PAC2013 - List of Authors (Dusatko, J.E.)

Paper	Title	Page
TUPAC25	Identification of Intra-Bunch Dynamics Using CERN SPS Machine Measurements	502
	O. Turgut, J.M. Cesaratto, J.E. Dusatko, J.D. Fox, K.M. Pollock, C.H. Rivetta SLAC, Menlo Park, California, USA
	Funding: U.S. Department of Energy under contract DE-AC02-76SF00515 and the US LHC Accelerator Research program (LARP) Modern control techniques can be used to design feedback systems for stabilizing the intra-bunch dynamics in the presence of electron cloud (ECI) and transverse mode coupling (TMCI) instabilities. These techniques require reduced models of the bunch dynamics. We present a methodology to identify reduced order linear models representing single bunch dynamics using CERN SPS machine measurements. Vertical motion, in response to a wideband excitation signal, is sampled multiple times across the 5 ns bunch. The data and an observable canonical structure are used to identify the dynamics, which is represented as discrete time multi-input multi-output (MIMO) system. We focused on mode 0 (barycentric) and mode 1 (head-tail) data to identify a reduced order model. Results show that models clearly capture dominant dynamics and replicate machine measurements with corresponding central tune, damping values for each mode and correct separation between modes.

Paper

Title

Page

Identification of Intra-Bunch Dynamics Using CERN SPS Machine Measurements

502

O. Turgut, J.M. Cesaratto, J.E. Dusatko, J.D. Fox, K.M. Pollock, C.H. Rivetta
SLAC, Menlo Park, California, USA

Funding: U.S. Department of Energy under contract DE-AC02-76SF00515 and the US LHC Accelerator Research program (LARP)
Modern control techniques can be used to design feedback systems for stabilizing the intra-bunch dynamics in the presence of electron cloud (ECI) and transverse mode coupling (TMCI) instabilities. These techniques require reduced models of the bunch dynamics. We present a methodology to identify reduced order linear models representing single bunch dynamics using CERN SPS machine measurements. Vertical motion, in response to a wideband excitation signal, is sampled multiple times across the 5 ns bunch. The data and an observable canonical structure are used to identify the dynamics, which is represented as discrete time multi-input multi-output (MIMO) system. We focused on mode 0 (barycentric) and mode 1 (head-tail) data to identify a reduced order model. Results show that models clearly capture dominant dynamics and replicate machine measurements with corresponding central tune, damping values for each mode and correct separation between modes.