JACIII Vol.27 No.4 pp. 537-542
doi: 10.20965/jaciii.2023.p0537

Research Paper:

Exploring Model Structures to Reduce Data Requirements for Neural ODE Learning in Control Systems

Takanori Hashimoto ORCID Icon, Nobuyuki Matsui, Naotake Kamiura ORCID Icon, and Teijiro Isokawa ORCID Icon

Graduate School of Engineering, University of Hyogo
2167 Shosha, Himeji, Hyogo 671-2280, Japan

December 20, 2022
February 6, 2023
July 20, 2023
system identification, neural ODE

In this study, we investigate model structures for neural ODEs to improve the data efficiency in learning the dynamics of control systems. We introduce two model structures and compare them with a typical baseline structure. The first structure considers the relationship between the coordinates and velocities of the control system, while the second structure adds linearity with respect to the control term to the first structure. Both of these structures can be easily implemented without requiring additional computation. In numerical experiments, we evaluate these structure on simulated simple pendulum and CartPole systems and show that incorporating these characteristics into the model structure leads to accurate learning with a smaller amount of training data compared to the baseline structure.

Neural ODEs approximation of system dynamics

Neural ODEs approximation of system dynamics

Cite this article as:
T. Hashimoto, N. Matsui, N. Kamiura, and T. Isokawa, “Exploring Model Structures to Reduce Data Requirements for Neural ODE Learning in Control Systems,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.4, pp. 537-542, 2023.
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