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

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

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)
72 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)7-12
Number of pages6
JournalIFAC-PapersOnLine
Volume55
Issue number31
DOIs
Publication statusPublished - 1 Oct 2022
Event14th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles, CAMS 2022 - Kongens Lyngby, Germany
Duration: 14 Sept 202216 Sept 2022

Keywords

  • Adaptive Control
  • Deep Learning
  • Lyapunov Stability
  • Underwater Vehicle

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