Adaptive Extremum Seeking Control via the RMSprop Optimizer

TL;DR

This paper introduces an adaptive extremum seeking control method using the RMSprop optimizer to improve convergence rates in model-free optimization, with stability proofs based on Lyapunov functions for interconnected systems.

Contribution

It proposes integrating RMSprop into extremum seeking control to enhance convergence speed and stability, a novel approach in model-free optimization.

Findings

RMSprop ESC achieves improved convergence rates.

Practical stability is proven using Lyapunov functions.

Applicable to interconnected systems.

Abstract

Extremum Seeking Control (ESC) is a well-known set of continuous time algorithms for model-free optimization of a cost function. One issue for ESCs is the convergence rates of parameters to extrema of unknown cost functions. The local convergence rate depends on the second, or sometimes higher, order derivatives of the unknown cost function. To mitigate this dependency, we propose the use of the RMSprop optimizer for ESCs as RMSprop is an adaptive gradient-based optimizer which attempts to have a normalized convergence rate in all parameters. Practical stability results are given for this RMSprop ESC (RMSpESC). In particular notability, the proof of practical stability uses Lyapunov function based on observed contracting, attractive sets. Versions of this Lyapunov function could be applied to other areas of applications, in particular for interconnected systems.