E. Meier, Y.-R. E. Tan, G. S. LeBlanc
Australian Synchrotron
3rd International Particle Accelerator Conference
Abstract
This paper reports the use of neural networks for orbitcorrection at the Australian Synchrotron Storage Ring. Theproposed system uses two neural networks in an actor-criticscheme to model a long term cost function and computeappropriate corrections. The system is entirely based onthe history of the beam position and the actuators, i.e. thecorrector magnets, in the storage ring. This makes the sys-tem auto-tuneable, which has the advantage of avoiding themeasure of a response matrix. The controller will automat-ically maintain an updated BPM corrector response matrix.In future if coupled with some form of orbit response anal-ysis, the system will have the potential to track drifts orchanges to the lattice functions in ”real time”. As a genericand robust orbit correction program it can be used duringcommissioning and in slow orbit feedback. In this study,we present positive initial results of the simulations of thestorage ring in Matlab.
Read the paper: https://accelconf.web.cern.ch/IPAC2012/papers/weppp057.pdf
Contact: Evelyne Meier