Improvements in LLRF Control Algorithms and Automation - Desy

Improvements in LLRF Control 

Algorithms and Automation 

Alexander Brandt 

September 7 th 2004 alexander.brandt@desy.de 1/14

Overview 

• Automation concepts 

– Finite State Machine 

– Algorithm development 

• Status 

– LLRF automation concept 

– Klystron FSM 

– LLRF FSM (Algorithms / Diagnostics) 

• Adaptive Feedforward 

• Summary / Outlook 

– One-button machine 


Finite State Machines 

State 1 

t21 

t31 

t13 

t23 

State 2 State 3 

t32 

• Graphical representation of 

logical dependencies 

• Transitions are connected to 

conditions 

• Inside states, procedures can 

be implemented for entry, exit, 

timer 

• DOOCs ddd offers a tool for 

FSM generation 

• Easy generation of states 

• Transparent behaviour of 

machine by full integration 

into ddd panels 


FSM Applications, Purposes 

• Manage complex logic dependencies (! klystron 

logic) 

• Ease the operators job, save time 

• Perform time-consuming calculations in the 

background for monitoring / parameter estimation 

• React on outer conditions (exception handling) 


Algorithms 

(Fast) ”prototyping“ using 

Matlab / Simulink and the 

DOOCs client library 

2 

3 

ttfr(‘TTF.RF/LLRF.DSP‘) 

ttfw(‘TTF.RF/ADC‘) 

. . . 

Implementation in C/C++ of 

algorithms using DOOCS 

FSM Generator and Matlab 

Cmath-Library 

Availability 

Algorithms 

diagnostics 

ddd 

of 

and 

via 

1 

#include “matlab.h“ 

mxArray* ma; 

for(int c=1; c

LLRF Automation Concept (Status) 

Interface 

Global LLRF FSM 

Gun ACC1 ACC2/3 ACC4/5 

Interface 

Interface 

Interface 

Interface 

Local LLRF FSM 




Interface 

Interface 

Interface 

Interface 

Klystron FSM 

Klystron FSM 

Klystron FSM 

Klystron FSM 

• Usually, operator interacts with 

global LLRF FSM interface, FSMs 

interact with hardware 

• Experienced operator interacts with 

sub FSM interfaces 

• Experts interact with hardware 

itself 

• Klystron FSM model (Cichalewski, Koseda) 

exists in Matlab Simulink and are currently 

translated to DOOCS FSM 

• Algorithms for diagnostics and parameter 

estimation exist in C/C++ and are ready to go 

into operation (after a few weeks evaluation 

phase) 

• Exception handling routines need to be 

studied and developed during current run 


Klystron FSM 

W. Cichalewski, B. Koseda, P. Cieciura 

• Logical dependencies have 

been recognized and translated 

into a Matlab State Flow model 

• Model is used for final logic / 

security tests 

• Currently, the implementation 

into the doocs environment 

takes place 

• Application for automation of 

the bouncer power adjustment 

developed 


LLRF FSM: Single Pulse Identification 

What can be obtained from a single pulse 

(3rd June 04, SP=14, no feedback, no beam) 

Ideally: 

Full system identification 

including non linear 

behaviour of all 

subsystems (slow, not 

completely achieved so 

far) 

Loop phase is obtained 

at the beginning of the 

pulse 

Timeconstant gives 

bandwidth of the cavity, 

early quench indicator 

Phase slope at the end 

of the pulse gives the 

detuning 

Slope of feedforwardand 

probe-phase in 

combination give 

detuning at the 

beginning of the pulse 

filling time flattop klystron off 

Evaluation of LO 

Generation 

Determination of 

offsets- and crosstalk 

parameters. 


LLRF FSM: Adaptive Feedforward (Principles) 

Vectormodulator 

HP Amplifier 

Simple view of the VUV- 

FELs RF control system 

+ 

Feedforward 

FF(t) 

Set- 

Point 

SP(t) 

. . . 

•The Feedback suppresses 

errors wrt. setpoint-table by 

1/gain 

£ Gain 

- 

Cavities 

•Feedforward can further 

increase the field cavity 

Error signal: suppressed by 1/gain in closed loop 


LLRF FSM: Adaptive Feedforward (Definition) 

• Driving the cavity with Feedforward only should get close to the 

setpoint curve to make proportional Feedback efficent. 

• Adaptive Feedforward should be self-updating, since several outer 

parameters may changes (human or technical interventions). 

• Adaptive Feedforward might be derived in a self-evolving, iterative 

process for being independent of nonlinearities. 

• Adaptive Feedforward needs to be fast. 

Repetitive fluctuations 

in klystron high voltage: 


LLRF FSM: Adaptive Feedforward (Algorithm) 

1. It is easy do derive that the cavity (a bandpass) acts as a 

lowpass on the envelope signal, i.e. . The 

inversion is 

i.e. a proportional part and a differential part. 

2. Apply appropriate signal smoothing (justified by long 

timeconstant, 700µs, of the cavities). 

3. Apply the algorithm in an iterative fashion. 

Collect data of 10 Pulses 

and do some statistics 

Apply inverted lowpass filter 

on error signal (probe-setpoint) 

Add result to existing 

Feedforward table 


Adaptive Feedforward (Real Data, DSP, 20MV/m) 

The Algorithm was tested with Chechia 

with the old DSP System at 20MV/m. 

(A similar test was performed successfully 

with the new FPGA-System at 5MV/m.) 

. . . 


LLRF FSM: Exception Handling 

• Avoidance of exceptions by early diagnosis 

• Statistical evaluation of exeptions 

• Coordination of subsystems in case one 

subsystem fails 

• Fast start-up after ocurrence of exceptions 

• Finally: increase the availability 

• … experience with exeptions still is to gain during 

the next weeks of the current run 


Summary (Outlook) 

• The LLRF automation concept forsees hierarchy of state 

machines with seperate units for each control system 

• Implementation uses DOOCS and Matlab environment 

• Applications (e.g. adaptive feedforward) of the FSMs 

already were tested using Chechia (vertical teststand) and 

simulations 

• First VUV-FEL test of a set of LLRF/Klystron FSM will 

take place at ACC2/3, Klystron 5 

• Exception handling is subject of further investigation in 

this run 

• … a one-button machine remains the goal of FSM 

development ☺

Improvements in LLRF Control Algorithms and Automation - Desy

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