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In this work, a model predictive functional control approach for automotive three-way catalyst oxygen storage state control is demonstrated on a Ford 2.0 liter I4 Duratec SI engine. The control system uses a UEGO sensor for the pre-catalyst air fuel ratio (AFR) measurement and a switching-type HEGO sensor for the post-catalyst measurement. The model predictive controller is the primary control loop within a multi-rate cascade control configuration that adapts the parameters of a post-catalyst HEGO relay controller in an optimal manner using a predictive functional control approach. This relay controller adjusts the target of a delay-compensated feedback controller for the pre-catalyst AFR in order to maintain the post-catalyst HEGO sensor signal within a specified range of the desired target voltage.

In this paper a new knock control algorithm is developed based on a stochastic interpretation of the knock signal and on a control objective specified as a certain percentage of knocking cycles. Unlike previous ‘stochastic’ knock controllers, the new algorithm does not average or low pass filter the knock intensity signal and the transient response of the controller is consequently much faster. The performance of the new controller is compared in detail with the response of a traditional deterministic controller using a simple but effective knock simulation tool. The results show that the new controller is able to operate at a more advanced mean spark angle and that there is much less cyclic variance about this mean. The transient response to excess knocking events is as fast, or faster, than the conventional controller, though the rate of recovery from overly retarded conditions is slower.