Stable Control

Stable control research focuses on designing and verifying control systems that reliably maintain desired system behavior, even under uncertainty or disturbances. Current efforts concentrate on developing robust algorithms, such as model predictive control (MPC) with explicit approximations and Lyapunov-based methods incorporating neural networks, to guarantee stability and safety, particularly in complex systems like autonomous vehicles and robots. These advancements are crucial for deploying safe and reliable autonomous systems in various applications, ranging from transportation to neurorobotics, by addressing challenges like computational complexity, robustness to sensor noise, and the avoidance of unstable control policies learned through reinforcement learning.