Evolving model architecture for custom output range exploration

In this paper, a methodology for combined online design of experiments and system identification is presented. More specifically, the paper addresses the problem of creating a model automatically that describes an unknown process accurately in a predefined range of its output. Such a model is typically needed for the calibration of combustion engines where only a relatively small emission range is of interest. The presented solution approach consists of two interacting components: first, an evolving local model network is used for creating, refining and extending a data-driven model, based on the incoming measurements; second, model-based approaches are proposed for designing new experiments so that the data-driven model has a high degree of accuracy in a predefined range of its output. The method uses, besides the models, a space-filling to explore untrained areas. The proposed concepts are illustrated and discussed by means of an academic and two real-world examples.

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