A Data-Driven Autopilot for Fixed-Wing Aircraft Based on Model Predictive Control

In particular, PCAC is implemented as a cold-start A fundamental necessity for autonomous atmospheric indirect adaptive controller, where the plant model order is flight vehicles is a reliable autopilot for controlling the specified as a hyperparameter, but otherwise no plant model attitude and flight path. For a fixed-wing vehicle, stability and is assumed to be available. The identified model updated control derivatives are typically determined through windtunnel by RLS is linear, and thus it is suitable for modeling the testing or computational modeling over a range of aircraft dynamics near trim. In practice, an autopilot designed Mach number, angle of attack, and sideslip angle. This to operate over a wide range of flight conditions depends modeling data is then used to develop an autopilot based on on gain scheduling of multiple linear controllers. The goal gain scheduling, feedback linearization, or dynamic inversion of this study is to investigate, via numerical experiments, [1], [2]. In practice, however, the aerodynamics of an aircraft the viability and potential performance of PCAC under may be too expensive to model with high accuracy or may conditions of high uncertainty, in effect, no prior modeling change due to atmospheric conditions, such as icing, as well information, without the need for gain scheduling.