Lyapunov-Based Deep Learning for Safe and Reliable Autonomy скачать в хорошем качестве

Lyapunov-Based Deep Learning for Safe and Reliable Autonomy

Modern autonomous systems increasingly rely on deep learning for perception, prediction, and control, yet ensuring safety and reliability remains a fundamental challenge. This talk presents a principled framework that integrates Lyapunov stability theory with deep neural networks to provide formal guarantees of convergence, robustness, and safety during online learning and control. By shaping adaptation dynamics through Lyapunov functions, analogous to enforcing dissipation in physical systems, learning evolves according to stability and passivity principles rather than unconstrained gradient updates. This energy-consistent perspective unifies deep learning and adaptive control, enabling real-time system identification and control with provable stability under uncertainty. The resulting framework supports structure-preserving autonomy in complex robotic environments, bridging physics-based modeling and data-driven learning.