Improved residual mode separation for finite-dimensional control of PDEs: application to the Euler-Bernoulli beam

TL;DR

This paper introduces a modified $H_$ control design for PDEs, specifically Euler-Bernoulli beams, that accounts for spillover effects by incorporating gains for truncated modes, enhancing control robustness.

Contribution

It proposes a simple modification to $H_$ control that prevents spillover in finite-dimensional PDE control by treating control effects on truncated modes as disturbances.

Findings

Numerical simulation confirms effectiveness on an aluminum beam.

Method applicable to various PDEs like heat, wave, and Kuramoto-Sivashinsky.

Framework adaptable to other control strategies such as guaranteed cost and stability analysis.

Abstract

We consider a simply-supported Euler-Bernoulli beam with viscous and Kelvin--Voigt damping. Our objective is to attenuate the effect of an unknown distributed disturbance using one piezoelectric actuator. We show how to design a suitable $H_\infty$ state-feedback controller based on a finite number of dominating modes. If the remaining (infinitely many) modes are ignored, the calculated $L^2$ gain is wrong. This happens because of the spillover phenomenon that occurs when the effect of the control on truncated modes is not accounted for in the feedback design. We propose a simple modification of the $H_\infty$ cost that prevents spillover. The key idea is to treat the control as a disturbance in the truncated modes and find the corresponding $L^2$ gains using the bounded real lemma. These $L^2$ gains are added to the control weight in the $H_\infty$ cost for the dominating modes, which prevents spillover. A numerical simulation of an aluminum beam with realistic parameters demonstrates the effectiveness of the proposed method. The presented approach is applicable to other types of PDEs, such as the heat, wave, and Kuramoto-Sivashinsky equations, as well as their semilinear versions. While this work focuses on $H_\infty$ control, the same methodology can be applied to guaranteed cost control, regional stability analysis, input-to-state stability, and systems with time-varying delays, including sampled-data systems.