One of the challenges with controlling autonomous aircraft is the coupling between the outer- and inner-loop control blocks, which becomes particularly pronounced when the aircraft executes aggressive maneuvers or experiences adverse onboard conditions such as motor or servo failures. The coupling between the outer- and inner-loop control blocks can cause phase shifts, sustained oscillations, and even lead to loss of control (LoC). To enhance safety and reliability in autonomous aircraft, a unified reinforcement learning (URL) longitudinal control framework has been developed for fixed-wing autonomous aircraft. This strategy replaces the cascaded outer- and inner-loop control blocks with a single inner-outer loop, which calculates the desired control commands (guidance) and executes them (control) in a single step. The structural design of URL enables the integration of fixed-wing aircraft dynamic constraints, such as stall angle of attack and maximum acceleration, as well as physical control constraints, like maximum control surface deflections. These constraints are often challenging to implement in many existing modern control methods. The URL controller robustness to modeling uncertainty is improved using a randomized training environment based on two different LTI models: a physics-based model and a model developed using the derivative-free cross-entropy (CEM) optimization algorithm and flight test data. The validation flight tests demonstrated the URL flight controller's superior performance in real-world environments with low to moderate wind conditions.
A Unified Inner-Outer Loop Reinforcement Learning Flight Controller for Fixed-Wing Aircraft
04.06.2024
3674084 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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