Published January 2017 | Version v1
Journal article

Plant identification module design for accelerator magnet power supplies

  • 1. University of Chinese Academy of Sciences, Beijing (China)
  • 2. Institute of High Energy Physics, Chinese Academy of Sciences, Beijing (China)

Description

Abstract Background: The control strategy of accelerator power supplies mainly depends on PID (Proportion-integral-derivative) controlling at domestic plant. The controlled plant is treated as transfer functions induced from physical models and the controller design depends on them. This approach suffers from the shifting between design values and the real elements as well as the uncertainty of the hardware structure. Moreover, the engineers are mainly not interested in the internal mechanisms of the plants but their input-output (I/O) behavior. Purpose: This study aims to design a plant identification module with better real-time performance, applicability and versatility. Methods: Based on subspace model identification, particularly the MOESP (Multivariable Output Error State sPace) method, the FPGA (Field Programmable Gate Array) modules are designed in pertinence and the identification algorithm is processed by embedded SOPC (System On a Programmable Chip). These modules were applied to magnet power supply digital control platform for both BEPCII (Beijing Electron Positron Collider II) and ADS (Accelerator Driven Sub-critical System). Results: The identified model was strictly tested and proved to be capable to predict the output current with significant accuracy for magnet power supplies of both BEPCII and ADS. Conclusion: The module is easy to use for providing key information for controller design and compatible with loadings of various characteristics. Compared with traditional analytic modelling, the plant identification module performs better in applicability, versatility and real-time performance. (authors)

Additional details

Publishing Information

Journal Title
Nuclear Techniques
Journal Volume
40
Journal Issue
1
Journal Page Range
[7 p.]
ISSN
0253-3219

Optional Information

Notes
6 figs., 12 refs.; http://dx.doi.org/10.11889/j.0253-3219.2017.hjs.40.010402