spark ignition engine with a catalytic converter and a linear oxygen sensor on the exhaust manifold to measure the air-to-fuel ratio (AF) after the combustion process. The case study is an Alfa Romeo 1.31 engine. A Neural-Network Based Control Solution to Air-Fuel Ratio Control for Automotive Fuel-Injection Systems Fig. 2. Efficiency of a catalytic converter as a function of AF. Fig. 4. Fundamental blocks constituting the process. FEASIBLE RANGE FOR THE INPUTS AND THE OUTPUT WITH THEIR UNITS to fully excite the process dynamics), the best model, in the best scenario, is able to approximate the behavior of the process only in the interval defined by the training pairs. TABLE I PERFORMANCES OF DIFFERENT NEURAL TOPOLOGIES Fig. 5. Validation on a real data set. (a) N = 4000 r/min. (b) VN = 3000 r/min. Fig. 6. Training configuration for the neural controller. Fig. 7. Operating configuration for the neural controller. Fig. 8. Final operational scheme for the neural controller. The neural structure of the controller was validated over dif- ferent validation sets composed of real data; the two experi- ments we present refer to data measured at FIAT. The input profile for a is given in Fig. 9. The profile has been obtained by pushing and releasing the accelerator pedal: we start from a constant pressure of a 20° angle and, then, we accelerate by reaching a 47° angle, and so on. N is equal to 1000 r/min in the and for about 9% in only few points. As the control action is concerned, the improvement in performance was obtained with an action comparable with that requested by the TFC: this is a consequence of the penalty terms introduced in the training error function. To compare the control actions, we refer to the second experiment. Fig. 11 shows the evolution over time of t; as provided by the neural controller (continuous line) and the TEC (dashed line). COMPARISON IN PERFORMANCES BETWEEN THE HYBRID CONTROLLER AND THE TFC ONE Fig. 11. Control actions (¢; ) corresponding to the second experiment. TABLE III
