Professor Ferrari's research aims at providing intelligent control systems with a higher degree of mathematical structure to guide their application and improve reliability. Decision-making processes are automated based on concepts drawn from control theory and the life sciences. Recent efforts have focused on the development of reconfigurable controllers implementing neural networks with procedural long-term memories. Full-scale simulations show that these controllers are capable of learning from new and unmodeled aircraft dynamics in real time, improving performance and even preventing loss of control in the event of control failures, nonlinear and near-stall dynamics, and parameter variations. New optimal control problems and methods based on computational geometry are being investigated to improve the effectiveness of integrated surveillance systems by networks of autonomous vehicles, such as, underwater gliders and ground robots.