Stable Dynamical System

Stable dynamical systems research focuses on developing mathematically guaranteed stable models for controlling complex systems, particularly robots, by learning from demonstrations or other data. Current efforts concentrate on learning nonlinear dynamical systems using polynomial representations, neural networks, and Riemannian geometry to handle complex state spaces and ensure stability through techniques like Lyapunov functions and barrier certificates. This work is crucial for improving the robustness and reliability of autonomous systems in real-world applications, such as robotics and control systems, by providing methods for learning safe and efficient control policies.