Neural Network Applications in Advanced Aircraft Flight Control System, a Hybrid System, a Flight Test Demonstration

  • Fola Soares
  • John Burken
  • Tshilidzi Marwala
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)


Modern exploration missions require modern control systems that can handle catastrophic changes in behavior, compensate for slow deterioration in sustained operations, and support fast system identification. The dynamics and control of new vehicles remains a significant technical challenge. Neural network based adaptive controllers have these capabilities, but they can only be used safely if proper Verification and Validation can be done. Due to the nonlinear and dynamic nature of an adaptive control system, traditional Verification and Validation (V&V) and certification techniques are not sufficient for adaptive controllers, which is a big barrier in their deployment in the safety-critical applications. Moreover, traditional methods of V&V involve testing under various conditions which is costly to run and requires scheduling a long time in advance. We have developed specific techniques, tools, and processes to perform design time analysis, verification and validation, and dynamic monitoring of such controllers. Combined with advanced modelling tools, an integrated development or deployment methodology for addressing complex control needs in a safety- and reliability-critical mission environment can be provided.


Adaptive Controller Adaptive Control System Adaptive Neural Network Aircraft System Matrix Failure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cukic, B.: The need for verification and validation techniques for adaptive control system. In: Proceedings of the Fifth International Symposium on Autonomous Decentralized Systems, pp. 297–298 (2001)Google Scholar
  2. 2.
    Mehrabian, A.R., Lucas, C., Roshanian, J.: Aerospace launch vehicle control: an intelligent adaptive approach. Aerospace Science and Technology 10, 149–155 (2006)CrossRefMATHGoogle Scholar
  3. 3.
    Kim, B.S., Calise, A.J.: A Nonlinear flight control using neural networks. Journal of Guidance, Control Dynamics 20, 26–33 (1997)CrossRefMATHGoogle Scholar
  4. 4.
    Lee, S., Ha, C., Kim, B.S.: Adaptive nonlinear control system design for helicopter robust command augmentation. Aerospace Science and Technology 9, 241–251 (2005)CrossRefMATHGoogle Scholar
  5. 5.
    Melin, P., Castillo, O.: Adaptive intelligent control of aircraft systems with a hybrid approach combining neural networks, fuzzy logic and fractal theory. Applied Soft Computing 3, 353–362 (2003)CrossRefGoogle Scholar
  6. 6.
    Gobbo, D.D., Mili, A.: An application of relational algebra: specification of a fault tolerant flight control system. Electronic Notes in Theoretical Computer Science 44, 1–18 (2003)CrossRefGoogle Scholar
  7. 7.
    Peterson, B.B., Narendra, K.S.: Bounded error adaptive control. IEEE Transactions on Automatic Control 27, 1161–1168 (1982)CrossRefMATHGoogle Scholar
  8. 8.
    Van de Vegte, J.: Feedback control systems, 3rd edn., pp. 134–148. Prentice Hall, Englewood Cliffs (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Fola Soares
    • 1
  • John Burken
    • 2
  • Tshilidzi Marwala
    • 3
  1. 1.Contek ResearchEl SegundoU.S.A
  2. 2.NASA Dryden Flight Research CenterEdwardsU.S.A
  3. 3.University of the WitwatersrandJohannesburgSouth Africa

Personalised recommendations