Automatic Aircraft Landing System: A Review
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Abstract
The aircraft landing phase, marking the finishing of a flight plane, is a critical yet dangerous aerial maneuver. Even though it might seem simple, predicting the complexities of performance during landing presents a big challenge due to the dynamic characteristics of the phase, interaction with piloting methods, as well as inherent uncertainties in aerodynamics. Landing is executed in close proximity to the ground at reduced airspeed, the landing phase entails an escalated safety risk. Notably, incidents and accidents, particularly overruns where aircraft fail to slow sufficiently on the runway before landing, underscore the imperative for advanced landing technologies. This paper presents a comprehensive review of research spanning from 2000 to the present exploring smart techniques like fuzzy logic and machine learning, an array of control strategies ranging from classic PID control to sophisticated hybrid control approaches, and optimization procedures utilizing diverse optimization algorithms. The primary objective is to provide a comprehensive evaluation of the existing research landscape, offering insights that can propel the evolution of strategies for safer and more efficient landings and making sure the plane touches down safely.
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Abbas, N.H. and Sami, A.R., 2018. Tuning of PID controllers for quadcopter system using cultural exchange imperialist competitive algorithm. Journal of Engineering, 24(2), pp.80-99. https://doi.org/10.31026/j.eng.2018.02.06.
Abbood, A. A., 2023. A Fuzzy Logic Controller Based Vector Control of IPMSM Drives. Journal of Engineering, 19(10), pp. 1287–1299. https://doi.org/10.31026/j.eng.2013.10.07.
Abdulla, I. I., and Mohammed, I. K., 2023. Aircraft pitch control design using LQG controller based on genetic algorithm. TELKOMNIKA Telecommunication Computing Electronics and Control. https://doi.org/10.12928/telkomnika.v21i2.22051.
Antsaklis, P.J. and Nerode, A., 1998. Hybrid control systems: An introductory discussion to the special issue. IEEE Transactions on Automatic Control, 43(4), pp. 457–460. https://doi.org/10.1109/TAC.1998.664148.
Airplanes, B.C., 1959. Statistical summary of commercial jet airplane accidents. Worldwide Operations, 2008.
Bansal, H.O., Sharma, R. and Shreeraman, P.R., 2012. PID controller tuning techniques: a review. J. Control Eng. Technol, 2(4), pp.168-176.
Belcastro, C.M., Chang, B.C. and Fischl, R., 1992. A problem formulation for glideslope tracking in wind shear
using advanced robust control techniques (No. NASA-TM-104164).
Bian, Q., Nener, B., Li, T. and Wang, X., 2019. Multimodal control parameter optimization for aircraft longitudinal automatic landing via the hybrid particle swarm-BFGS algorithm. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 233(12), pp.4482-4491. https://doi.org/10.1177/0954410019825946.
Blum, C. and Roli, A., 2003. Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM computing surveys (CSUR), 35(3), pp.268-308. https://doi.org/10.1145/937503.937505.
Brukarczyk, B., Nowak, D., Kot, P., Rogalski, T. and Rzucidło, P., 2021. Fixed wing aircraft automatic landing with the use of a dedicated ground sign system. Aerospace, 8(6), p.167. https://doi.org/10.3390/aerospace8060167.
Chen, C., Ke, J., Xu, H., Lu, B. and Li, Q., 2021, July. Control Parameter Optimization for a Longitudinal Automatic Landing System via a Multi-objective Genetic Algorithm. In International Conference on Aerospace System Science and Engineering (pp. 119-133). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-16-8154-7_10.
Choi, R. Y., Coyner, A. S., Kalpathy-Cramer, J., Chiang, M. F., and Campbell, J. P., 2020. Introduction to Machine Learning, Neural Networks, and Deep Learning. Translational Vision Science and Technology, 9(2), P.14. https://doi.org/10.1167/tvst.9.2.14.
Dai, J.T. and Juang, J.G., 2019. Mathematical modeling of hybrid intelligent system to longitudinal landing control design. Journal of Intelligent & Fuzzy Systems, 36(2), pp.1287-1299. https://doi.org/10.3233/JIFS-169900.
Dai, J.T., Lee, C.L. and Juang, J.G., 2022. Intelligent System on Chip for Aircraft Landing Control. Sensors & Materials, 34. https://doi.org/10.18494/sam.2022.3547.
Desale, S., Rasool, A., Andhale, S. and Rane, P., 2015. Heuristic and meta-heuristic algorithms and their relevance to the real world: a survey. Int. J. Comput. Eng. Res. Trends, 351(5), pp.2349-7084.
Dorato, P., 1987. A historical review of robust control. IEEE Control Systems Magazine, 7(2), pp.44-47. https://doi.org/10.1109/MCS.1987.1105273.
Eroglu, B., Sahin, M.C. and Ure, N.K., 2020. Autolanding control system design with deep learning based fault estimation. Aerospace Science and Technology, 102, P.105855. https://doi.org/10.1016/j.ast.2020.105855.
Gautam, A., Sujit, P.B. and Saripalli, S., 2014, May. A survey of autonomous landing techniques for UAVs. In 2014 international conference on unmanned aircraft systems (ICUAS), pp. 1210-1218. IEEE. https://doi.org/10.1109/ICUAS.2014.6842377.
Gil, D., Hernàndez-Sabaté, A., Enconniere, J., Asmayawati, S., Folch, P., Borrego-Carazo, J., and Piera, M. À., 2022. E-Pilots: A System to Predict Hard Landing During the Approach Phase of Commercial Flights. IEEE Access, 10, pp. 7489-7503.https://doi.org/10.1007/978-981-16-8154-7_10.
Gudeta, S., and Karimoddini, A., 2019. Design of a Smooth Landing Trajectory Tracking System for a Fixed-wing Aircraft. In 2019 American Control Conference (ACC), pp. 5674-5679. Philadelphia, PA, USA: IEEE. https://doi.org/10.23919/ACC.2019.8814912.
Iqbal, J., Ullah, M., Khan, S.G., Khelifa, B. and Ćuković, S., 2017. Nonlinear control systems-A brief overview of historical and recent advances. Nonlinear Engineering, 6(4), pp. 301-312. https://doi.org/10.1515/nleng-2016-0077.
Jeong, S.H., Lee, K.B., Ham, J.H., Kim, J.H. and Cho, J.Y., 2020. Estimation of maximum strains and loads in aircraft landing using artificial neural network. International Journal of Aeronautical and Space Sciences, 21, pp.117-132. https://doi.org/10.1007/s42405-019-00204-2.
Juang, J.G. and Chio, J.Z., 2005. Fuzzy modelling control for aircraft automatic landing system. International Journal of Systems Science, 36(2), pp.77-87. https://doi.org/10.1080/0020772042000325961.
Juang, J.G. and Cheng, K.C., 2006. Application of neural networks to disturbances encountered landing control. IEEE Transactions on Intelligent Transportation Systems, 7(4), pp.582-588. https://doi.org/10.1109/TITS.2006.884885.
Juang, J.G. and Yu, S.T., 2015. Disturbance encountered landing system design based on sliding mode control with evolutionary computation and cerebellar model articulation controller. Applied Mathematical Modelling, 39(19), pp. 5862-5881. https://doi.org/10.1016/j.apm.2015.04.005.
Juang, J.G., Chang, H.H. and Chang, W.B., 2003. Intelligent automatic landing system using time delay neural network controller. Applied Artificial Intelligence, 17(7), pp.563-581. https://doi.org/10.1080/713827212.
Juang, J.G., Chang, H.H. and Cheng, K.C., 2002. Intelligent landing control using linearized inverse aircraft model. In Proceedings of the 2002 American Control Conference (IEEE Cat. No. CH37301). 4, pp. 3269-3274. IEEE. https://doi.org/10.1109/ACC.2002.1025295.
Juang, J.G., Chien, L.H. and Lin, F., 2011. Automatic landing control system design using adaptive neural network and its hardware realization. IEEE Systems Journal, 5(2), pp. 266-277. https://doi.org/10.1109/JSYST.2011.2134490.
Juang, J.G., Lin, B.S. and Chin, K.C., 2005. Automatic landing control using particle swarm optimization. In IEEE International Conference on Mechatronics, 2005. ICM'05. (pp. 721-726). IEEE. https://doi.org/10.1109/ICMECH.2005.1529350.
Khattak, A., Zhang, J., Chan, P.W. and Chen, F., 2023. Turbulence along the Runway Glide Path: The Invisible Hazard Assessment Based on a Wind Tunnel Study and Interpretable TPE-Optimized KTBoost Approach. Atmosphere, 14(6), P.920. https://doi.org/10.3390/atmos14060920.
Lungu, M., 2017. Automatic Control of Aircraft Landing by Using the H2/H∞ Control Technique. Modelling, Identification and Control. https://doi.org/10.2316/P.2017.848-004.
Lungu, M., and Lungu, R., 2016. Automatic control of aircraft lateral-directional motion during landing using neural networks and radio-technical subsystems. Neurocomputing, 171, pp.471-481. https://doi.org/10.1016/j.neucom.2015.06.084 .
Lungu, M., Lungu, R. and Tutunea, D., 2016. Control of Aircraft Landing using the Dynamic Inversion and the H-inf Control. In 2016 17th International Carpathian Control Conference (ICCC). pp. 461-466. IEEE. https://doi.org/10.1109/CarpathianCC.2016.7501142.
Lungu, R. and Lungu, M., 2015. Application of H2/H∞ and dynamic inversion techniques to aircraft landing control. Aerospace Science and Technology, 46, pp.146-158. https://doi.org/10.1016/j.ast.2015.07.005.
Lungu, R. and Lungu, M., 2017. Automatic landing system using neural networks and radio-technical subsystems. Chinese Journal of Aeronautics, 30(1), pp.399-411. https://doi.org/10.1016/j.cja.2016.12.019.
Lungu, R., Lungu, M. and Grigorie, L.T., 2013. Automatic control of aircraft in longitudinal plane during landing. IEEE Transactions on Aerospace and Electronic Systems, 49(2), pp.1338-1350. https://doi.org/10.1109/TAES.2013.6494418.
Luo, Q. and Duan, H., 2014. Chaotic artificial bee colony optimization approach to aircraft automatic landing system. IFAC Proceedings Volumes, 47(3), pp.876-881. https://doi.org/10.3182/20140824-6-ZA-1003.00330.
Luong, Q.V., Jang, D.S. and Hwang, J.H., 2020. Intelligent Control based on a neural network for aircraft landing gear with a magnetorheological damper in different landing scenarios. Applied Sciences, 10(17), p.5962. https://doi.org/10.3390/app10175962.
Maier, H.R., Razavi, S., Kapelan, Z., Matott, L.S., Kasprzyk, J. and Tolson, B.A., 2019. Introductory overview: Optimization using evolutionary algorithms and other metaheuristics. Environmental modelling & software, 114, pp.195-213. https://doi.org/10.1016/j.envsoft.2018.11.018.
Miller, W.T., Sutton, R.S. and Werbos, P.J., 1995. A neural network baseline problem for control of aircraft flare and touchdown. in Neural Networks for Control , MIT Press. pp. 403-425.
Nho, K. and Agarwal, R.K., 2000. Automatic landing system design using fuzzy logic. Journal of Guidance, Control, and Dynamics, 23(2), pp.298-304. https://doi.org/10.2514/2.4522.
Noor, M.Y.B.M., Ismail, M.A., b Khyasudeen, M.F., Shariffuddin, A., Kamel, N.I. and Azzuhri, S.R., 2017. Autonomous precision landing for commercial UAV: A review. The International Conference on Fuzzy Systems and Data Mining (FSDM), pp.459-468.
Nowak, D., Kopecki, G., Kordos, D. and Rogalski, T., 2022. The papi lights-based vision system for aircraft automatic control during approach and landing. Aerospace, 9(6), p.285. https://doi.org/10.3390/aerospace9060285.
O'Shea, K. and Nash, R., 2015. An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458.
Postlethwaite, I., Turner, M.C. and Herrmann, G., 2007. Robust control applications. Annual Reviews in Control, 31(1), pp.27-39. https://doi.org/10.1016/j.arcontrol.2007.02.003.
Pratiwi, W., Sofwan, A. and Setiawan, I., 2021. Implementation of fuzzy logic method for automation of decision making of Boeing aircraft landing. IAES International Journal of Artificial Intelligence, 10(3), p.545 –552. https://doi.org/10.11591/ijai.v10.i3.pp545-552.
Puranik, T.G., Rodriguez, N. and Mavris, D.N., 2020. Towards online prediction of safety-critical landing metrics in aviation using supervised machine learning. Transportation Research Part C: Emerging Technologies, 120, p.102819. https://doi.org/10.1016/j.trc.2020.102819.
Qian, S., Zhou, S., Chang, W. and Wei, F., 2017, December. An improved aircraft landing distance prediction model based on particle swarm optimization—Extreme learning machine method. In 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 2326-2330). IEEE. https://doi.org/10.1109/IEEM.2017.8290307.
Raj, K.D.S. and Tattikota, G., 2013. Design of fuzzy logic controller for auto landing applications. International Journal of Scientific and Research Publications, 3(5), pp. 1-9.
Ramli, A.A., Islam, M.R., Fudzee, M.F.M., Salamat, M.A. and Kasim, S., 2014. A practical weather forecasting for air traffic control system using fuzzy hierarchical technique. In Recent Advances on Soft Computing and Data Mining: Proceedings of The First International Conference on Soft Computing and Data Mining (SCDM-2014) Universiti Tun Hussein Onn Malaysia, Johor, MalaysiaJune 16th-18th, 2014 (pp. 99-109). Springer International Publishing. https://doi.org/10.1007/978-3-319-07692-8_10.
Rao, D.V. and Go, T.H., 2014. Automatic landing system design using sliding mode control. Aerospace Science and Technology, 32(1), pp.180-187. https://doi.org/10.1016/J.AST.2013.10.001.
Razzazan, M., Mohammadhassani, F. and Ramezani, A., 2016. Application of Model Predictive Control to a Boeing 747 longitudinal motion.
Rehman, A.U., Khan, M.U., Ali, M.Z.H., Shah, M.S., Ullah, M.F. and Ayub, M., 2021. Stability enhancement of commercial boeing aircraft with integration of pid controller. In 2021 International Conference on Applied and Engineering Mathematics (ICAEM) (pp. 43-48). IEEE. https://doi.org/10.1109/ICAEM53552.2021.9547186.
Rigatos, G., 2021. A nonlinear optimal control approach for the vertical take-off and landing aircraft. Guidance, Navigation and Control, 1(03), p.2150012. https://doi.org/10.1142/S2737480721500126.
Sabri, N., Aljunid, S.A., Salim, M.S., Badlishah, R.B., Kamaruddin, R. and Malek, M.A., 2013. Fuzzy inference system: Short review and design. Int. Rev. Autom. Control, 6(4), pp.441-449.
Salih, Z. and Saleh, M.H., 2022. Attitude and Altitude Control of Quadrotor Carrying a Suspended Payload using Genetic Algorithm. Journal of Engineering, 28(5), pp.25-40. https://doi.org/10.31026/j.eng.2022.05.03.
Sariyildiz, E., Oboe, R. and Ohnishi, K., 2019. Disturbance observer-based robust control and its applications: 35th anniversary overview. IEEE Transactions on Industrial Electronics, 67(3), pp.2042-2053. https://doi.org/10.1109/TIE.2019.2903752.
Saud, L.J. and Hasan, A.F., 2018. Design of an Optimal Integral Backstepping Controller for a Quadcopter. Journal of Engineering, 24(5), pp.46-65. https://doi.org/10.31026/j.eng.2018.05.04.
Seitbattalov, Z.Y., Atanov, S.K. and Moldabayeva, Z.S., 2021, April. An Intelligent Decision Support System for Aircraft Landing Based on the Runway Surface. In 2021 IEEE International Conference on Smart Information Systems and Technologies (SIST) (pp. 1-5). IEEE. https://doi.org/10.1109/SIST50301.2021.9466000.
Sudha, G. and Deepa, S.N., 2016. Optimization for pid control parameters on pitch control of aircraft dynamics based on tuning methods. Applied Mathematics & Information Sciences, 10(1), p.343. https://doi.org/10.18576/amis/100136.
Suharev, A., Shestakov, V. and Stefanski, K., 2019, February. Analysis of the affecting factors on aircraft takeoff and landing ground path length. In AIP Conference Proceedings, 2077(1), P. 020056. AIP Publishing LLC. https://doi.org/10.1063/1.5091917.
Szabolcsi, R., 2018. Optimal PID controller based autopilot design and system modelling for small unmanned aerial vehicle. Review of the Air Force Academy, (3), pp.43-58. https://doi.org/10.19062/1842-9238.2018.16.3.6.
Tamkaya, K., Ucun, L. and Ustoglu, I., 2019. H∞-based model following method in autolanding systems. Aerospace Science and Technology, 94, p.105379. https://doi.org/0.1016/j.ast.2019.105379.
Tang, C. and Lai, Y.C., 2020. Deep reinforcement learning automatic landing control of fixed-wing aircraft using deep deterministic policy gradient. In 2020 international conference on unmanned aircraft systems (ICUAS) (pp. 1-9). IEEE. https://doi.org/10.1109/ICUAS48674.2020.9213987.
Voicu, S.C. and Buţu, F.A., 2017. H-Infinity Design for Automatic Landing System. International Journal of Modeling and Optimization, 7(3), p.173.
Wang, X., Sang, Y. and Zhou, G., 2020. Combining stable inversion and H∞ synthesis for trajectory tracking and disturbance rejection control of civil aircraft autolanding. Applied Sciences, 10(4), p.1224. https://doi.org/10.3390/app10041224.
Wang, Y., Li, Q. and Lu, B., 2018. Automatic landing system design via multivariable model reference adaptive control. Aerospace Systems, 1, pp.63-71. https://doi.org/10.1007/s42401-018-0006-z.
Wijaya, R.F., Tondang, Y.M. and Siahaan, A.P.U., 2016. Take Off and Landing Prediction using Fuzzy Logic. Weather, 2(12).
Wu, Y., Tan, W., Sun, L. and Qu, X., 2016. A Decision-Making Method for Landing Routes of Aircraft on the Carrier. In MATEC Web of Conferences, 75, p. 05002. EDP Sciences. https://doi.org/10.1051/matecconf/20167505002.
Xin, L., Tang, Z., Gai, W., and Liu, H., 2022. Vision-based autonomous landing for the uav: A review. Aerospace, 9(11), 634, pp. 1-20. https://doi.org/10.3390/aerospace9110634
Yadav, D.K., Kannan, P. and Mansor, S., 2022. Evaluating an aircraft response to disturbances caused by vibration frequency of wind forces during landing. Journal of aerospace technology and management, 14. https://doi.org/10.1590/jatm.v14.1261 .
Yoon, S.H., Kim, Y.D. and Park, S.H., 2012. Constrained adaptive backstepping controller design for aircraft landing in wind disturbance and actuator stuck. International Journal of Aeronautical and Space Sciences, 13(1), pp.74-89. https://doi.org/10.5139/IJASS.2012.13.1.74.
Yu, B. and Zhang, Y., 2016. Fault-tolerant control of a boeing 747-100/200 based on a laguerre function-based mpc scheme. IFAC-PapersOnLine, 49(17), pp.58-63. https://doi.org/10.1016/j.ifacol.2016.09.011.
Yu, C.Y. and Juang, J.G., 2013, June. Application of intelligent systems and DSP to landing controller design. In 2013 9th Asian Control Conference (ASCC), pp. 1-6. IEEE. https://doi.org/10.1109/ASCC.2013.6606275.
Zadeh, V.T., 2011. Fuzzy logic approach to airplane precision instrument approach and landing (Doctoral dissertation, California State University, Sacramento).
Zarchi, M. and Attaran, B., 2019. Improved design of an active landing gear for a passenger aircraft using multi-objective optimization technique. Structural and Multidisciplinary Optimization, 59, pp.1813-1833. https://doi.org/10.1007/s00158-018-2135-8.
Zhang, S., 2017. Review of vertical take-off and landing aircraft. Second International Conference on Mechanical, Control and Computer Engineering (ICMCCE), pp. 53-56. IEEE. https://doi.org/10.1109/ICMCCE.2017.9
Zipeng, L. and Yanyang, W., 2018. A Review for Aircraft Landing Problem. International Conference on Mechanical, Material and Aerospace Engineering 179(03016), pp.1-6. https://doi.org/matecconf/201817903016.
Zulkifli, A., Aziz, N.A.A., Aziz, N.H.A., Ibrahim, Z. and Mokhtar, N., 2018. Review on computational techniques in solving aircraft landing problem. International Conference on Artificial Life and Robotics, Oita, Japan, pp. 128-131.