Institute of Automation and Control, Graz University of Technology
The Institute of Automation and control (IRT) within the Faculty of Electrical and Information Engineering at Graz University of Technology (TU Graz) is experienced in conducting research in the field of state estimation, modeling of complex technical systems, condition monitoring, signal processing, and control development.
Modern technical systems such as modern industrial apparatus, vehicles, and autonomous mobile robots are made of a variety of individual components, whose interactions result in the required performance. Modern control and automation approaches are therefore essential. The institute contributes to improvements on the performances and development of control algorithms through engaging in scientific activities. Exemplarily, we have been involved in different research projects, from basic research to industry-related projects, working on
- parameter identification and system observation in the presence of disturbances and uncertainties,
- mathematical modeling of dynamic systems,
- motion planning and control for automated driving,
- development of model-based control strategies for complex test bed systems.
The institute is in cooperation with research centers, other institutes of the university, and companies in the automotive industry.
The IRT at TU Graz is a partner within iRel40 in order to conduct a status analysis, investigate requirements of upcoming automated driving functions, and explore possibilities for diagnosis and predictive fault detection. Moreover, it is aimed at developing selected predictive causality driven fault detection algorithms for an automated driving use case.
The IRT at TU Graz will analyze operation data, failures, and degradation of in-wheel technology. Online estimation of parameters (electronics and mechanical components) is carried out. “State of health” of electronic components, especially sensors and control electronics is focused. Our contribution is fault detection (fault isolation) and lifetime estimation of the Elaphe in-wheel motors and their control electronics.