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arXiv · 2203.09710

Learning Stabilizable Deep Dynamics Models

Kenji Kashima·Ryota Yoshiuchi·Yu Kawano
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Abstract

When neural networks are used to model dynamics, properties such as stability of the dynamics are generally not guaranteed. In contrast, there is a recent method for learning the dynamics of autonomous systems that guarantees global exponential stability using neural networks. In this paper, we propose a new method for learning the dynamics of input-affine control systems. An important feature is that a stabilizing controller and control Lyapunov function of the learned model are obtained as well. Moreover, the proposed method can also be applied to solving Hamilton-Jacobi inequalities. The usefulness of the proposed method is examined through numerical examples.

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BibTeXRIS

Kenji Kashima, Ryota Yoshiuchi, Yu Kawano. 2022-03-18. Learning Stabilizable Deep Dynamics Models. https://arxiv.org/abs/2203.09710

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