Conditioning of the SPring-8 Linac RF System Using Fuzzy Logic
Hironao SAKAKI, Toshihiko HORI, Hiroshi YOSHIKAWA, Shinsuke SUZUKI,Tsutomu TANIUCHI, Atsushi KUBA, and Hideaki YOKOMIZOJAERI-RIKEN SPring-8 project team,
Kamigori-cho, Ako-gun, Hyogo, 678-12 JAPAN
Abstract
In present accelerators it is necessary to construct advanced remote control systems. This is because the facility
becomes huge and high quality beams are always specified. Under such situations the accelerator must be operated by
setting precisely quantitative parameters obtained from accelerator science (quantitative control). However excellent
beam characterizations are obtained not only from the quantitative control but also from the hands-on tuning provided
by accelerator experts or operators (manual control). Manual control is usually qualitative and is done with uncertainty.
The computer is not good at such control, so that experts or operators must aid the computer with their knowledge.
If the computer system were able to embed manual control, the required beam could be obtained with much easieradjustment of the system. Such would be an Artificial Intelligence (AI) control system. At the SPring-8 Linac we haveundertaken the construction of an AI system, using fuzzy logic theory. In this theory manual control is expressed by aset of linguistic rules of the form IF-THEN, plus numerical membership functions. For our first attempt we apply fuzzy
logic for conditioning of the RF system. This fuzzy logic system is based on VME computers.

I. Introduction
The commissioning of the SPring-8 1 GeV injector Linac will start in August 1996. Before commissioning starts, allconditioning of Linac components will have to finish efficiently. We have designed and constructed the Linac controlsystem [1] [2] and we are now trying to begin remote control of conditioning. Special mention should be made of theconditioning of the high voltage Radio Frequency (RF) system. Because we have chosen 80 MW klystrons, (ToshibaE3712, 2856 MHz) the RF system will have the largest average RF power in the world. The number of E3712s is 13,and we usually drive one at about ∼60 MW, ∼4 msec and ∼60 pps. We must achieve near this power in a conditioning
period from May to July 1996, a very short time. Thus, we plan that the RF conditioning will be under the AI control
system, and hope to achieve a reduction in conditioning hours.
The chief problem in RF conditioning involves electric discharges, a difficult phenomenon, and best handled with theexperience of experts. RF conditioning system is best directed by experience, just the situation for an AI control system.
If an AI control system is adopted with fuzzy logic, then it might be able to simulate the behavior of the manual control.
Thus, we shall try to control the conditioning of the RF system using the experience and sense embedded in fuzzy logic,
in the hope of conditioning the RF system with high efficiency.

II. High power conditioning test of the wave guide circuit
Before design of the conditioning of the RF system using fuzzy logic simulation, the high power conditioning test of thewave guide circuit (the RF conditioning test ) was carried out [3]. The test unit of a wave guide system is composed of
RF windows, 3dB directional couplers, vacuum pumps and phase shifters.
The test unit is shown in Figure 1, and the RF conditioning test is summarized in Figure 2. After 420 hours a stable
operation with maximum RF power (80 MW - 4 msec - 60 pps) was realized. However, this test unit does not connect
to accelerator tubes. When it is, we will have longer conditioning times (500– 550 hours per one unit). We shall try
to realize conditioning times of under 500 hours using fuzzy logic control. This test was very helpful in making fuzzy
production rules for our AI control system.

III. Fuzzy logic system
A. Outline of fuzzy logic and the fuzzy logic engine board
The characteristics of fuzzy logic are shown in Table I, in comparison with PID (Proportional, Integral, Derivative) control.
What is seen is that fuzzy logic control is good at linear and non-linear multi-parameter systems using experiential rules.

Klystron(E3712 80MW)

AV
CCG

CCG: cold cathode vacuum gage
IP:ion vacuum pump
DC: directional coupler
CCD:monitor camera
AV: valveIP(50l/s)RF-window
CCD

CCD
AV
AV

DC
CCG
DL
3dB
directional coupler

DC

CCD

CCD

DC

CCG

CCD

CCG

DLDL

AV
IP
(50l/s)phase
shifterIP(50l/s)Figure. 1. High Power test bench

100

580

460

340

220

10Pulse Width (micro sec)

Peak Power (MW)
Repetition (pps)

Peak Power
RepetitionPulse Width00100200300
Time (hour)

400Figure. 2. Summary of the RF conditioning test
As conditioning of the RF system is quite experiential, involving avoidance of electric discharge, it is difficult to make aPID model for its control. However, we think that it is suitable for fuzzy logic control. There are some fuzzy engine boardson the VMEbus market, able to calculate fuzzy logic at high speed. We are going to adopt one, EVME-FZY21(ELNISJAPAN), for our VME computer control system, and have already written its device drivers in C. Under fuzzy logic, asystem is controlled by qualitative rules, based on linguistic and unclear values, such as “TIME = little,” where the ruleparameters have no particular thresholds. The board has an easy rule editor, so that we have been able to model and set
up the fuzzy production rules.
As to the fuzzy logic calculations, this board has two types of defuzzifier, a Center of Gravity (CG) method, and a Height
method. Of these we have chosen the CG method for our engine. During conditioning, the system will be as shown inFigure 3.
B. Modeling of the RF conditioning
We must model the conditioning of our RF system. All of an operator’s judgments are influenced by experience in suchwork. First of all we state a new experiential guideline which we call “RF power safety (safety)”.

Table I

Comparison of fuzzy and PID control
characteristic

fuzzy

the subject of control: model: qualitative relation

unnecessity

necessity

experiential

unnecessity

possiblepossiblepossiblepossible

little

many

good

bad

structure of control systemcomplexsimplechangeover control

sequence

nonsequenceknowledge control

fit

unfit

one or many

one set onlyoperation: speed type
: positional type: saturation
read a rulenumber of input, output

PID

1. If it is zero, then the peak power agrees with the RF conditioning test value (experiential value).
2. If it is a negative large value (safety=bad), then the peak power does not agree with the experiential value, and it isabove the “safety = zero” line. There is a significant possibility of electric discharge.
3. If it is a positive large value (safety=good), then the peak power does not agree with the experiential value and isunder the “safety = zero” line. There is little possibility of electric discharge.
Safety is used for judgment of the RF peak power value. Figure 4 shows the judgment of RF power safety. Under thisguideline, we have modeled that there is a two step judgment in the RF conditioning process.
Step 1: Safety is obtained from the results of RF conditioning tests. It requires two input variables.
a. Time interval is small or large.
b. Peak power is low or high.
Step 2: We judge the peak power steps according to the present safety. Three input variables are required.
a. Safety is good or not
b. RF pulse width is short or longc. Vacuum value is good or not
These variables are used to make the fuzzy production rules and membership functions. Figure 5 shows the block diagramof the RF conditioning system. The following subsection discusses in detail the construction of fuzzy production rules.
C. Fuzzy production rules and membership functions
We have made 52 rules and 6 membership functions from models of conditioning.. Generally speaking, this control
systems should be designed to be fail-safe. Our high power RF system, having such a large average power, has thepotential to cause severe equipment damage in the case of an accident. Figures 6 and 7 show the fuzzy production rules
and membership functions created.
Each membership function has the following definition.
A) Fuzzy inference Step 1
1. Definition of peak power and time on the RF conditioning test.
There are 5 levels for raising the peak power, 0−10 MW (0−100 hours), 30 MW (∼110 hours), 50 MW (∼120 hours ), 60
MW (∼150 hours), and 80 MW (∼180 hours). These regions have greater possibility of electric discharge. Thus we must
be careful in these regions not to hurry up input power increases. This is reflected in the definitions of the membershipfunctions in these regions.
2. The definition of safety peak power has 5 regions, so safety also has 5 regions.
As the system must be kept fail-safe, the membership function of safety leans toward the negative.

Attenuator
30dB

Directional
Coupler

Wave detector
Accelerator tube

TWT
Amp.

MasterOscllator

Timing unit

Low-pass Fileter

Klystron
MasterTrigger
Unit

Modulator
PFN Volt
Timing unit

15.3 kV

E/Ofor PFN Setting
OSClogging
126HDD

Vaccum and Temp Monitors

Time(Internal Clock)Operator

Vacuum1
Vacuum2
Water Temp

VME ( FUZZY Control System )Figure. 3. Design of the conditioning system using fuzzy logic
ResultResultofofconditioning
conditioning test
test

RF power safety
bad90

Peak
Peak power
power adjustment
adjustment

8070

MW

60
50
403020safty = zero line

10RF power safety
good0050

100

150

200

250

Hour

Figure. 4. Judgment of the ConditioningB) Fuzzy inference Step 2
1. Definition of Vacuum
During RF conditioning we want to keep the vacuum value about 1−3x10−7 torr. The vacuum interlock set level shouldbe ∼5x10−7 torr.
2. Definition of Pulse Width
As pulse width increases electric discharges occur more frequently. We must be especially careful for pulses longer than2 msec.
3. Definition of Step
Step is the control parameter of RF power adjustment. If step is positive and large, then the RF peak power is getting
large. For example, if the vacuum value is getting bad then the step must get negative quickly, as we must keep thesystem fail-safe.

rules and membership functions for "safety"
output RF power
fuzzy inference

time

1step

rules and membership functions for " RF power step"safetyfuzzy inference

Pulse width

RF power step

Modulator
Kly

vacuum value

2step

Vacuumpump

Figure. 5. Block diagram of the RF conditioning system
Table II

Result of the step2 fuzzy inference

Peak power (MW)1040404040406080

18018030

18018018050

200

vacuum value (x10^-10 torr) 1000

1000

1000

1000

100

4000

1000

1000

pulse width (µsec)

0.50.50.5310.50.51

safety value

113-35333-350power step

75-647-26-6

1Time(hour)

IV. Simulation
The RF conditioning was simulated using these fuzzy production rules and membership functions. Figure 8 shows theresult of step 1 fuzzy inference. A simulation was performed relating passage of time and peak power. In this figure theopen squares show “safety = zero” from the simulation. Using our fuzzy production rules and membership functions, thesimulation is in good agreement with the RF conditioning curve. Table II shows the results of step 2 fuzzy inference. For
example, if peak power is 10 MW, time is 180 hour, vacuum is 10−7 torr, and pulse width is 0.5 msec while the passage
time is very long, then peak power is not large, so that the power steps can be given a positive large value (=7). If vacuum
is too bad, ∼5x10−7 torr, the power steps are large and negative (value = -26).
As we want to have a minimal discharge system, the peak power is lowered quickly. When peak power of 80 MW isachieved, the power step is near zero. In this case the system does not require more RF power.

V. Conclusion
We have constructed and simulated an RF conditioningsystem using fuzzy logic. In May 1996 we will use it in production,and hope to achieve conditioning times under 500 hours. First we will do the RF conditioning test (local operation) of
only one RF unit (with accelerator tubes) for recheck of the RF system. Though there may be some accidents in thischeckout, the fuzzy logic control system will reflect this. In the future, the membership functions will be learned byneural control, and thus the system will become more intelligent. Later we are going to gather many fuzzy production
rules on Linac operation, for example, phase control, beam energy control, etc. These rules will be managed using a222

membership functionof peak-power

membership functionof safety

membership functionof time

Step 1: Output variable

Step 1:
Input variable

Step 1:
Input variable

Step 2:

1

Input variable

10000

10203040MW

5060

7080065130

1952603252

Step 2:

Step 2:

Input variable

Input variable

04000

5000-5515

Step 2:
Output variable

003000-15safety

112000-25

membership functionof step

membership functionof pulse width

11000-35

22

membership functionof vacuum

0-45

hour

010 -10 torr

10203040-45-35

pulse width (10 -5sec)-25-15step-5515

Figure. 6. Membership functions of the RF conditioning
database and employed on the AI control system.

References[1] H.Yoshikawa et al., Nucl. Instr. and Meth. A, 352,216-217 (1994)[2] H.Sakaki et al., Proc. of Particle Accelerator Conference and International Conference of High-energy Accelerators,Dallas, Texas USA (1995)[3] S.Suzuki et al., Proc. of 10th Accel. sci. and Tech., Hitachinaka, Japan, Oct. 25-27, 1995, to be published.

Fuzzy inference Step 1: 25 rules
If PEAK = quite low
If PEAK = quite low
If PEAK = quite low
If PEAK = quite low
If PEAK = quite low
If PEAK = low
If PEAK = low& TIME = littlethen& TIME = zerothen
& TIME = bigthen& TIME = quite big then
& TIME = too big then
& TIME = littlethen& TIME = zerothen::
If PEAK = quite high & TIME = quite big thenIf PEAK = quite high & TIME = too big then

STABILITY = good
STABILITY = positive small
STABILITY = positive big
STABILITY = positive big
STABILITY = positive big
STABILITY = negative small
STABILITY = good
STABILITY = good
STABILITY = positive small

Fuzzy inference Step 2 : 27 rules
If STABILITY = negative big & VACUUM = too bad
If STABILITY = negative big & VACUUM = quite bad
If STABILITY = negative big & VACUUM = good
If STABILITY = negative big & VACUUM = quite good
If STABILITY = negative big & VACUUM = excellence
If STABILITY = negative small & VACUUM = too bad::
If STABILITY = positive big& VACUUM = quite good
If STABILITY = positive big& VACUUM = excellence

then STEP = negative big
then STEP = negative smallthen STEP = negative smallthen STEP = goodthen STEP = positive smallthen STEP = negative big
then STEP = positive big
then STEP = positive big

If PULSE_WIDTH = short then STEP = positive small
If PULSE_WIDTH = wide then STEP = zero
Figure. 7. Rules of the RF conditioning

Conditoning Test
Safety=zero (Fuzzy Simulation)

908070MW6050403020100050100150
Hour

200250Figure. 8. Result of the step1 fuzzy inference