Dynamic NOx emission modelling using local model networks

Abstract

More and more stringent emission regulations and the desire to reduce fuel consumption lead to an increasing demand for efficient and reliable modelling tools in the automotive industry. When conventional physical modelling is not possible due to the lack of precise, formal knowledge about the system, black-box- and grey-box-oriented nonlinear system identification procedures are a widely used concept to create models based on measured input and output data of the process. In this context, local model networks are an established approach for nonlinear dynamic system identification as they provide not only accurate but also interpretable models and therefore allow a better understanding of the true system than pure black-box models. As a consequence, local model networks provide a basis for the development of systematic approaches to stability analysis and nonlinear controller design. In this article, local model network–based dynamic NOx emission modelling is presented. A robust and efficient local model network training algorithm is described, and the proposed concepts are validated using real measurement data. An important advantage of the architecture of local model networks is their good interpretability which is an important advantage for the design of controllers or observers. Additionally, stability analysis of both the nonlinear open- and closed-loop system is possible based on Lyapunov stability theory.