Nuclear Techniques ›› 2017, Vol. 40 ›› Issue (1): 10402-010402.doi: 10.11889/j.0253-3219.2017.hjs.40.010402


Plant identification module design for accelerator magnet power supplies

SHU Kun1,2, LONG Fengli1   

  1. 1 Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China;
    2 University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2016-10-11 Revised:2016-11-06 Online:2017-01-10 Published:2017-01-11
  • Supported by:

    Supported by Strategic Priority Program on ADS Transmutation System of Advanced Fission Energy (No.Y12C32L129)


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.

Key words: Accelerator magnet power supply, Subspace model identification, State space model, FPGA, SOPC

CLC Number: 

  • TL503.5