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Controlling optimum combustion requires the prediction of cylinder air mass (mair non measurable variable), and the calculation of the injected fuel mass. The elaboration of an accurate predictor requires the determination of an exact and robust « intake manifold » model. From a behavioural model tending toward the real « intake manifold » behaviour, this study describes the determination of two exact and robust stochastic state models, one using 1 sensor (intake manifold pressure), the other 2 sensors (intake manifold pressure and air flow indicator). The non linearity of the process is treated, and all the internal combustion engine map coefficients η(Ne,X) are defined as a single constant scalar.

One way of improving electronic engine control is to get an insight into the combustion process, using a direct measurement method: this means the sensor must be put straight into the combustion chamber. The reference for analyzing combustion development is the cylinder pressure sensor. Due to the price of this sensor and the added complexity for cylinder head design and manufacturing, cylinder pressure sensors are not conceivable today for mass production. An alternative to the cylinder pressure sensor is the ionization sensor. It seems to be very promising for electronic engine control. Several publications have already demonstrated the benefits of ionization currents sensing for misfire detection, knock detection, closed loop ignition control, air-fuel ratio estimation. On the contrary, other publications have shown severe limitations of the ionization sensor. For example, fuel composition or additives can influence the ionization current.

The control of an optimal combustion requires an estimation of cylinder air mass (mair), and the calculation of the injected fuel mass. However, the low bandwidth of the engine process and the fuel injector impose a 2 Top Dead Center (TDC) predictive computation of mair. The elaboration of an accurate predictor requires the determination of an exact and robust « intake manifold » model. Thus, the model has to be validated and qualified experimentally, even though no mair sensor and no physical model currently exist. From a behavioural model tending toward the real « intake manifold » behaviour, this study describes the determination of a stochastic state model. The non linearity of the process is treated, as well as the « no computation » characteristic of the internal combustion engine map. The numeralization method of the state system respects the real time constraint of the engine (engine speed interval [Niddle; 7000rpm]).

The optimization of fuel efficiency and the minimization of the residual gas fraction require individual cylinder control of the amounts of inducted air mass and injected fuel mass. Determination of an individual cylinder air/fuel ratio (AFR) regulator is based on the measured AFR for each cylinder, using 4 proportional UEGO sensors. The innovative character of this study describes a unified and robust individual cylinder AFR estimator, using a single measuring point: a proportional oxygen sensor located in the exhaust manifold. The model used for the estimator is a state model such that the dimension of the state and measurement matrices are unique, whatever the manifold configuration and the sensor position (confluence point or exhaust manifold: unified model), the engine speed (robust model).