Robust Flare Phase Auto Landing of Aircrafts, Based on Modified ASBO

  • G. Parimala Gandhi
  • Nagaraj Ramrao
  • Manoj Kumar Singh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 516)


The presented research work is focused on automatic flight landing control of an aircraft for synthesis of optimal flare phase with considering the dynamic deflection angle as a control parameter. The behavior of the aircraft has considered in terms of four first-order differential equations and an explicit one step Runge Kutta has an order of 4 to 5 has applied to solve that. Computational intelligence based approach; modified adaptive social behavior optimization (mASBO) has applied to estimate the optimum deflection angles on discrete time to deliver the desired flare phase performances. The proposed modification in adaptive social behavior optimization has a better balance between exploration and exploitation by providing a competitive environment for leader selection, in result, faster convergence achieved. Height based on ascent rate controlling function presented here provides an adaptive control mechanism to obtain the desired landing performances in the presence of change in starting landing altitude in compare of predefined reference altitude due to poor visibility or wind disturbance.


Aircraft landing control Flare phase Deflection angle ASBO 



This research has completed in Manuro Tech Research Pvt. Ltd., Bangalore, India. The Authors express their thanks to associated members for their valuable suggestions to accomplish this research.


  1. 1.
    Boeing: Statistical summary of commercial jet airplane accidents, worldwide operations, 1959–2012 (Boeing Commercial Airplanes, Seattle, WA 2013)Google Scholar
  2. 2.
    Flight Safety Foundation. Data’s source: aviation safety network (2014),
  3. 3.
    P. MaSłowskI. Longitudinal Motion control for flare phase of landing. Trans. Inst. Aviation 217, 79–93 (2011)Google Scholar
  4. 4.
    J.-G. Juang K.-C. Chin, J.-Z. Chio, Intelligent automatic landing system using fuzzy neural networks and genetic algorithm, in Proceeding of the 2004 American Control Conference Boston, Massachusetts, 30 June−2 July 2004Google Scholar
  5. 5.
    J.-G. Juang, W.-P. Lin, Aircraft landing control based on CMAC and GA Techniques, in Proceedings of the 17th World Congress, The International Federation of Automatic Control Seoul, Korea, 6–11 July 2008Google Scholar
  6. 6.
    J.-G. Juang, H.-K. Chiou, L.-H. Chien, Analysis and comparison of aircraft landing control using recurrent neural networks and genetic algorithms approaches. Neurocomputing 71(16–18), 3224–3238 (2008)CrossRefGoogle Scholar
  7. 7.
    R. Lungu, M. Lungu, L.T. Grigorie, Automatic control of aircraft in longitudinal plane during landing. IEEE Trans. Aerospace Electron. Syst. 49(2), 1338−1350 (2013)Google Scholar
  8. 8.
    R. Lungu, M. Lungu, Design of automatic landing systems using the H-inf control and the dynamic inversion. ASME. J. Dyn. Sys. Meas. Control. 138(2), 024501-5 (2015). doi: 10.1115/1.4032028
  9. 9.
    M. Lungu, R. Lungu, Automatic control of aircraft lateral-directional motion during landing using neural networks and radio-technical subsystems. Neurocomputing. 171, 471–481 (2016)Google Scholar
  10. 10.
    M.K. Singh, A new optimization method based on adaptive social behavior: ASBO. (Springer, AISC 174, 2012), pp. 823–831Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • G. Parimala Gandhi
    • 1
  • Nagaraj Ramrao
    • 2
  • Manoj Kumar Singh
    • 3
  1. 1.Department of ECE-RRITBangaloreIndia
  2. 2.DhirubhaiAmbani-IICTGandhinagarIndia
  3. 3.Manuro Tech Research Pvt. LtdBangaloreIndia

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