Experimental comparative studies on neural network controllers for DC-motor micromaneuvering

To Chan, Anthony Tzes, Pei Kai Wang

Research output: Contribution to journalConference articlepeer-review


The performance attributes of a structure-free and a model-based neural network (NN) controller for dc-motor micromaneuvering purposes are compared in experimental studies in this article. The former NN has a generic structure independent of the friction model, where its input vector consists of the time history of the motor angular shaft velocity within a time window. The NN provides the nonlinear control mapping to the supplied motor input through adjustment of its weights using the sign gradient descent algorithm. The model-based NN has a predetermined structure that depends on the utilized friction model. This NN provides a feedforward term which compensates for the inherent friction while rejecting noise via an additional linear velocity error feedback term. Application of both NN-based controllers on a dc-motor system reveals that the model-based NN has a superior performance measured in terms of its: a) convergence, b) implementation computational requirements, and c) integral square error after the weights' convergence.

Original languageEnglish (US)
Pages (from-to)1687-1691
Number of pages5
JournalProceedings of the American Control Conference
StatePublished - 1999
EventProceedings of the 1999 American Control Conference (99ACC) - San Diego, CA, USA
Duration: Jun 2 1999Jun 4 1999

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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