In this article, a modified Steiglitz–McBride (SM) structure with variable step size is proposed for aircraft feedforward control to reduce lateral landing deviation in the presence of crosswind disturbance. Currently, most of the aircraft landing studies focus on the trajectory tracking problems in the longitudinal plane and seldom focus on lateral disturbances. This article develops a modified SM structure–based feedforward control system to online estimate both the primary path and the control path (secondary path), where a fuzzy logic–based variable step size strategy is implemented to ensure fast convergence rate and strong robustness under complex crosswind scenarios. As a consequence, the lateral deviation during crosswind landing could be largely reduced as fast as possible while the stability of the flight control system is maintained. A Boeing 747 model is used as a test bed, and the simulations are carried out on two different crosswind conditions to demonstrate the feasibility and effectiveness of the proposed method.
OkamotoKTsuchiyaT. Optimal aircraft control in stochastic severe weather conditions. J Guidance, Control Dyn2016; 39(1): 77–85.
2.
LiTYangHTianJ, et al.. Improved disturbance rejection control based on H∞ synthesis and equivalent-input-disturbance for aircraft longitudinal autopilot design. Proc Inst Mech Eng G: J Aerospace Eng2019; 233(9): 3323–3335.
3.
BianQNenerBLiT, et al.. Multimodal control parameter optimization for aircraft longitudinal automatic landing via the hybrid particle swarm-BFGS algorithm. Proc Inst Mech Eng Part G: J Aerospace Eng2019; 233(12): 4482–4491.
4.
HuangBLuBLiQ. A proportional-integral-based robust state-feedback control method for linear parameter-varying systems and its application to aircraft. Proc Inst Mech Eng Part G: J Aerospace Eng2019; 233(12): 4663–4675.
5.
BianQNenerBWangX. An improved NSGA-II based control allocation optimisation for aircraft longitudinal automatic landing system. Int J Control2019; 92(4): 705–716.
6.
WangLZhangZZhuQ, et al.Longitudinal automatic carrier landing system guidance law using model predictive control with an additional landing risk term. Proc Inst Mech Eng Part G: J Aerospace Eng2019; 233(3): 1089–1105.
7.
ZhenZTaoGYuC, et al.A multivariable adaptive control scheme for automatic carrier landing of UAV. Aerospace Sci Technology2019; 92: 714–721.
De BruinAJonesT. Accurate autonomous landing of a fixed-wing unmanned aircraft under crosswind conditions. IFAC-PapersOnLine2016; 49(17): 170–175.
10.
TheisJOssmannDThieleckeF, et al.Robust autopilot design for landing a large civil aircraft in crosswind. Control Eng Pract2018; 76: 54–64.
11.
WangKLiuGHongG. Lateral-Directional Simulation of F/A-18 Aircraft Landing on a Carrier in Complex Environment. In: AIAA Modeling and simulation technologies conference, WA, USA, 13-17 June 2016, pp. 3528.
12.
ChoiHSLeeSLeeJ, et al.Development of an aircraft auto-landing guidance system using time delay control, and simulation with crosswind and aileron fault. Trans Jpn Soc Aeronaut Space Sci2015; 58(1): 23–29.
13.
BianQNenerBWangX. A quantum inspired genetic algorithm for multimodal optimization of wind disturbance alleviation flight control system. Chin J Aeronautics2019; 32(11): 2480–2488.
14.
LeeHSnyderSHovakimyanN. L1 adaptive control within a flight envelope protection system. J Guidance, Control Dyn2017; 40(4): 1013–1026.
15.
LunguM. Backstepping and dynamic inversion combined controller for auto-landing of fixed wing UAVs. Aerospace Sci Technology2020; 96: 105526.
16.
LunguRLunguM. Automatic landing control using H-inf control and dynamic inversion. Proc Inst Mech Eng Part G: J Aerospace Eng2014; 228(14): 2612–2626.
17.
XieLQiuZ-CZhangX-M. Analysis and experiments of adaptive feedforward and combined vibration control system with variable step size and reference filter. Aerospace Sci Technology2017; 61: 109–120.
18.
MizumotoIFujiiSMitaH. Output feedback-based output tracking control with adaptive output predictive feedforward for multiple-input-multiple-output systems. Ind Eng Chem Res2019; 58(26): 11382–11391.
19.
HankeCR. The simulation of a large jet transport aircraft. Volume 1: Mathematical model. Chicago, IL, USA: Boeing Company1971. Tech Rep NASA CR-, 1756.
20.
HankeCRNordwallDR. The simulation of a jumbo jet transport aircraft. volume 2 modeling data. IL, USA: Boeing Company, 1970. Tech Rep D6 -30643
21.
BianQNenerBWangX. Control parameter tuning for aircraft crosswind landing via multi-solution particle swarm optimization. Eng Optimization2018; 50(11): 1914–1925.
22.
HoblitFM. Gust loads on aircraft: concepts and applications. DC, USA: The American Institute of Aeronautics and Astronautics, 1988.
23.
BianQNenerBWangX. A modified nsga-ii for solving control allocation optimization problem in lateral flight control system for large aircraft. IEEE Access2019; 7: 17696–17704.
24.
FeiJFangY. Robust adaptive feedforward vibration control for: flexible structure. Int J Innovative Comput Inf Control2010; 6(5): 2189–2197.
25.
KodhandaAKolheJPZeruT, et al.Robust aircraft control based on UDE theory. Proc Inst Mech Eng Part G: J Aerospace Eng2017; 231(4): 728–742.
26.
Sadat-HoseiniHFazelzadehSARastiA, et al.Final approach and flare control of a flexible aircraft in crosswind landings. J Guidance, Control Dyn2013; 36(4): 946–957.
27.
HongWJMaDL. Influence of control coupling effect on landing performance of flying wing aircraft. Appl Mech Mater2016; 829: 110–117.
28.
BianQNenerBWangX. A modified bacterial-foraging tuning algorithm for multimodal optimization of the flight control system. Aerospace Sci Technology2019; 93: 105274.
29.
GeYSongBPeiY, et al.A fuzzy logic based method for fault tolerant hierarchical load management of more electric aircraft. Proc Inst Mech Eng Part G: J Aerospace Eng2019; 233(10): 3846–3856.
30.
NishadAKBharatiSKSinghSR. A new centroid method of ranking for intuitionistic fuzzy numbers. In: Proceedings of the second international conference on soft computing for problem solving, New Delhi, 28–30 December 2012, pp. 151–159. SocProS.
31.
LjungL. Analysis of recursive stochastic algorithms. IEEE Trans automatic Control1977; 22(4): 551–575.
32.
CainesPE. Linear stochastic systems, Vol. 11. NY, USA: Wiley, 1988.
33.
WidrowB. In: KalmanREDeClarisN.,HoltRinehard (eds). Adaptive filters, a chapter from aspects of network and systems theory. NY, USA: Winston. Inc., 1970.
34.
HansenCSnyderSQiuX, et al.Active control of noise and vibration. 2nd ed. Boca Raton, Florida: CRC Press, 2012.
