Neural network-based nonlinear dynamic inversion control of variable-span morphing aircraft

Abstract

A nonlinear command and stability augmentation system is designed based on feedback linearization for a morphing aircraft in the presence of different variable-span configurations. A variable-span morphing aircraft model is obtained by modifying a conventional aircraft model. Feed-forward neural networks are trained to learn the inverse dynamics in various morphing configurations and flight conditions. An attitude orientation system is designed to stabilize the airframe and track the commanded angular rates. Finally, an online adaptive mechanism is employed to compensate for inversion error. Even when the aircraft is changing the morphing configuration while maneuvering, the proposed design satisfies the desired handling quality specifications. Consequentially, the proposed design provides another degree of freedom to manipulate the morphing configuration, and therefore agility or fuel efficiency can be improved. Numerical simulation is performed to demonstrate the effectiveness of the proposed scheme.

Flight dynamics modeling and stability analysis of a tube-launched tandem wing aerial vehicle during the deploying process

“…In order to solve the multibody dynamic equations expressed as equations ( 9)- (11), additional relationship between the velocity vector and the angle of attack, α, as well as the sideslip angle, β, should be added as…”

“…Morphing aircraft can change its force and moment characteristics significantly and quickly to augment the flight performance and combat effect, which makes it adaptive to multiple missions and objectives. [5][6][7] Various wing morphing types, such as composite bendable-wing, 8 flexible membrane wing, 3 inflatable wing, 9 telescopic wing, 10,11 sweeping wing, 12,13 and folding wing, 14 have been proposed and studied in recent years.…”

A Generative Verification Framework on Statistical Stability for Data-Driven Controllers

“…In the case of linear systems, proportional-integral-derivative (PID) controllers can be designed for transfer function models. For nonlinear systems, various types of nonlinear controllers such as sliding mode control (SMC) [10], feedback linearization (FL) [11], and backstepping control [12] can be designed and applied depending on the characteristics of the control system [13]. However, controllers designed using conventional controller design methodologies have a limitation in that they must be designed to have a specific structure to ensure stability and optimality from a mathematical perspective.…”