Challenges in Implementing Nonlinear System Identification Techniques in Structural Dynamic

Abstract

Structural dynamics, the study of structures and their behavior under dynamic loads, is an area of crucial importance in fields such as civil engineering, aerospace engineering, and mechanical engineering. Accurately modeling these dynamic systems is fundamental for design, analysis, and performance prediction. While many systems in this field can be approximated using linear models, the complex and nonlinear behavior of certain systems necessitates the use of nonlinear system identification techniques. The successful application of these techniques, however, remains a significant challenge due to a variety of issues. A critical challenge is the determination of the model structure. In contrast to linear system identification where well-established model structures are used, the nonlinear equivalent does not offer universally applicable model structures, which poses a difficulty in selecting the correct form of the model. Furthermore, the computational complexity of nonlinear system identification algorithms is significant, mainly due to the involved mathematics and typically large datasets. Convergence issues also pose challenges in implementing nonlinear system identification techniques. Iterative algorithms common to these methods often face the risk of becoming entrapped in local minima due to the non-convex nature of many nonlinear system identification problems, preventing them from locating the global optimum. Additionally, validation of the identified nonlinear models remains complex. Although a model might fit the input-output data well, its performance in untested scenarios is not guaranteed. Furthermore, the lack of parameter interpretability in complex nonlinear models, such as neural networks, is an issue for engineers interested in understanding the physical underpinnings of their systems. Lastly, the sensitivity of nonlinear system identification to noise in the input and output data can impact the accuracy of the identified system. Despite these challenges, ongoing research endeavors continue to develop and refine methods that promise to enhance the effectiveness of nonlinear system identification in structural dynamics.