A physics-guided neural network framework for prediction and control of spring-mass running
Bioinspiration & biomimetics, cilt.21, sa.4, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 21 Sayı: 4
- Basım Tarihi: 2026
- Doi Numarası: 10.1088/1748-3190/ae7e2d
- Dergi Adı: Bioinspiration & biomimetics
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, Compendex, EMBASE, INSPEC, MEDLINE, Zoological Record
- Anahtar Kelimeler: legged locomotion, neural networks, spring-mass running, trajectory tracking
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Hacettepe Üniversitesi Adresli: Evet
Özet
The spring-mass template acts as a fundamental bridge between animal locomotion and legged robotic platforms. However, controlling spring-mass dynamics involves a persistent trade-off: numerical integration offers accuracy but high computational cost, while approximate analytical solutions provide efficiency but suffer from linearization errors. To resolve this, we propose a physics-guided hybrid neural network framework for the spring-mass running template. Unlike closed-box predictors trained end-to-end, our architecture enforces flight phase physics by embedding exact analytical solutions for the tractable ballistic dynamics directly into the network structure, and learning only the non-integrable stance dynamics. This 'analytically augmented' design enhances interpretability without sacrificing accuracy. For control, we introduce a mixed-objective training strategy that combines supervised imitation with goal-conditioned learning, enabling precise trajectory tracking without the computational burden of per-step optimization. We evaluated the framework's prediction accuracy, control precision, and runtime efficiency through extensive simulations. Furthermore, we validate the robustness of the learned policies through a comprehensive basin of attraction analysis, demonstrating that the proposed networks stabilize contiguous manifolds of highly perturbed initial conditions compared to existing methods. Finally, we assessed the framework's real-world adaptability by validating our predictor against an archival experimental dataset from a physical one-legged hopper. Our results demonstrate that this hybrid approach outperforms existing analytical and data-driven methods in accuracy and robustness while maintaining the low inference times required for real-time embedded control.