Event-Triggered Newton-Based Extremum Seeking Control

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

Computing Research Repository (CoRR)（2025）

Cited 0|Views2

Abstract

This paper proposes the incorporation of static event-triggered control in the actuation path of Newton-based extremum seeking and its comparison with the earlier gradient version. As in the continuous methods, the convergence rate of the gradient approach depends on the unknown Hessian of the nonlinear map to be optimized, whereas the proposed event-triggered Newton-based extremum seeking eliminates this dependence, becoming user-assignable. This is achieved by means of a dynamic estimator for the Hessian's inverse, implemented as a Riccati equation filter. Lyapunov stability and averaging theory for discontinuous systems are applied to analyze the closed-loop system. Local exponential practical stability is guaranteed to a small neighborhood of the extremum point of scalar and static maps. Numerical simulations illustrate the advantages of the proposed approach over the previous gradient method, including improved convergence speed, followed by a reduction in the amplitude and updating frequency of the control signals.

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【要点】：本文提出了一种将静态事件触发控制与牛顿基极值寻优相结合的方法，通过动态估计逆Hessian矩阵，实现了用户可指定的收敛速率，并对比了与早期梯度法的性能差异。

【方法】：采用动态估计器估计Hessian逆矩阵，通过Riccati方程滤波器实现，并结合Lyapunov稳定性和断续系统的平均理论分析闭环系统。

【实验】：通过数值模拟，证明了所提方法相较于梯度法在收敛速度、控制信号幅值和更新频率方面的优势，实验使用的数据集未在文中明确提及。

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