Event-Triggered Newton Extremum Seeking for Multivariable Optimization
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866912836730486784 |
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| author | Rodrigues, Victor Hugo Pereira Oliveira, Tiago Roux Krstic, Miroslav Tabuada, Paulo |
| author_facet | Rodrigues, Victor Hugo Pereira Oliveira, Tiago Roux Krstic, Miroslav Tabuada, Paulo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_14416 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Event-Triggered Newton Extremum Seeking for Multivariable Optimization Rodrigues, Victor Hugo Pereira Oliveira, Tiago Roux Krstic, Miroslav Tabuada, Paulo Optimization and Control Systems and Control 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. |
| title | Event-Triggered Newton Extremum Seeking for Multivariable Optimization |
| topic | Optimization and Control Systems and Control |
| url | https://arxiv.org/abs/2601.14416 |