On the Design and Analysis of Multivariable Extremum Seeking Control using Fast Fourier Transform

TL;DR

This paper introduces a multivariable extremum seeking control method utilizing FFT to optimize networked subsystems based on a single measurement, with analytical design rules and stability analysis.

Contribution

It presents a novel FFT-based gradient estimation approach for multivariable extremum seeking and provides stability analysis for static maps.

Findings

Effective in wind farm power optimization

Successful in heat exchanger network optimization

Analytical design rules for FFT-based gradient estimation

Abstract

This paper proposes a multivariable extremum seeking scheme using Fast Fourier Transform (FFT) for a network of subsystems working towards optimizing the sum of their local objectives, where the overall objective is the only available measurement. Here, the different inputs are perturbed with different dither frequencies, and the power spectrum of the overall output signal obtained using FFT is used to estimate the steady-state cost gradient w.r.t. each input. The inputs for the subsystems are then updated using integral control in order to drive the respective gradients to zero. This paper provides analytical rules for designing the FFT-based gradient estimation algorithm and analyzes the stability properties of the resulting extremum seeking scheme for the static map setting. The effectiveness of the proposed FFT-based multivariable extremum seeking scheme is demonstrated using two examples, namely, wind farm power optimization problem, and a heat exchanger network for industrial waste-to-heat recovery.