Einladung zum Vortrag im Kolloquium Technische Kybernetik

Model-based hybrid estimation and diagnosis

Prof. Michael Hofbaur

Institut für Regelungs- und Automatisierungstechnik
Technische Universität Graz
Graz, Österreich

    Zeit: Dienstag · 30. 5. 2006 · 16:00 Uhr
    Ort: IST-Seminarraum 3.241 · Pfaffenwaldring 9 · Campus Stuttgart-Vaihingen

Abstract

Many modern artifacts, such as automotive systems, airplanes, mobile robotic devices and space probes, exhibit complex patterns of behavior in order to satisfy the high demand on performance, durability and autonomy. Key for the artifact's operation is a sophisticated hybrid control system that orchestrates the many components of the artifact through closed loop, system-wide interaction. A key functionality of autonomous control is to track the artifact's behavior as it dynamically moves through a possibly large set of operational and fault modes. Computational complexity of this task prevents one from considering all possible evolutions so that sub-optimal methods that focus onto a set of most likely modes are needed. Our approach combines multi-model filtering methods with advanced search and reasoning methods from the toolkit of AI and provides means for on-line system-analysis, system-decomposition and filter deduction that handles systems with a potentially large number of operational modes and failure. In my talk, I want to present our hybrid estimation and diagnosis framework in detail and demonstrate its operation within our overall model-based autonomous automation framework that also includes configuration management and hybrid control.

Biographical Information

Michael Hofbaur is associate professor for automation at the Institute of Automation and Control, Graz University of Technology, Austria. From 2000 to 2001, he was with the Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, USA. Dr. Hofbaur's research objective is to provide methods for autonomous automation of complex systems that are on the interface between control theory, computer science and artificial intelligence. The goal is to build autonomous artifacts that reason quickly, extensively and accurately about the world and react to novel or unforeseen situations. For this purpose, he conducts research in the fields of qualitative and model-based reasoning, hybrid system theory, and model-predictive control.