Abstract
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 language | English (US) |
|---|---|
| Pages (from-to) | 1687-1691 |
| Number of pages | 5 |
| Journal | Proceedings of the American Control Conference |
| Volume | 3 |
| State | Published - 1999 |
| Event | Proceedings of the 1999 American Control Conference (99ACC) - San Diego, CA, USA Duration: Jun 2 1999 → Jun 4 1999 |
ASJC Scopus subject areas
- Electrical and Electronic Engineering