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Neural Computing and Applications

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29.08.2017 | Original Article

Modifying CMAC adaptive control with weight smoothing in order to avoid overlearning and bursting

verfasst von: C. J. B. Macnab

Erschienen in: Neural Computing and Applications | Ausgabe 7/2019

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Abstract

This paper proposes a method to prevent overlearning (weight drift) and bursting in adaptive control using the cerebellar model arithmetic computer (CMAC). Traditional robust adaptive methods such as deadzone, projection, e-modification are not necessarily suitable for CMAC control. The proposed method relies on the idea of weight smoothing, where the difference between adjacent weights in the CMAC is penalized in the adaptation. Previously proposed weight smoothing schemes are only suitable for one or two-input CMACs and do not have stability guarantees. This work extends the method for use with n-input CMACs and develops the adaptive scheme within a Lyapunov framework to guarantee uniformly ultimately bounded signals. Simulations with a two-link, flexible-joint robot subject to sinusoidal disturbance demonstrate the performance and stability of the new method.

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Titel: Modifying CMAC adaptive control with weight smoothing in order to avoid overlearning and bursting
verfasst von: C. J. B. Macnab
Publikationsdatum: 29.08.2017
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 7/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-017-3182-6

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