Extremum seeking with stochastic perturbations

Abstract

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