Discrete time extremum seeking using stochastic perturbations

Abstract

Extremum seeking using periodic, deterministic perturbations has been an effective method for non-model based real time optimisation when only limited knowledge of the system is available. However, periodicity can naturally lead to predictability which is undesirable in some applications and unrepresentative of some biological optimisation processes such as bacterial Chemotaxis. With this in mind, it is useful to investigate the benefit of employing stochastic perturbations in the context of a typical extremum seeking architecture, and to compare the approach with existing stochastic optimisation techniques. In this work, we show that the convergence towards the extremum of a static map can ..