Combining Robust Control and Machine Learning for Uncertain Nonlinear Systems Subject to Persistent Disturbances

Fuente: arXiv
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Main Authors: Banderchuk, A., Coutinho, D., Camponogara, E.
Format: Preprint
Published: 2023
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author Banderchuk, A.
Coutinho, D.
Camponogara, E.
author_facet Banderchuk, A.
Coutinho, D.
Camponogara, E.
contents This paper proposes a control strategy consisting of a robust controller and an Echo State Network (ESN) based control law for stabilizing a class of uncertain nonlinear discrete-time systems subject to persistent disturbances. Firstly, the robust controller is designed to ensure that the closed-loop system is Input-to-State Stable (ISS) with a guaranteed stability region regardless of the ESN control action and exogenous disturbances. Then, the ESN based controller is trained in order to mitigate the effects of disturbances on the system output. A numerical example demonstrates the potentials of the proposed control design method.
format Preprint
id arxiv_https___arxiv_org_abs_2303_11890
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Combining Robust Control and Machine Learning for Uncertain Nonlinear Systems Subject to Persistent Disturbances
Banderchuk, A.
Coutinho, D.
Camponogara, E.
Systems and Control
This paper proposes a control strategy consisting of a robust controller and an Echo State Network (ESN) based control law for stabilizing a class of uncertain nonlinear discrete-time systems subject to persistent disturbances. Firstly, the robust controller is designed to ensure that the closed-loop system is Input-to-State Stable (ISS) with a guaranteed stability region regardless of the ESN control action and exogenous disturbances. Then, the ESN based controller is trained in order to mitigate the effects of disturbances on the system output. A numerical example demonstrates the potentials of the proposed control design method.
title Combining Robust Control and Machine Learning for Uncertain Nonlinear Systems Subject to Persistent Disturbances
topic Systems and Control
url https://arxiv.org/abs/2303.11890