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Soft Computing

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01.03.2013 | Original Paper

Discrete-time weight updates in neural-adaptive control

verfasst von: D. Richert, K. Masaud, C. J. B. Macnab

Erschienen in: Soft Computing | Ausgabe 3/2013

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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.

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Titel: Discrete-time weight updates in neural-adaptive control
verfasst von: D. Richert
K. Masaud
C. J. B. Macnab
Publikationsdatum: 01.03.2013
Verlag: Springer-Verlag
Erschienen in: Soft Computing / Ausgabe 3/2013
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-012-0918-1

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