Data-driven adaptive dynamic programming control for gust response of camber morphing wing

Abstract

This paper presents a data-driven adaptive dynamic programming (ADP) control law for gust response alleviation in active camber morphing wings. The controller is synthesized using input-output data generated from a reduced-order aeroelastic model, which is validated across a broad operational envelope, including inflow velocities V = 15-25 m/s, sinusoidal gusts with frequency f_g = 1-3 Hz, and gust amplitude A_g = 2 m/s. Simulation results demonstrate that the ADP controller adaptively tunes its parameters to achieve a peak alleviation efficiency of 89.6% at f_g = 2.4 Hz. Moreover, the control law also performs well in V = 20 m/s, a sinusoidal gust of f_g = 2 Hz, A_g = 2 m/s with 10 dB Gaussian white noise and “1-cos” discrete gust, respectively. Compared to the GPC-based (Generalized Predictive Control, GPC) controller, the proposed ADP controller achieves superior alleviation performance with significantly reduced training data requirements. Furthermore, a fluid-structure-control interaction (FSCI) framework is developed based on the high-fidelity computational fluid dynamics (CFD) simulation embedded with ADP control to investigate the alleviation mechanism under sinusoidal gusts with large gust ratios (GRs). The mechanism reveals that the ADP controller modulates trailing-edge morphing to generate a counter-acting pressure zone and inhibit the formation of the leading-edge vortex (LEV). This dual action directly suppresses the sources of unsteady aerodynamic forces, thereby reducing the generalized forces and effectively attenuating the structural response.

Keywords

gust response data-driven adaptive dynamic programming control law camber morphing fluid-structure-control interaction

Get full access to this article

View all access options for this article.

References

Karpel

Moulin

Chen

. Dynamic response of aeroservoelastic systems to gust excitation. Journal of Aircraft 2005; 42(5): 1264–1272. https://doi.org/10.2514/1.6678

Herrmann

Brunton

Pohl

, et al. Gust mitigation through closed-loop control. II. Feedforward and feedback control. Physical Review Fluids 2022; 7(2): 024706. https://doi.org/10.1103/physrevfluids.7.024706

Ning

. A review of flow control for gust load alleviation. Applied Sciences 2022; 12(20): 10537. https://doi.org/10.3390/app122010537

Wang

Mkhoyan

, et al. Seamless active morphing wing simultaneous gust and maneuver load alleviation. Journal of Guidance, Control, and Dynamics 2021; 44(9): 1649–1662. https://doi.org/10.2514/1.g005870

Ning

. Gust load alleviation by normal microjet. Aerospace Science and Technology 2021; 117: 106919. https://doi.org/10.1016/j.ast.2021.106919

Chen

Yang

. Study on gust alleviation control and wind tunnel test. Science China Technological Sciences 2013; 56: 762–771. https://doi.org/10.1007/s11431-013-5131-7

Zhao

Yue

. Gust load alleviation on a large transport airplane. J Aircr 2016; 53(6): 1932–1946. https://doi.org/10.2514/1.c033713

Zeng

Moulin

DE Callafon

, et al. Adaptive feedforward control for gust load alleviation. Journal of Guidance, Control, and Dynamics 2010; 33(3): 862–872. https://doi.org/10.2514/1.46091

Shao

Yang

, et al. Theoretical and experimental study of gust response alleviation using neuro-fuzzy control law for a flexible wing model. Chinese Journal of Aeronautics 2010; 23(3): 290–297.

10.

Shao

Yang

, et al. Design of a gust-response-alleviation online control system based on neuro-fuzzy theory. Journal of Aircraft 2013; 50(2): 599–609. https://doi.org/10.2514/1.c031959

11.

Gili

Ruotolo

Gili

, et al. A neural gust alleviator for a non-linear combat aircraft model. In: Guidance Navigation, and control conference, AIAA, 1997, p. 3761.

12.

Zhou

Yang

. Intelligent feed forward gust alleviation based on neural network. Chinese Journal of Aeronautics 2023; 37: 116–132.

13.

Dai

Yang

Wang

. Strategy for robust gust response alleviation of an aircraft model. Control Engineering Practice 2017; 60: 211–217. https://doi.org/10.1016/j.conengprac.2016.11.013

14.

Dai

Yang

. GPC-based gust response alleviation for aircraft model adapting to various flow velocities in the wind tunnel. Shock and Vibration 2015; 2015: 1–11. https://doi.org/10.1155/2015/348971

15.

Lewis

Vrabie

Vamvoudakis

. Reinforcement learning and feedback control: using natural decision methods to design optimal adaptive controllers. IEEE Control Systems Magazine 2012; 32(6): 76–105.

16.

Lian

Xue

Lewis

, et al. Integral and inverse reinforcement learning for optimal control systems and games. Springer, 2024.

17.

Kiumarsi

Lewis

Modares

, et al. Reinforcement Q-learning for optimal tracking control of linear discrete-time systems with unknown dynamics. Automatica 2014; 50(4): 1167–1175. https://doi.org/10.1016/j.automatica.2014.02.015

18.

Kiumarsi

Lewis

Naghibi-Sistani

, et al. Optimal tracking control of unknown discrete-time linear systems using input-output measured data. IEEE Transactions on Cybernetics 2015; 45(12): 2770–2779. https://doi.org/10.1109/TCYB.2014.2384016

19.

Chen

Zong

Zhang

, et al. Data-driven active vibration control for helicopter with trailing-edge flaps using adaptive dynamic programming. Chinese Journal of Aeronautics 2024; 37: 151–166.

20.

Moerland

Broekens

Plaat

, et al. Model-based reinforcement learning: a survey. Foundations and Trends® in Machine Learning 2023; 16(1): 1–118. https://doi.org/10.1561/2200000086

21.

Rivero

Fournier

Manolesos

, et al. Wind tunnel comparison of flapped and FishBAC camber variation for lift control. In: AIAA SCITECH 2020 Forum. AIAA, 2020, p. 1300.

22.

Vartio

Shaw

Vetter

. Gust load alleviation flight control system design for a sensorcraft vehicle. In: 26th AIAA applied aerodynamics conference. AIAA, 2008, p. 7192.

23.

Wong

Bil

Marino

. Design and aerodynamic performance of a FishBAC morphing wing. In: AIAA SCITECH 2022 Forum. AIAA, 2022, p. 1298.

24.

Nguyen

Cramer

Hashemi

, et al. Progress on gust load alleviation wind tunnel experiment and aeroservoelastic model validation for a flexible wing with variable camber continuous trailing edge flap system. In: AIAA SCITECH 2020 Forum. AIAA, 2020, p. 0214.

25.

Huang

Yang

Yao

, et al. Parameterized modeling methodology for efficient aeroservoelastic analysis of a morphing wing. AIAA Journal 2019; 57(12): 5543–5552. https://doi.org/10.2514/1.j058211

26.

Zou

Huang

. Body-freedom flutter suppression for a flexible flying-wing drone via time-delayed control. Journal of Guidance, Control and Dynamics 2022; 45(1): 28–38. https://doi.org/10.2514/1.g006114

27.

Huang

Zhao

. Wind-tunnel tests for active flutter control and closed-loop flutter identification. AIAA Journal 2016; 54(7): 2089–2099. https://doi.org/10.2514/1.j054649

28.

Zhang

Dai

Wei

, et al. Experimental study on dynamic response alleviation of a wing with variable camber trailing edge. Journal of Beijing University of Aeronautics and Astronautics. 2024; 50(10): 3239–3249. https://doi.org/10.13700/j.bh.1001-5965.2022.0761

29.

Wei

. Numerical study of wing gust response alleviation based on camber morphing trailing edge. Journal of Beijing University of Aeronautics and Astronautics 2023; 49(07): 1864–1874.

30.

Kostić

. Review of the Spalart-Allmaras turbulence model and its modifications to three-dimensional supersonic configurations. Scientific Technical Review 2015; 65(1): 43–49. https://doi.org/10.5937/str1501043k

31.

Dai

, et al. Time-domain feedforward control for gust response alleviation based on seamless morphing wing. AIAA Journal 2022; 60(10): 5707–5722. https://doi.org/10.2514/1.j061763

32.

Zeng

Dai

Yang

, et al. Effect of wingtip bending morphing on gust-induced aerodynamics based on fluid-structure interaction method. Physics of Fluids 2023; 35: 11.