Lagrangian Neural Network-Based Control
Improving Robotic Trajectory Tracking via Linearized Feedback
Abstract
This letter introduces a control framework that leverages Lagrangian neural network (LNN) for computed torque control (CTC) of robotic systems with unknown dynamics. Unlike prior LNN-based controllers that are placed outside the feedback-linearization framework (e.g., feedforward), we embed an LNN inverse-dynamics model within a CTC loop, thereby shaping the closed-loop error dynamics. This strategy, referred to as LNN-CTC, ensures a physically consistent model and improves extrapolation, requiring neither prior model knowledge nor extensive training data. The approach is experimentally validated on a robotic arm with four degrees of freedom and compared with conventional model-based CTC, physics-informed neural network (PINN)-CTC, deep neural network (DNN)-CTC, an LNN-based feedforward controller, and a PID controller. Results demonstrate that LNN-CTC significantly outperforms model-based baselines by up to 30 % in tracking accuracy, achieving high performance with minimal training data. In addition, LNN-CTC outperforms all other evaluated baselines in both tracking accuracy and data efficiency, attaining lower joint-space RMSE for the same training data. The findings highlight the potential of physics-informed neural architectures to generalize robustly across various operating conditions and contribute to narrowing the performance gap between learned and classical control strategies.
Details
- Organisationseinheit(en)
-
Institut für Mechatronische Systeme
- Externe Organisation(en)
-
Berliner Hochschule für Technik (BHT)
- Typ
- Artikel
- Journal
- IEEE Robotics and Automation Letters
- Band
- 11
- Seiten
- 2546-2553
- Anzahl der Seiten
- 8
- ISSN
- 2377-3766
- Publikationsdatum
- 20.01.2026
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Steuerungs- und Systemtechnik, Biomedizintechnik, Mensch-Maschine-Interaktion, Maschinenbau, Maschinelles Sehen und Mustererkennung, Angewandte Informatik, Steuerung und Optimierung, Artificial intelligence
- Elektronische Version(en)
-
https://doi.org/10.1109/LRA.2026.3653326 (Zugang:
Offen
)