Back
In many control applications it is the goal to operate a dynamical system in an optimal way with respect to a certain performance criterion. In a combustion engine, for example, the goal could be to control the engine such that the emissions are minimized. Due to the complexity of an engine, the desired operating point is unknown or may even change over time so that it cannot be determined a priori. Extremum seeking control is a learning-control methodology to solve such kind of control problems. It is a model-free method that optimizes the steady-state behavior of a dynamical system. Since it can be implemented with very limited resources, it has found several applications in industry. In this talk we give an introduction to extremum seeking theory based on a recently developed framework which relies on tools from geometric control. Furthermore, we discuss how this framework can be utilized to solve distributed optimization and coordination problems in multi-agent systems.
Christian Ebenbauer (Institute for Systems Theory and Automatic Control, University of Stuttgart)
Professor
Christian Ebenbauer received his MS (Dipl.-Ing.) in Telematics (Electrical Engineering and Computer Science) from Graz University of Technology, Austria, in 2000 and his PhD (Dr.-Ing.) in Mechanical Engineering from the University of Stuttgart, Germany, in 2005. After having completed his PhD, he was a Postdoctoral Associate and an Erwin Schrödinger Fellow at the Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, USA. Since April 2009, he is a professor at the Institute for Systems Theory and Automatic Control, University of Stuttgart, Germany. His research interests lie in the areas dynamical systems, control theory, optimization and computation.