Adaptive model inversion flight control for tilt-rotor aircraft

Neural-network augmented model inversion control is used to provide a civilian tilt-rotor aircraft with consistent response characteristics throughout its operating envelope, including conversion flight. The implemented response type is Attitude Command Attitude Hold in the longitudinal channel. Conventional methods require extensive gain scheduling with tilt-rotor nacelle angle and speed. A control architecture that can alleviate this requirement, and thus has the potential to reduce development time and cost, is developed. This architecture also facilitates the implementation of desired handling qualities and permits compensation for partial failures. One of the powerful aspects of the controller architecture is the accommodation of uncertainty in control as well as in the states. It includes an online, i.e., learning-while-controlling, neural network that is initialized with all weights equal to zero. Lyapunov analysis guarantees the boundedness of tracking errors and network parameters. Performance of the controller is demonstrated using a nonlinear generic tilt-rotor simulation code.