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

Hector Kohler; Benoit Clement; Thomas Chaffre; Gilles Le Chenadec

doi:10.1016/j.ifacol.2022.10.401

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arXiv · 2404.12025

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

Hector Kohler·Benoit Clement·Thomas Chaffre·Gilles Le Chenadec

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Abstract

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.

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BibTeX RIS

Hector Kohler, Benoit Clement, Thomas Chaffre, Gilles Le Chenadec. 2024-04-18. PID Tuning using Cross-Entropy Deep Learning: a Lyapunov Stability Analysis. https://doi.org/10.1016/j.ifacol.2022.10.401

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