ML Anthology

Neural Network On-Line Learning Control of Spacecraft Smart Structures

Christopher Bowman
NeurIPS 1992 pp. 303-310
/neurips/1992/bowman1992neurips-neural/

Abstract

The overall goal is to reduce spacecraft weight. volume, and cost by on(cid:173) line adaptive non-linear control of flexible structural components. The objective of this effort is to develop an adaptive Neural Network (NN) controller for the Ball C-Side 1m x 3m antenna with embedded actuators and the RAMS sensor system. A traditional optimal controller for the major modes is provided perturbations by the NN to compensate for unknown residual modes. On-line training of recurrent and feed-forward NN architectures have achieved adaptive vibration control with unknown modal variations and noisy measurements. On-line training feedback to each actuator NN output is computed via Newton's method to reduce the difference between desired and achieved antenna positions.

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Cite

Text

Bowman. "Neural Network On-Line Learning Control of Spacecraft Smart Structures." Neural Information Processing Systems, 1992.

Markdown

[Bowman. "Neural Network On-Line Learning Control of Spacecraft Smart Structures." Neural Information Processing Systems, 1992.](https://mlanthology.org/neurips/1992/bowman1992neurips-neural/)

BibTeX

@inproceedings{bowman1992neurips-neural,
  title     = {{Neural Network On-Line Learning Control of Spacecraft Smart Structures}},
  author    = {Bowman, Christopher},
  booktitle = {Neural Information Processing Systems},
  year      = {1992},
  pages     = {303-310},
  url       = {https://mlanthology.org/neurips/1992/bowman1992neurips-neural/}
}