Physics-Informed Hybrid Modeling of Pneumatic Artificial Muscles

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 network-based 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.