PID Tuning using Cross-Entropy Deep Learning: a Lyapunov Stability Analysis. Issue 31 (2022)

• Record Type: Journal Article
• Authors: Kohler, Hector; Clement, Benoit; Chaffre, Thomas; Chenadec, Gilles Le
• Is Part Of: IFAC-PapersOnLine. Volume 55:Issue 31(2022)
• Page Start: 7
• Page End: 12
• Publication Date: 2022
• Subjects: Underwater Vehicle -- Adaptive Control -- Deep Learning -- Lyapunov Stability; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2022.10.401
• Languages: English
• ISSNs: 2405-8963

Abstract

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.