Novel Control

Novel control research focuses on developing advanced algorithms and architectures to improve the performance and safety of robotic and autonomous systems in complex, dynamic environments. Current efforts concentrate on incorporating safety constraints using barrier functions and Lyapunov methods, leveraging efficient model architectures like neural networks and dual quaternions for improved computational performance, and employing techniques such as model predictive control and iterative LQR for trajectory optimization and tracking. These advancements are significant for enhancing the reliability and capabilities of robots in diverse applications, from aerial manipulation and human-robot collaboration to multi-robot coordination and autonomous navigation.