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On Recurrent Neural Networks for learning-based control: recent results and ideas for future developments. (arXiv:2111.13557v2 [eess.SY] UPDATED)
May 11, 2022, 1:12 a.m. | Fabio Bonassi, Marcello Farina, Jing Xie, Riccardo Scattolini
cs.LG updates on arXiv.org arxiv.org
This paper aims to discuss and analyze the potentialities of Recurrent Neural
Networks (RNN) in control design applications. The main families of RNN are
considered, namely Neural Nonlinear AutoRegressive eXogenous, (NNARX), Echo
State Networks (ESN), Long Short Term Memory (LSTM), and Gated Recurrent Units
(GRU). The goal is twofold. Firstly, to survey recent results concerning the
training of RNN that enjoy Input-to-State Stability (ISS) and Incremental
Input-to-State Stability ($\delta$ISS) guarantees. Secondly, to discuss the
issues that still hinder the widespread …
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