Data-Efficient Extremum-Seeking Control Using Kernel-Based Function Approximation

TL;DR

This paper introduces a data-efficient extremum-seeking control method that leverages kernel-based function approximation to reduce the number of measurements needed for optimization, enhancing efficiency during regular system operation.

Contribution

It presents a novel sampled-data ESC approach that uses online data to approximate the cost function, reducing measurement requirements compared to traditional methods.

Findings

Reduces measurement count in extremum-seeking control

Ensures stability with the new approximation approach

Demonstrates improved efficiency in simulations

Abstract

Existing extremum-seeking control (ESC) approaches typically rely on applying repeated perturbations to input parameters and performing measurements of the corresponding performance output. The required separation between the different timescales in the ESC loop means that performing these measurements can be time-consuming. Moreover, performing these measurements can be costly in practice, e.g., due to the use of resources. With these challenges in mind, it is desirable to reduce the number of measurements needed to optimize performance. Therefore, in this work, we present a sampled-data ESC approach aimed at reducing the number of measurements that need to be performed. In the proposed approach, we use input-output data obtained during regular operation of the extremum-seeking controller to construct online an approximation of the system's underlying cost function. By using this approximation to perform parameter updates when a decrease in the cost can be guaranteed, instead of performing additional measurements to perform this update, we make more efficient use of data collected during regular operation of the extremum-seeking controller. As a result, we indeed obtain a reduction in the required number of measurements to achieve optimization. We provide a stability analysis of the novel sampled-data ESC approach, and demonstrate the benefits of the synergy between kernel-based function approximation and standard ESC in simulation on a multi-input dynamical system.