Review of iterative learning control for super-maneuver flight

Super-maneuver flight represented by Pugachev's cobra maneuver, as a key measure of the maneuverability of aircraft, is practically significant for getting rid of the enemy's attack and changing the battlefield situation in close-range air combat. However, the high angle of attack and post-stall in supermaneuver flight generally lead to the serious shortage of wing aerodynamic efficiency, the drastic increase of external disturbances, and the rapid change of aerodynamic characteristics. It brings great challenges to the design and test of the controller. Realizing the complete tracking of the predetermined trajectory in a finite duration becomes the main goal of controller design. The characteristics and control difficulties of supermaneuver flight are analyzed and summarized. The research progress of iterative learning control (ILC) is introduced. The practical problems and solutions of ILC applied to super-maneuver flight, such as nonrepetitive uncertainties, initial state errors, learning constraints, and model-free problems, are summarized in detail. The application prospect and research direction of ILC in super-maneuver flight are prospected. ILC solutions are presented for super-maneuver with limited duration, high angle of attack, and post-stall in the consideration of safety, effectiveness, and robustness. The provided ILC solutions may provide the theoretical basis and engineering reference for the development of super-maneuver flight.