Selbstadaptierendes und lernfähiges Management für mobile Arbeitsmaschinen

Control systems in mobile machines are designed on the basis of predefined configurations to control target values according to an operator’s reference, whereas disturbance variables are considered indirectly. In this article this issue will be defined more distinctly and a notion of holistic optimization will be given. According to that, an alternative control architecture in an interdisciplinary DFG-promoted project called OCOM (Organic Computing in Off-highway Machines) will be presented. Due to this, the possibility of a basis for a self-adaptive and self-learning operating strategy (realization of a target function) in mobile machines is given, to optimize the machine holistically.