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 language | English |
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Pages (from-to) | 7-12 |
Number of pages | 6 |
Journal | IFAC-PapersOnLine |
Volume | 55 |
Issue number | 31 |
DOIs | |
Publication status | Published - 1 Oct 2022 |
Event | 14th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles, CAMS 2022 - Kongens Lyngby, Germany Duration: 14 Sept 2022 → 16 Sept 2022 |
Keywords
- Adaptive Control
- Deep Learning
- Lyapunov Stability
- Underwater Vehicle