6 papers
Robust and structure exploiting optimization algorithms: An integral quadratic constraint approach
Simon Michalowsky, Carsten Scherer, Christian Ebenbauer
We consider the problem of analyzing and designing gradient-based discrete-time optimization algorithms for a class of unconstrained optimization problems having strongly convex ob…
Characterizing the learning dynamics in extremum seeking
Stefan Wildhagen, Simon Michalowsky, Jan Feiling +1
We consider perturbation-based extremum seeking, which recovers an approximate gradient of an analytically unknown objective function through measurements. Using classical needle v…
On the Lie bracket approximation approach to distributed optimization: Extensions and limitations
Simon Michalowsky, Bahman Gharesifard, Christian Ebenbauer
We consider the problem of solving a smooth convex optimization problem with equality and inequality constraints in a distributed fashion. Assuming that we have a group of agents a…
A family of extremum seeking laws for a unicycle model with a moving target: theoretical and experimental studies
Victoria Grushkovskaya, Simon Michalowsky, Alexander Zuyev +2
In this paper, we propose and practically evaluate a class of gradient-free control functions ensuring the motion of a unicycle-type system towards the extremum point of a time-var…
A Lie bracket approximation approach to distributed optimization over directed graphs
Simon Michalowsky, Bahman Gharesifard, Christian Ebenbauer
We consider a group of computation units trying to cooperatively solve a distributed optimization problem with shared linear equality and inequality constraints. Assuming that the…
Gradient approximation and extremum seeking via needle variations
Simon Michalowsky, Christian Ebenbauer
We consider a gradient approximation scheme that is based on applying needle shaped inputs. By using ideas known from the classic proof of the Pontryagin Maximum Principle we deriv…