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.
WickenheiserAMGarciaE. Aerodynamic modeling of morphing wings using an extended lifting-line analysis. J Aircraft2007; 44: 10–16.
2.
ReichGSandersB. Introduction to morphing aircraft research. J Aircraft2007; 44: 1059–1059.
3.
BarbarinoSBilgenOAjajRM, et al.A review of morphing aircraft. J Intell Mater Syst Struct2011; 22: 823–877.
4.
ValasekJ. Morphing aerospace vehicles and structures, Chichester: Wiley, 2012.
5.
Seigler TM, Bae JS and Inman DJ. Flight control of a variable span cruise missile. In: ASME international mechanical engineering congress and exposition, Anaheim, CA, USA, 13–20 November 2004, paper no. IMECE2004-61961, pp. 565–574. New York: ASME.
6.
Bae JS, Seigler TM, Inman DJ, et al. Aerodynamic and aeroelastic considerations of a variable- span morphing wing. In: AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics & materials conference, Palm Springs, CA, USA, 19–22 April 2004, paper no. AIAA 2004-1726.
7.
BaeJSSeiglerTMInmanDJ. Aerodynamic and static aeroelastic characteristics of a variable-span morphing wing. J Aircraft2005; 42: 528–534.
8.
Joshi SP, Tidwell Z, Crossley WA, et al. Comparison of morphing wing strategies based upon aircraft performance impacts. In: AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics & materials conference, Palm Springs, CA, USA, 19–22 April 2004, paper no. AIAA 2004-1722.
9.
SeiglerTMNealDABaeJ, et al.Modeling and flight control of large-scale morphing aircraft. J Aircraft2007; 44: 1077–1087.
10.
TongLJiH. Multi-body dynamic modelling and flight control for an asymmetric variable sweep morphing UAV. Aeronaut J2014; 118: 683–706.
11.
Boothe K, Fitzpatric K and Lind R. Controllers for disturbance rejection for a linear input-varying class of morphing aircraft. In: AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics & materials conference, Austin, TX, USA, 18–21 April 2005, paper no. AIAA 2005-2374. Reston, VA: AIAA.
12.
YueTWangLAiJ. Gain self-scheduled H control for morphing aircraft in the wing transition process based on an LPV model. Chinese J Aeronaut2013; 26: 909–917.
13.
YueTWangLAiJ. Longitudinal linear parameter varying modeling and simulation of morphing aircraft. J Aircraft2013; 50: 1673–1681.
14.
JiangWDongCWangQ. A systematic method of smooth switching LPV controllers design for a morphing aircraft. Chinese J Aeronaut2015; 28: 1640–1649.
15.
HeZYinMping LuY. Tensor product model-based control of morphing aircraft in transition process. Proc IMechE, Part G: J Aerospace Engineering2016; 230: 378–391.
16.
WenNLiuZSunY, et al.Design of LPV-based sliding mode controller with finite time convergence for a morphing aircraft. Int J Aerosp Eng2017; 2017: 1–20.
17.
WenNLiuZZhuL. Linear-parameter-varying-based adaptive sliding mode control with bounded L2 gain performance for a morphing aircraft. Proc IMechE, Part G: J Aerospace Engineering. 2019; 233: 1847–1864.
18.
ValasekJTandaleMDRongJ. A reinforcement learning - adaptive control architecture for morphing. J Aerosp Comput Inf Commun2005; 2: 174–195.
19.
ValasekJDoebblerJTandaleMD, et al.Improved adaptive-reinforcement learning control for morphing unmanned air vehicles. IEEE Trans Syst Man Cybern Part B-Cybern2008; 38: 1014–1020.
20.
Valasek J, Lampton A and Marwaha M. Morphing unmanned air vehicle intelligent shape and flight control. In: AIAA Infotech@Aerospace conference, Seattle, WA, USA, 6–9 April 2009, paper no. AIAA 2009-1827. Reston, VA: AIAA.
21.
WuZLuJRajputJ, et al.Adaptive neural control based on high order integral chained differentiator for morphing aircraft. Math Probl Eng2015; 2015: 1–12.
22.
WuZLuJZhouQ, et al.Modified adaptive neural dynamic surface control for morphing aircraft with input and output constraints. Nonlinear Dyn2017; 87: 2367–2383.
23.
Menon PKA. Nonlinear command augmentation system for a high performance aircraft. In: AIAA guidance, navigation, and control conference, Monterey, CA, USA, 9–11 August 1993, paper no. AIAA 1993-3777. Reston, VA: AIAA.
24.
Kim BS. Nonlinear flight control using neural networks. PhD Thesis, Georgia Institute of Technology, USA, 1993.
25.
KimBSCaliseAJ. Nonlinear flight control using neural networks. J Guid Control Dyn1997; 20: 26–33.
