Robust model reductions for the boundary feedback stabilization of magnetizable piezoelectric beams

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Authors: Ahmet Kaan Aydın, Ahmet Özer, Jacob Walterman
Cited by: 3
References: 25

Abstract

Magnetizable piezoelectric beams exhibit strong couplings between mechanical, electric, and magnetic fields, significantly affecting their high-frequency vibrational behavior. Ensuring exponential stability under boundary feedback controllers is challenging due to the uneven distribution of high-frequency eigenvalues in standard finite difference models. While numerical filtering can mitigate instability as the discretization parameter tends to zero, its reliance on explicit spectral computations is computationally demanding. This work introduces two novel model reduction techniques for stabilizing magnetizable piezoelectric beams. First, a finite element discretization using linear splines is developed, improving numerical stability over standard finite differences. However, this method still requires numerical filtering to eliminate spurious high-frequency modes, necessitating full spectral decomposition. Numerical investigations further reveal a direct dependence of the optimal filtering threshold on feedback amplifiers. To overcome these limitations, an alternative order-reduction finite difference scheme is proposed, eliminating the need for numerical filtering. Using a Lyapunov-based framework, we establish exponential stability with decay rates independent of the discretization parameter. The reduced model also exhibits exponential error decay and uniform energy convergence to the original system. Numerical simulations validate the effectiveness of the proposed methods, and we construct an algorithm for separating eigenpairs for the proper application of the numerical filtering. Comparative spectral analyses and energy decay results confirm the superior stability and efficiency of the proposed approach, providing a robust framework for model reduction in coupled partial differential equation systems.

Fields of Study & Subjects

Structural Health Monitoring Techniques Ultrasonics and Acoustic Wave Propagation Model Reduction and Neural Networks

Publication details

Journal / source
Evolution equations and control theory
Publisher
American Institute of Mathematical Sciences (AIMS)
Volume
21
Issue
0
Pages
244–276
Publication date
1 January 2026
Language
EN