Uncertain Dynamic

Uncertain dynamic systems, characterized by incompletely known or time-varying parameters and disturbances, pose significant challenges for control design. Current research focuses on developing robust control strategies that guarantee stability and performance despite these uncertainties, employing techniques like adaptive control, reinforcement learning (with actor-critic methods and Gaussian processes), and model predictive control with neural network models. These advancements are crucial for deploying autonomous systems in safety-critical applications, such as robotics, aerospace, and autonomous driving, where precise modeling is often infeasible. The ultimate goal is to create controllers that are both effective and provably safe in the face of unpredictable system behavior.