Control of ultra-high precision magnetic leadscrew using recurrent neural networks

Timothy Chang, Tony Wong, Bhaskar Dani, Zhiming Ji, Mike Shimanovich, Reggie Caudill

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

In this work, the problem of vibration control for a contactless magnetic leadscrew system is considered. A contactless drive system is a magnetic nut/leadscrew and air bearing assembly that operates on the principle of magnetic/aerodynamic suspension to position a load with high accuracy. However, the dynamics of such system is lightly damped, load dependent, and generally difficult to stabilize by conventional linear controllers. Therefore, the technique of recurrent neural network is applied to separate the oscillatory signals so that passband shaping can be carried out to regulate plant dynamics and to reject disturbances. This controller possesses a modular structure and is easy to implement. Experimental results also confirm the vibration suppression capabilities of this controller.

Original languageEnglish (US)
Pages (from-to)208-219
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume3518
StatePublished - Dec 1 1998
EventProceedings of the 1998 Conference on Sensors and Controls for Intelligent Machining, Agile Manufacturing, and Mechatronics - Boston, MA, USA
Duration: Nov 4 1998Nov 5 1998

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Control of ultra-high precision magnetic leadscrew using recurrent neural networks'. Together they form a unique fingerprint.

Cite this