Multivariable Gradient-Based Extremum Seeking Control with Saturation Constraints

TL;DR

This paper develops a multivariable gradient-based extremum seeking control method that handles input and gradient saturation using LMIs, ensuring stability and convergence with non-diagonal gains.

Contribution

It introduces a novel LMI-based design framework for multivariable ESC under saturation, allowing for non-diagonal control gains and rigorous stability guarantees.

Findings

Guarantees exponential stability under saturation

Enables design of non-diagonal control gains

Numerical simulations confirm convergence with saturation

Abstract

This paper addresses the multivariable gradient-based extremum seeking control (ESC) subject to saturation. Two distinct saturation scenarios are investigated here: saturation acting on the input of the function to be optimized, which is addressed using an anti-windup compensation strategy, and saturation affecting the gradient estimate. In both cases, the unknown Hessian matrix is represented using a polytopic uncertainty description, and sufficient conditions in the form of linear matrix inequalities (LMIs) are derived to design a stabilizing control gain. The proposed conditions guarantee exponential stability of the origin for the average closed-loop system under saturation constraints. With the proposed design conditions, non-diagonal control gain matrices can be obtained, generalizing conventional ESC designs that typically rely on diagonal structures. Stability and convergence are rigorously proven using the Averaging Theory for dynamical systems with Lipschitz continuous right-hand sides. Numerical simulations illustrate the effectiveness of the proposed ESC algorithms, confirming the convergence even in the presence of saturation.