Learning Deep Input-Output Stable Dynamics

Ryosuke Kojima, Yuji Okamoto

Keywords: Dynamical systems, Input-output stability, Nonlinear system identification

TL;DR: This paper proposed a method to learn nonlinear dynamical systems guaranteeing the input-output stability using the Hamilton-Jacobi inequality.

Abstract: Learning stable dynamics from observed time-series data is an essential problem in robotics, physical modeling, and systems biology. Many of these dynamics are represented as an inputs-output system to communicate with the external environment. In this study, we focus on input-output stable systems, exhibiting robustness against unexpected stimuli and noise. We propose a method to learn nonlinear systems guaranteeing the input-output stability. Our proposed method utilizes the differentiable projection onto the space satisfying the Hamilton-Jacobi inequality to realize the input-output stability. The problem of finding this projection can be formulated as a quadratic constraint quadratic programming problem, and we derive the particular solution analytically. Also, we apply our method to a toy bistable model and the task of training a benchmark generated from a glucose-insulin simulator. The results show that the nonlinear system with neural networks by our method achieves the input-output stability, unlike naive neural networks. Our code is available at https://github.com/clinfo/DeepIOStability .

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2206.13093/code)