Event-Triggered Newton Extremum Seeking for Multivariable Optimization
Abstract
This paper presents a static event-triggered control strategy for multivariable Newton-based extremum seeking. The proposed method integrates event-triggered actuation into the Newton-based optimization framework to reduce control updates while maintaining rapid convergence to the extremum. Unlike traditional gradient-based extremum seeking, where the convergence rate depends on the unknown Hessian of the cost function, the proposed approach employs a dynamic estimator of the Hessian inverse, formulated as a Riccati equation, enabling user-assignable convergence rates. The event-triggering mechanism is designed to minimize unnecessary actuation updates while preserving stability and performance. Using averaging theory, we establish local stability results and exponential convergence to a neighborhood of the unknown extremum point. Additionally, numerical simulations illustrate the benefits of the proposed approach over gradient-based and continuously actuated Newton-based extremum seeking, showing improved convergence rates and reduced control update frequency, leading to more efficient implementation in real-time optimization scenarios.
Cite
@article{arxiv.2601.14416,
title = {Event-Triggered Newton Extremum Seeking for Multivariable Optimization},
author = {Victor Hugo Pereira Rodrigues and Tiago Roux Oliveira and Miroslav Krstic and Paulo Tabuada},
journal= {arXiv preprint arXiv:2601.14416},
year = {2026}
}