Self-tuning optimization of spark ignition automotive engines

The use of self-tuning control concepts applied to the adaptive optimization of spark ignition angles of an automotive engine is described. In particular, it is shown how the concepts of self-adaptive control theory can be modified to allow their use in a continuous updating of the spark-angle map as a function of load and speed. Used in this manner, the self-tuner can account for in-service changes in engine characteristics, changes due to variations of ambient and operational conditions. In addition, self-adaptation allows the spark-controller algorithm to tune itself, so that the factory-generated spark map is precisely matched to the exact nature of each individual engine. The basic theory of self-tuning optimizers (also known as extremum controllers) is outlined, and their performance features are discussed with particular reference to their implementation in adaptive spark-timing systems. It is shown that the self-tuning extremum controller can be made robust even when the signal-to-noise ratio is less than one.<>