基于知识引导智能鸽群优化的舰载机着舰控制

doi:10.7527/S1000-6893.2024.30801

Abstract

Abstract:

Aiming at the problem of automatic landing of carrier-based aircraft under deck motion and airflow disturbance， a longitudinal landing control law design method based on dynamic inverse control is proposed. The carrier-based aircraft model， deck motion model and stern flow disturbance model are established， and the dynamic inverse control method of carrier-based aircraft landing is designed to estimate the first-order derivative of the reference command within a finite time. An knowledge-based intelligent pigeon-inspired optimization algorithm is proposed to iteratively optimize the parameters of the dynamic inverse controller， which can improve the command following accuracy of carrier-based aircraft landing. The MATLAB Simulink simulation environment is used to simulate and verify the proposed automatic landing control system of carrier-based aircraft. By comparing with pigeon-inspired optimization algorithm and particle swarm optimization algorithm， it is shown that the superiority of the carrier-based aircraft landing control method of knowledge-based intelligent pigeon-inspired optimization， which can significantly improve the accuracy and speed of carrier-based aircraft attitude control， meet the requirements of carrier-based aircraft landing， and has good robustness.

Key words: carrier-based aircraft landing, dynamic inverse control, intelligent optimization algorithm, knowledge-based, parameter optimization, controllers

CLC Number:

V249.1

Dapeng ZHOU, Xiaolei QU. Knowledge-based intelligent pigeon-inspired optimization of carrier-based aircraft landing control[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(S1): 730801.

Figures/Tables 11

Fig.1

Fig.2

Fig.3

Table 1

Initial trim states of carrier-based aircrafts

舰载机状态量	数值
$V$ /（m·s^-1）	$70$
$χ / r a d$	$0$
$γ / r a d$	$0$
$μ / r a d$	$0$
$α$ $/ r a d$	$7.84 π / 180$
$β / r a d$	$0$
$θ / r a d$	$7.84 π / 180$
$ψ$ $/ r a d$	$0$
$ϕ / r a d$	$0$
$p$ /（rad·s^-1）	$0$
$q$ /（rad·s^-1）	$0$
$r$ /（rad·s^-1）	$0$
$T$ /N	$23 082.8$
$δ e$ /（rad·s^-1）	$0$
$δ a$ /（rad·s^-1）	$0$
$δ r$ /（rad·s^-1）	$0$

Table 1

Table 2

Parameter settings of intelligent optimization algorithms

算法	参数	数值
PIO、PSO、KPIO	$N$	$30$
PIO、PSO、KPIO	$[X m i n, X m a x]$	$[1.5,2.5]$
PIO	$R$	$0.2$
	$N c 1 m a x$	$100$
	$N c 2 m a x$	$50$
PSO	$w$	$1.2$
	$c 1$	$2$
	$c 2$	$2$

Table 2

Table 3

Parameter values of optimal solutions

算法	最优解参数值	代价函数值
PIO	$2.334 94,1.802 23,1.793 90$	41.479 8
PSO	$1.499 66,1.687 78,1.556 99$	41.446 3
KPIO	$1.917 33,2.240 10,1.965 47$	41.440 3

Table 3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

References 21

1	ZHEN Z Y， JIANG S Y， JIANG J. Preview control and particle filtering for automatic carrier landing［J］. IEEE Transactions on Aerospace and Electronic Systems， 2018， 54（6）： 2662-2674.
2	ASTOLFI A， KARAGIANNIS D， ORTEGA R. Towards applied nonlinear adaptive control［J］. Annual Reviews in Control， 2008， 32（2）： 136-148.
3	SINGH S， PADHI R. Automatic path planning and control design for autonomous landing of UAVs using dynamic inversion［C］‍∥2009 American Control Conference. Piscataway： IEEE Press， 2009： 2409-2414.
4	ZHENG Z W， JIN Z H， SUN L， et al. Adaptive sliding mode relative motion control for autonomous carrier landing of fixed-wing unmanned aerial vehicles［J］. IEEE Access， 2017， 5： 5556-5565.
5	SUBRAHMANYAM M B. H-infinity design of F/A-18A automatic carrier landing system［J］. Journal of Guidance， Control， and Dynamics， 1994， 17（1）： 187-191.
6	LUNGU M H， LUNGU R. Automatic control of aircraft lateral-directional motion during landing using neural networks and radio-technical subsystems［J］. Neurocomputing， 2016， 171： 471-481.
7	WANG X， CHEN X， WEN L Y. Adaptive disturbance rejection control for automatic carrier landing system［J］. Mathematical Problems in Engineering， 2016， 2016： 7345056.
8	LUNGU M H. Auto-landing of fixed wing unmanned aerial vehicles using the backstepping control［J］. ISA Transactions， 2019， 95： 194-210.
9	KOO S， KIM S， SUK J， et al. Improvement of shipboard landing performance of fixed-wing UAV using model predictive control［J］. International Journal of Control， Automation and Systems， 2018， 16（6）： 2697-2708.
10	LUNGU M H. Auto-landing of UAVs with variable centre of mass using the backstepping and dynamic inversion control［J］. Aerospace Science and Technology， 2020， 103： 105912.
11	ZHEN Z Y， JIANG S Y， MA K. Automatic carrier landing control for unmanned aerial vehicles based on preview control and particle filtering［J］. Aerospace Science and Technology， 2018， 81： 99-107.
12	DUAN H B， HUO M Z， YANG Z Y， et al. Predator-prey pigeon-inspired optimization for UAV ALS longitudinal parameters tuning［J］. IEEE Transactions on Aerospace and Electronic Systems， 2019， 55（5）： 2347-2358.
13	YANG Z Y， DUAN H B， FAN Y M， et al. Automatic carrier landing system multilayer parameter design based on Cauchy mutation pigeon-inspired optimization［J］. Aerospace Science and Technology， 2018， 79： 518-530.
14	DENG Y M， DUAN H B. Control parameter design for automatic carrier landing system via pigeon-inspired optimization［J］. Nonlinear Dynamics， 2016， 85（1）： 97-106.
15	DUAN H B， QIAO P X. Pigeon-inspired optimization： A new swarm intelligence optimizer for air robot path planning［J］. International Journal of Intelligent Computing and Cybernetics， 2014， 7（1）： 24-37.
16	XU X B， DENG Y M. UAV power component-DC brushless motor design with merging adjacent-disturbances and integrated-dispatching pigeon-inspired optimization［J］. IEEE Transactions on Magnetics， 2018， 54（8）： 7402307.
17	ZHANG B， DUAN H B. Three-dimensional path planning for uninhabited combat aerial vehicle based on predator-prey pigeon-inspired optimization in dynamic environment［J］. IEEE/ACM Transactions on Computational Biology and Bioinformatics， 2017， 14（1）： 97-107.
18	DUAN H B， WANG X H. Echo state networks with orthogonal pigeon-inspired optimization for image restoration［J］. IEEE Transactions on Neural Networks and Learning Systems， 2016， 27（11）： 2413-2425.
19	DUAN H B， HUO M Z， SHI Y H. Limit-cycle-based mutant multiobjective pigeon-inspired optimization［J］. IEEE Transactions on Evolutionary Computation， 2020， 24（5）： 948-959.
20	DUAN H B， CHEN L， ZENG Z G. Automatic landing for carrier-based aircraft under the conditions of deck motion and carrier airwake disturbances［J］. IEEE Transactions on Aerospace and Electronic Systems， 2022， 58（6）： 5276-5291.
21	DUAN H B， YUAN Y， ZENG Z G. Automatic carrier landing system with fixed time control［J］. IEEE Transactions on Aerospace and Electronic Systems， 2022， 58（4）： 3586-3600.

[1]	Yu LI, Tongwen CHEN, Zhigang WANG, Chiyung WEN, Xiaoxiong LIU. Incremental control of direct lift landing based on predefined-time theory [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(13): 531163-531163.
[2]	Xinze XU, Guanxin HONG, Liang DU, Gang LIU. Manual approach and landing model of carrier-based aircraft in complex environments [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(13): 531802-531802.
[3]	Jiaxing WANG, Hao CHEN, Zheng SHAO, Yang ZHANG. Direct lift landing control method based on flight path angle command [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(13): 532162-532162.
[4]	Guanghan XIAO, Zeyan HU, Junhu LIU, Liang WANG, Qingtang MAO. A design method of even-fold continuous-coverage constellation for space targets [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(14): 229637-229637.
[5]	Shuaiwen TANG, You CAO, Peng ZHANG, Jiang JIANG. Health assessment of LIMU based on evidential reasoning rule with dependent evidence [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(12): 329453-329453.
[6]	Kaiming ZHANG, Kelu WANG, Shiqiang LU, Mutong LIU, Ping ZHONG, Ye TIAN. Thermal deformation behavior of S280 ultra-high strength stainless steel based on response surface methodology [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(8): 427293-427293.
[7]	Baohui JIA, Fan JIANG, Yuxin WANG, Du WANG. Fault diagnosis method based on civil aircraft maintenance text data [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(5): 326598-326598.
[8]	Yuhang XIA, Yu WANG. Comparative study on optimal control methods of independent excitation DC power generation system [J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(16): 327975-327975.
[9]	CAO Yi, MENG Gang, JU Yongjian, XU Weisheng. Large-stroke compliant micro-positioning stage considering parasitic rotation [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(7): 425498-425498.
[10]	WU Baohai, ZHANG Yang, ZHENG Zhiyang, ZHANG Ying, ZHANG Siqi. Review and prospects of feedrate optimization in CNC machining [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(4): 525467-525467.
[11]	WANG Bin, WANG Haitao, WANG Yufeng, ZHANG Wenwu. Water-assisted laser scanning machining test of thermal barrier coating [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(4): 525353-525353.
[12]	GUO Lei, GAO Yuan, XIN Hui. Laser modification parameters optimization and structural design of thermal barrier coatings [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(7): 424114-424114.
[13]	ZOU Shiyu, LI Fuming, XIE Aiping, ZHOU Tao, LIU Peng. Resource allocation based on improved fireworks algorithm [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(12): 324716-324716.
[14]	WU Aiguo, GONG Zhihao. Optimization of aerocapture orbit based on improved pigeon inspired optimization algorithms [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(9): 324292-324292.
[15]	LIU Yiming, SHENG Wen, HU Bing, ZHANG Lei. Long time tracking beam scheduling and waveform optimization strategy for phased array radar [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(3): 323519-323519.

Knowledge-based intelligent pigeon-inspired optimization of carrier-based aircraft landing control

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 11

References 21

Related Articles 15

Recommended Articles

Metrics

Comments