Intelligent and Nonlinear Control

Many real-world systems exhibit unneglectable nonlinearities in their dynamics that should be considered in the design process of the controller.

Methods from the field of computational and artificial intelligence as well as nonlinear control allow a very general approach to system identification and controller design, using either analytical models of the system dynamics, data from the system, or expert knowledge. Other, alternative methods try to iteratively „learn“ a controller by interacting with the system.

The fields of research at the chair of automatic control are fuzzy modelling and controller design, system-identification and machine learning techniques.