Event-Triggered Newton Extremum Seeking for Multivariable Optimization

Victor Hugo Pereira Rodrigues; Tiago Roux Oliveira; Miroslav Krstic; Paulo Tabuada

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arXiv · 2601.14416

Event-Triggered Newton Extremum Seeking for Multivariable Optimization

Victor Hugo Pereira Rodrigues·Tiago Roux Oliveira·Miroslav Krstic·Paulo Tabuada

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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.

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BibTeX RIS

Victor Hugo Pereira Rodrigues, Tiago Roux Oliveira, Miroslav Krstic, Paulo Tabuada. 2026-01-20. Event-Triggered Newton Extremum Seeking for Multivariable Optimization. https://arxiv.org/abs/2601.14416

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