Best Paper Award of the magazine at - Automatisier­ungstechnik

The authors Dipl.-Ing. Julius Fiedler, Dipl.-Ing. Daniel Gerbet and Prof. Klaus Röbenack from the Institute of Control Theory received the Best Paper Award of volume 72 (2024) of the journal at - Automatisierungstechnik in the category Methods for the article Data-based design of embedding observers using automatic differentiation.

The full text can be accessed at http://dx.doi.org/10.1515/auto-2024-5066

The prize is sponsored by Siemens AG.

Summary: High gain observers are frequently utilized to estimate the current internal state of nonlinear systems. The approach relies on transforming the system into the observability canonical form and occasionally embedding it into a higher dimensional space. While this can offer advantages in terms of existence conditions and convergence, the computational and implementation tasks are often daunting. In this paper, we address some of these challenges by using neural networks and automatic differentiation to approximate the necessary functions for implementing the observer. This offers a pragmatic approach to bypassing some of the problems associated with embedding observers.