Analysis of fault-tolerant neurocontrol architectures
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Analysis of fault-tolerant neurocontrol architectures

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Published by National Aeronautics and Space Administration, For sale by the National Technical Information Service in [Washington, DC, Springfield, Va .
Written in English

Subjects:

  • Neural networks (Computer science),
  • Fault-tolerant computing.

Book details:

Edition Notes

Other titlesAnalysis of fault tolerant neurocontrol architectures.
StatementT. Troudet and W. Merrill.
SeriesNASA technical memorandum -- 105898.
ContributionsMerrill, Walter C., United States. National Aeronautics and Space Administration.
The Physical Object
FormatMicroform
Pagination1 v.
ID Numbers
Open LibraryOL15367269M

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By Lyapunov analysis, we prove the uniform ultimately boundedness (UUB) of all the states, their estimation errors, and NN weights of the fault tolerant system under the unpredictable faults. Abstract. In this paper we discuss the resolution of the generalized Sylvester matrix equation AXB + CXD = E, where A, C ∈ R m xm, B, D ∈ R m xm, E ∈ R m xn, and the unknown X is m × equation is related to different topics in Control Theory and Linear Algebra: perturbation analysis of the generalized eigenvalue problem (Stewart-Sun [19]), solution of implicit linear differential.   Inverse kinematics is a fundamental problem in robotics. Past solutions for this problem have been realized through the use of various algebraic or algorithmic procedures. In this paper the use of feedforward neural networks to solve the inverse kinematics problem is examined for three different cases. A closed kinematic linkage is used for mapping input joint Cited by: 6. Applications of a variety of neural network architectures in control are surveyed. We explore the links between the fields of control science and neural networks in a unified presentation and identify key areas for future by:

Inverse Kinematics is relevant to other areas of control, either for deriving the set-points for controllers, or being an active part of an intelligent system. Previous neural network approaches to this problem have examined only limited regions of the robot work space. GNC Adaptive and Reconfigurable Control II • Monday, 05 August • hrs. Bibliographic content of IEEE Transactions on Neural Networks, Volume Rafael Serrano-Gotarredona, Teresa Serrano-Gotarredona, Antonio Acosta-Jimenez, Carmen Serrano-Gotarredona, José Antonio Pérez-Carrasco, Bernabé Linares-Barranco, Alejandro Linares-Barranco, Gabriel Jiménez-Moreno, Antón Civit Balcells: On Real-Time AER 2-D Convolutions . Wang, N. Hovakimyan, L. Sha, L1Simplex: Fault-Tolerant Control of Cyber-Physical Systems, In Proceedings of ACM/IEEE 4 th International Conference on Cyber-Physical Systems, Philadelphia, PA, pp. , (one of the four finalists for “best paper award”).

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