Control Affine

Control-affine systems, characterized by a control input linearly affecting the system dynamics, are a central focus in nonlinear control research. Current efforts concentrate on developing robust and safe control strategies for these systems, often employing model predictive control (MPC), control barrier functions (CBFs), and machine learning techniques like neural networks and random features to handle uncertainties and complex dynamics. This research is crucial for advancing autonomous systems, particularly in robotics and aerospace, where safe and efficient control in unpredictable environments is paramount. The development of provably safe and efficient control algorithms for control-affine systems directly impacts the reliability and performance of real-world applications.