Accommodation of Pulsed Field Gradients with Cascade Field Regulation in Powered Magnets

Benjamin D. McPheron, Jeffrey L. Schiano, Ilya M. Litvak, William W. Brey

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

High magnetic fields significantly improve the resolution and sensitivity of nuclear magnetic resonance (NMR) spectroscopy measurements, which presents exciting research opportunities in areas of chemistry, biology, and material science. Powered magnets can provide much higher magnetic fields than persistent mode superconducting magnets but suffer from temporal magnetic field fluctuations due to power supply ripple and variations in cooling water temperature and flow rate which make powered magnets non-viable for high resolution NMR experiments. Previous work has demonstrated that a multi-rate sampled data cascade control system may be used to improve the resolution of NMR experiments in powered magnets. Despite these advances in reducing temporal magnetic field fluctuations, the field regulation design does not accommodate the use of pulsed field gradients, which are necessary in many NMR experiments. This work presents a control topology which accommodates the use of pulsed field gradient signals with the field regulation system. This control approach is verified using NMR measurements.

Original languageEnglish (US)
Title of host publication60th IEEE Conference on Decision and Control, CDC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages611-616
Number of pages6
ISBN (Electronic)9781665436595
DOIs
StatePublished - 2021
Event60th IEEE Conference on Decision and Control, CDC 2021 - Austin, United States
Duration: Dec 13 2021Dec 17 2021

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume2021-December
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370

Conference

Conference60th IEEE Conference on Decision and Control, CDC 2021
Country/TerritoryUnited States
CityAustin
Period12/13/2112/17/21

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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