[Zurück]

C. Hametner, C. Mayr, S. Jakubek:
"Dynamic NOx emission modelling using local model networks";
International Journal of Engine Research, 15 (2014), S. 928 - 933.

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.

http://dx.doi.org/10.1177/1468087414523281