A   B   C   D   E   F   G   H   I   J   K   L   M   N   O   P   Q   R   S   T   U   V   W   X   Y   Z  

Biedron, S.

Paper Title Page
TU5RFP050 Electron Beam Energy Stabilization Using a Neural Network Hybrid Controller at the Australian Synchrotron Linac 1201
 
  • E. Meier, G. LeBlanc
    ASCo, Clayton, Victoria
  • S. Biedron
    Argonne National Laboratory, Office of Naval Research Project, Argonne
  • M.J. Morgan
    Monash University, Faculty of Science, Victoria
  • J. Wu
    SLAC, Menlo Park, California
  
 

This paper describes the implementation of a neural network hybrid controller for energy stabilization at the Australian Synchrotron Linac. The structure of the controller consists of a neural network (NNET) feed forward control, augmented by a conventional Proportional-Integral (PI) feedback controller to ensure stability of the system. The system is provided with past states of the machine in order to predict its future state, and therefore apply appropriate feed forward control. The NNET is able to cancel multiple frequency jitter in real-time. When it is not performing optimally due to jitter changes, the system can successfully be augmented by the PI controller to attenuate the remaining perturbations.