Robust Controller

Robust control aims to design controllers that maintain stability and performance despite uncertainties in system models and external disturbances. Current research emphasizes developing controllers that are resilient to impacts, handle input saturation, and adapt to unknown dynamics, often employing neural networks, adaptive control techniques (like L1 adaptive control and RISE), and model predictive control (MPC) with various robustness enhancements (e.g., tube-based MPC, distributionally robust MPC). These advancements are crucial for improving the reliability and safety of autonomous systems across diverse applications, from robotics and autonomous vehicles to industrial automation and aerospace.