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

Autor:	Kohler, Hector, Clement, Benoit, Chaffre, Thomas, Chenadec, Gilles Le
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Systems and Control Computer Science - Machine Learning Computer Science - Robotics
Zdroj:	IFAC-PapersOnLine, Volume 55, Issue 31, 2022
Druh dokumentu:	Working Paper
DOI:	10.1016/j.ifacol.2022.10.401
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2404.12025 Zobrazit plný text záznamu View this record from Arxiv