Neural network-based nonlinear dynamic inversion control of variable-span morphing aircraft. (August 2020)

• Record Type: Journal Article
• Authors: Lee, Jihoon; Kim, Youdan
• Other Names: Choukroun Daniel guest-editor.
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 234:Number 10(2020)
• Page Start: 1624
• Page End: 1637
• Publication Date: 2020-08
• Subjects: Morphing aircraft -- nonlinear dynamic inversion control -- neural network -- intelligent control -- adaptive control; Aeronautics -- Periodicals; Astronautics -- Periodicals; Airplanes -- Design and construction -- Periodicals; Aerospace industries -- Periodicals; 629.1
• DOI: 10.1177/0954410019846713
• Languages: English
• ISSNs: 0954-4100

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.