Risk Aware Control

Risk-aware control focuses on designing controllers for systems operating under uncertainty, prioritizing safety and constraint satisfaction alongside performance optimization. Current research emphasizes probabilistic methods, such as particle filters and Kalman filters, to model uncertainty, coupled with algorithms like control barrier functions and risk-sensitive reinforcement learning to synthesize safe and efficient controllers. This field is crucial for deploying autonomous systems in safety-critical applications, including robotics, autonomous driving, and smart grids, by providing frameworks to manage and mitigate risks arising from unpredictable environments and potential failures.