Discrete-time weight updates in neural-adaptive control

Discrete-time weight updates in neural-adaptive control Typical neural-adaptive control approaches update neural-network weights as though they were adaptive parameters in a continuous-time adaptive control. However, requiring fast digital rates usually restricts the size of the neural network. In this paper we analyze a delta-rule update for the weights, applied at a relatively slow digital rate. We show that digital weight update causes the neural network to estimate a discrete-time model of the system, assuming that state feedback is still applied in continuous time. A Lyapunov analysis shows uniformly ultimately bounded signals. Furthermore, slowing the update frequency and using the extra computational time to increase the size/accuracy of the neural network results in better performance. Experimental results achieving link tracking of a two-link flexible-joint robot verify the improved performance. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Soft Computing Springer Journals

Discrete-time weight updates in neural-adaptive control

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Publisher
Springer Journals
Copyright
Copyright © 2012 by Springer-Verlag
Subject
Engineering; Computational Intelligence; Artificial Intelligence (incl. Robotics); Mathematical Logic and Foundations; Control, Robotics, Mechatronics
ISSN
1432-7643
eISSN
1433-7479
D.O.I.
10.1007/s00500-012-0918-1
Publisher site
See Article on Publisher Site

Abstract

Typical neural-adaptive control approaches update neural-network weights as though they were adaptive parameters in a continuous-time adaptive control. However, requiring fast digital rates usually restricts the size of the neural network. In this paper we analyze a delta-rule update for the weights, applied at a relatively slow digital rate. We show that digital weight update causes the neural network to estimate a discrete-time model of the system, assuming that state feedback is still applied in continuous time. A Lyapunov analysis shows uniformly ultimately bounded signals. Furthermore, slowing the update frequency and using the extra computational time to increase the size/accuracy of the neural network results in better performance. Experimental results achieving link tracking of a two-link flexible-joint robot verify the improved performance.

Journal

Soft ComputingSpringer Journals

Published: Sep 30, 2012

References

  • Experimental studies of a generalized neuron based adaptive power system stabilizer
    Chaturvedi, DK; Malik, OP
  • Adaptive output recurrent cerebellar model articulation controller for nonlinear system control
    Chiu, CH
  • Robust intelligent backstepping tracking control for wheeled inverted pendulum
    Chiu, CH; Peng, YF; Lin, YW
  • A new robust weight update for multilayer-perceptron adaptive control
    Macnab, C
  • Feedback error learning and nonlinear adaptive control
    Nakanishi, J; Schaal, S
  • A survey on the control of flexible joint robots
    Ozgoli, S; Taghirad, HD

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