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- Title
Bidirectional dynamic neural networks with physical analyzability.
- Authors
Li, Changjun; Zhao, Fei; Lan, Xuguang; Tian, Zhiqiang; Tao, Tao; Mei, Xuesong
- Abstract
The rapid growth in research exploiting deep learning to predict mechanical systems has revealed a new route for system identification; however, the analytic model as a white box has not been replaced in applications because of its open physical information. In contrast, the models generated by end-to-end learning usually lack the ability of physical analysis, which makes them inapplicable in many situations. Consequently, high-accuracy modeling with physical analyzability becomes a necessity. In this paper, we introduce bidirectional dynamic neural networks, a deep learning framework that can infer the dynamics of physical systems from control signals and observed state trajectories. Based on forward dynamics, we train the neural ordinary differential equations in a trajectory backtracking algorithm. With the trained model, the inverse dynamics can be calculated and based on Lagrangian Mechanics , the physical parameters of the mechanical system can be estimated, including inertia, Coriolis and centrifugal forces, and gravity. As a result, the model can seamlessly incorporate prior knowledge, learn unknown dynamics without human intervention, and provide information as transparent as analytic models. We demonstrate our method on simulated 2-axis and 6-axis robots to evaluate model accuracy, including physical parameters and verified its applicability on real 7-axis robots. The experimental results show that this method is superior to the existing methods. This framework provides a new idea for system identification by providing interpretable, physically consistent models for physical systems.
- Subjects
LAGRANGIAN mechanics; ORDINARY differential equations; CORIOLIS force; SYSTEM identification; CENTRIFUGAL force; HUMAN fingerprints
- Publication
Nonlinear Dynamics, 2023, Vol 111, Issue 17, p16309
- ISSN
0924-090X
- Publication type
Article
- DOI
10.1007/s11071-023-08672-8