PID Tuning using Cross-Entropy Deep Learning: a Lyapunov Stability Analysis

Fuente: arXiv
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Auteurs principaux: Kohler, Hector, Clement, Benoit, Chaffre, Thomas, Chenadec, Gilles Le
Format: Preprint
Publié: 2024
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author Kohler, Hector
Clement, Benoit
Chaffre, Thomas
Chenadec, Gilles Le
author_facet Kohler, Hector
Clement, Benoit
Chaffre, Thomas
Chenadec, Gilles Le
contents Underwater Unmanned Vehicles (UUVs) have to constantly compensate for the external disturbing forces acting on their body. Adaptive Control theory is commonly used there to grant the control law some flexibility in its response to process variation. Today, learning-based (LB) adaptive methods are leading the field where model-based control structures are combined with deep model-free learning algorithms. This work proposes experiments and metrics to empirically study the stability of such a controller. We perform this stability analysis on a LB adaptive control system whose adaptive parameters are determined using a Cross-Entropy Deep Learning method.
format Preprint
id arxiv_https___arxiv_org_abs_2404_12025
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle PID Tuning using Cross-Entropy Deep Learning: a Lyapunov Stability Analysis
Kohler, Hector
Clement, Benoit
Chaffre, Thomas
Chenadec, Gilles Le
Systems and Control
Machine Learning
Robotics
Underwater Unmanned Vehicles (UUVs) have to constantly compensate for the external disturbing forces acting on their body. Adaptive Control theory is commonly used there to grant the control law some flexibility in its response to process variation. Today, learning-based (LB) adaptive methods are leading the field where model-based control structures are combined with deep model-free learning algorithms. This work proposes experiments and metrics to empirically study the stability of such a controller. We perform this stability analysis on a LB adaptive control system whose adaptive parameters are determined using a Cross-Entropy Deep Learning method.
title PID Tuning using Cross-Entropy Deep Learning: a Lyapunov Stability Analysis
topic Systems and Control
Machine Learning
Robotics
url https://arxiv.org/abs/2404.12025