Uncertain System

Uncertain systems research focuses on designing controllers and decision-making strategies for systems with incompletely known dynamics or parameters. Current efforts concentrate on developing robust and adaptive control methods, often employing neural networks, Gaussian processes, or sum-of-squares programming to handle uncertainties and guarantee stability and safety. These advancements are crucial for improving the reliability and performance of autonomous systems in various applications, from robotics and aerospace to process control and healthcare, where model inaccuracies are unavoidable. The field is actively exploring techniques to balance performance optimization with rigorous stability and safety guarantees under uncertainty.