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Stability, Convergence, and Verification and Validation Challenges of Neural Net Adaptive Flight Control

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Applications of Neural Networks in High Assurance Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 268))

Abstract

This paper provides a discussion of challenges of neural net adaptive flight control and an examination of stability and convergence issues of adaptive control algorithms. Understanding stability and convergence issues with adaptive control is important in order to advance adaptive control to a higher technology readiness level. The stability and convergence of neural net learning law are investigated. The effect of unmodeled dynamics on learning law is examined. Potential improvements in the learning law and adaptive control architecture based on optimal estimation are presented. The paper provides a brief summary of the future research of the Integrated Resilient Aircraft Control (IRAC) in the area of adaptive flight control. The paper also discusses challenges and future research in verification and validation.

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Authors
  1. Nhan T. Nguyen
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  2. Stephen A. Jacklin
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Editor information

Editors and Affiliations

  1. RIACS/USRA, NASA Ames Research Center, M/S 269-3, 94035, Moffett Field, CA, USA

    Johann Schumann

  2. Motorola Autonomic Networking Labs, 1301 East Algonquin Road, 60193, Schaumburg, IL, USA

    Yan Liu

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Nguyen, N.T., Jacklin, S.A. (2010). Stability, Convergence, and Verification and Validation Challenges of Neural Net Adaptive Flight Control. In: Schumann, J., Liu, Y. (eds) Applications of Neural Networks in High Assurance Systems. Studies in Computational Intelligence, vol 268. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10690-3_5

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  • DOI: https://doi.org/10.1007/978-3-642-10690-3_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10689-7

  • Online ISBN: 978-3-642-10690-3

  • eBook Packages: EngineeringEngineering (R0)

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