RKHS Embedding for Estimating Nonlinear Piezoelectric Systems

TL;DR

This paper presents an RKHS-based adaptive estimation method for nonlinear piezoelectric systems, enabling data-driven modeling without detailed prior knowledge of the nonlinearities involved.

Contribution

It introduces a novel RKHS-based adaptive estimator for nonlinear piezoelectric systems, allowing model development without explicit nonlinearity structure knowledge.

Findings

Numerical studies demonstrate the estimator's effectiveness.

The method accurately captures nonlinear behaviors in piezoelectric beams.

The approach offers a flexible alternative to traditional modeling techniques.

Abstract

Nonlinearities in piezoelectric systems can arise from internal factors such as nonlinear constitutive laws or external factors like realizations of boundary conditions. It can be difficult or even impossible to derive detailed models from the first principles of all the sources of nonlinearity in a system. As a specific example, in traditional modeling techniques that use electric enthalpy density with higher-order terms, it can be problematic to choose which polynomial nonlinearities are essential. This paper introduces adaptive estimator techniques to estimate the nonlinearities that can arise in certain piezoelectric systems. Here an underlying assumption is that the nonlinearities can be modeled as functions in a reproducing kernel Hilbert space (RKHS). Unlike traditional modeling approaches, the approach discussed in this paper allows the development of models without knowledge of the precise form or structure of the nonlinearity. This approach can be viewed as a data-driven method to approximate the unknown nonlinear system. This paper introduces the theory behind the adaptive estimator and studies the effectiveness of this approach numerically for a class of nonlinear piezoelectric composite beams.