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

  • C. J. B. Macnab
Original Article

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.

Keywords

Cerebellar model articulation controller CMAC Direct adaptive control Parameter drift Weight drift overlearning Lyapunov stability Adaptive backstepping Flexible joint 

Notes

Compliance with ethical standards

Conflict of interest

The author declares that he has no conflict of interest.

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Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringUniversity of CalgaryCalgaryCanada

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