Year: 2025
Type: conference-paper
Abstract: Pneumatic Artificial Muscles (PAMs) are complex nonlinear systems characterized by hysteresis, making them challenging to model with classical system identification methods. While deep learning has emerged as a powerful tool for modeling nonlinear systems from data, purely neural networkbased models often lack interpretability and are prone to overfitting. To address these challenges, this study explores several hybrid approaches that combine analytical models with neural networks to model PAM behavior more effectively. The results demonstrate that hybrid models significantly outperform both purely analytical and black-box neural network models, particularly in terms of generalization and dynamic accuracy. Among the approaches, the Physics-Informed Neural Network (PINN) unsupervised model shows the most robust performance, capturing complex PAM dynamics while maintaining computational efficiency. These findings suggest that hybrid modeling is a promising and scalable solution for accurately representing the intricate behavior of PAMs.
Institutions: Lyon 1 Université, École Centrale de Lyon, Centre National de la Recherche Scientifique, Laboratoire Ampère, Institut National des Sciences Appliquées de Lyon, Informatique, BioInformatique, Systèmes Complexes, Université d'Évry Val-d'Essonne, Instituto de Investigación Tecnológica, Universidad Pontificia Comillas
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4406959745