Unbiased Extremum Seeking for PDEs

TL;DR

This paper introduces an unbiased extremum seeking method for PDE systems that guarantees exponential convergence to the optimum, overcoming limitations of prior local stability guarantees and effectively handling PDE delay and diffusion dynamics.

Contribution

It proposes a novel unbiased extremum seeking approach for PDEs that ensures global exponential convergence and compensates for PDE delay and diffusion effects.

Findings

Guarantees exponential convergence to the extremum.

Handles delay and diffusion PDE dynamics effectively.

Demonstrates efficacy through numerical simulations.

Abstract

There have been recent efforts that combine seemingly disparate methods, extremum seeking (ES) optimization and partial differential equation (PDE) backstepping, to address the problem of model-free optimization with PDE actuator dynamics. In contrast to prior PDE-compensating ES designs, which only guarantee local stability around the extremum, we introduce unbiased ES that compensates for delay and diffusion PDE dynamics while ensuring exponential and unbiased convergence to the optimum. Our method leverages exponentially decaying/growing signals within the modulation/demodulation stages and carefully selected design parameters. The stability analysis of our designs relies on a state transformation, infinite-dimensional averaging, local exponential stability of the averaged system, local stability of the transformed system, and local exponential stability of the original system. Numerical simulations are presented to demonstrate the efficacy of the developed designs.