Data-driven Robust Control Design: Unfalsified Control

Feedback control systems for aerospace applications must maintain precise control despite uncertain operating conditions and unanticipated circumstances such as battle damage. These systems must be designed to perform robustly, despite uncertain design models and difficult to analyze nonlinear effects. They must also be capable of learning and adapting when accumulating data indicates that previous models must be abandoned and that existing control strategies must be changed. We present recent developments that address the need for data-driven design methods well suited to situations in which available mathematical models are poor or unreliable. These innovative data-driven design methods, collectively known as unfalsified control theory, facilitate the creation of robust control systems that learn, discover and evolve in real time in order to rapidly and reliably compensate for the effects of battle damage, equipment failures and other changing circumstances. Potential applications include aircraft stability augmentation systems, highly maneuverable aircraft design, missile guidance systems, and precision pointing and tracking systems. “It is a capital mistake to theorise before one has data. Insensibly one begins to twist facts to suit theories instead of theories to suit facts. ” Arthur Conan Doyle, Sherlock Holmes