Global Output-Feedback Extremum Seeking Control with Source Seeking Experiments

TL;DR

This paper introduces a global output-feedback extremum seeking control method for uncertain nonlinear systems, capable of achieving near-optimal solutions with proven stability and convergence, validated through experiments including source seeking with limited sensing.

Contribution

It presents a novel extremum seeking control scheme that guarantees global convergence for uncertain nonlinear systems with arbitrary relative degrees, using a monitoring function and time-scaling techniques.

Findings

Achieves arbitrarily small proximity to the optimal point.

Ensures global convergence and stability.

Validated through numerical and source-seeking experiments.

Abstract

This paper discusses the design of an extremum seeking controller that relies on a monitoring function for a class of SISO uncertain nonlinear systems characterized by arbitrary and uncertain relative degree. Our demonstration illustrates the feasibility of achieving an arbitrarily small proximity to the desired optimal point through output feedback. The core concept involves integrating a monitoring function with a norm state observer for the unitary relative degree case and its expansion to arbitrary relative degrees by means of the employment of a time-scaling technique. Significantly, our proposed scheme attains the extremum of an unknown nonlinear mapping across the entire domain of initial conditions, ensuring global convergence and stability for the real-time optimization algorithm. Furthermore, we provide tuning rules to ensure convergence to the global maximum in the presence of local extrema. To validate the effectiveness of the proposed approach, we present a numerical example and apply it to a source-seeking problem involving a cart-track linear positioning servomechanism. Notably, the cart lacks the ability to sense its velocity or the source's position, but can detect the source of a light signal of unknown concentration field.