Extremum seeking (ES) is a powerful approach to the optimization and stabilization of
unknown dynamic systems and is an active field of research in control theory. This paper …

Extremum seeking is a form of adaptive control where the steady-state input-output
characteristic is optimized, without requiring any explicit knowledge about this input-output …

Stochastic Averaging and Extremum Seeking treats methods inspired by attempts to
understand the seemingly non-mathematical question of bacterial chemotaxis and their …

In practice, the convergence rate and stability of perturbation based extremum-seeking
schemes can be very sensitive to the curvature of the plant map. An example of this can be …

We apply the recently introduced method of stochastic extremum seeking to navigate a
nonholonomic unicycle towards the maximum of an unknown, spatially distributed signal …

Feedback optimization is a control paradigm that enables physical systems to autonomously
reach efficient operating points. Its central idea is to interconnect optimization iterations in …

We investigate stochastic averaging theory in continuous time for locally Lipschitz systems
and the applications of this theory to stability analysis of stochastic extremum seeking …

We design and analyze the scalar gradient extremum seeking control feedback for static
maps with actuation dynamics governed by diffusion PDEs. Conceptually, a non-model …

In this paper, a spark advance self-optimization strategy is presented for lean-burn operation
mode of spark-ignition (SI) engine which aims on-board combustion phase tuning to achieve …

One of the most important factors affecting fuel efficiency and emissions of automotive
engines is combustion quality that is usually controlled by managing spark advance (SA) in …