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Control of Large Scale Accelerator

Control of Large Scale Accelerator. Masanori Ikegami. Outline. Introduction Control system Feed-back and feed-forward Control of large scale accelerator Complexity Distribution in geometry Machine Protection System Personnel P rotection System. Accelerators to Control.

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Control of Large Scale Accelerator

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  1. ControlofLargeScaleAccelerator Masanori Ikegami

  2. Outline M. Ikegami, December 4 2018 PHY862 • Introduction • Control system • Feed-back and feed-forward • Control of large scale accelerator • Complexity • Distribution in geometry • Machine Protection System • Personnel Protection System

  3. Accelerators to Control • A number of large scale accelerators are in operation and in construction worldwide • Many of them are frontier accelerators intended to explore high energy or high intensity frontiers • Frontieraccelerators tend to be large in scale in seeking the highest energy or intensity in the world M. Ikegami, December 4 2018 PHY862

  4. Examples of Large Scale Accelerators[1] High Intensity Accelerators Materials, Life Science ADS RIB 104 Design value FRIB (CW) In operation PSI (CW) SPIRAL2 SNS 103 RIBF(U) SPES J-PARC RCS Beam Power Nuclear, Particle Physics TRIUMF ISIS LANSCE 1 MW 102 FAIR(U) 1 kW RIBF(O) ATLAS 100 kW FAIR(p) IPNS 10 J-PARC MR NSCL Average Beam Current (I·A/Q) [mA·u] AGS GANIL CERN-PS FNAL-MI RIBF(U) 100 SPS U70 Tevatron 10-1 10-2 102 103 10-2 10-1 100 10 104 Beam Kinetic Energy (E / A) [GeV/u] M. Ikegami, December 4 2018 PHY862

  5. Examples of Large Scale Accelerators[2] • FRIB is mainly taken as an example for large scale accelerator in this lecture M. Ikegami, December 4 2018 PHY862

  6. GoalsofAccelerators • Goals (or mission) of accelerators are different for each accelerator • In the case of FRIB, the goals for accelerators are to • Accelerate all stable ions to 200 MeV/u • Provide beam power on target of 400 kW M. Ikegami, December 4 2018 PHY862

  7. ElementsofAccelerator • Ion source to generate ions to accelerate • Power supplies and RF amplifiers to energizeionsource • RF cavities to accelerator the beam • RF amplifiers to energize RF cavities • Magnets to focus, bend, or steer the beam • Power supplies to energize magnets • Vacuum pumps to vacuum the beam path • Cooling water system to remove heat from RF cavities, magnets, etc. • Cryogenic system to keep superconducting elements to superconducting temperature • Beam diagnostics to measure properties of beam • Etc. M. Ikegami, December 4 2018 PHY862

  8. ScaleofAccelerator[1] • Elements of accelerator are distributed to wide area (order of 100 m to 1 km) geometrically M. Ikegami, December 4 2018 PHY862

  9. ScaleofAccelerator[2] • Elements of accelerator are controlled through network as they are distributed geometrically • The number of network devices (having network ports) are ~2,500 in the case of FRIB • Programmable Logic Controller (PLC), Process Variables, etc. are explained later M. Ikegami, December 4 2018 PHY862

  10. GoalofAcceleratorControlSystem • Goal of accelerator control system is to control elements of accelerator to achieve goals of accelerator • We need to define what “control” mean M. Ikegami, December 4 2018 PHY862

  11. SimpleExampleofControl[1] Spindle Balls Engine’s output shaft Valve stem Valve Steam Steam to seam engine Cut-away drawing of steam engine speed governor M. Ikegami, December 4 2018 PHY862 • Speed governor regulate rotating speed of output shaft driven by steam engine • The valve starts fully open at zero speed • As output shaft starts to rotate, central spindle starts to rotate • Balls starts to move outward and upward as they get kinetic energy • Central valve stem is forced downward and closes the valve • This is an example of feed-back control system

  12. SimpleExampleofControl[2] System Output Input Rate ofsteam provided to engine Speed of engine Steam engine M. Ikegami, December 4 2018 PHY862 • Speedgovernormaintain speed of engine by adjusting rate of steam provided to the engine measuring speed of engine by output shaft • Goal: Maintain speed of engine • Input: Rate of steam provided to the engine • Output: Speed of engine • Controlsystem adjusts “input” to affect “output” to meet its goal • System to be controlled converts “input” to “output”

  13. Feed-backandFeed-forward [1] Disturbance Output Input System M. Ikegami, December 4 2018 PHY862 Outputofsystemoftenaffectedby“disturbance”(inputs to the system not under control of control system) Disturbancemayhinder control system from achieving its goal

  14. Feed-backandFeed-forward [2] Disturbance Feed-forward Output Input System M. Ikegami, December 4 2018 PHY862 • A schemetodealwithdisturbanceis • Tomeasuredisturbance(or to predictdisturbance) • To understand how disturbance affects output • To adjust input to compensate the influence of disturbance to output • This scheme is called “feed-forward” • It is rare that control system can have enough information on disturbance and its correlation with output

  15. Feed-backandFeed-forward [3] Disturbance Output Input System Feed-back M. Ikegami, December 4 2018 PHY862 • Another schemetodealwithdisturbanceis • Tomeasureoutput • To compare measured output with target • To adjust input to reduce(inthecaseofnegativefeedback) the gap between measured output with target • This scheme is called “feed-back” • Feed-back control does not require knowledge on disturbance

  16. Examplefor Feed-backandFeed-forward Control in Accelerator Timing System Low Level RF Controller RF Amplifier RF Cavity (Resonator) Pickup loop M. Ikegami, December 4 2018 PHY862 PhaseandamplitudeofRFcavityshouldbestabletohavestablebeamenergyandbunchwidth Feed-backcontrol:PhaseandamplitudeofRFfieldinRFcavityismeasuredwithpickuploopandoutputfromLowLevelRF (LLRF)controllerisadjustedto compensate deviation from target values Feed-forward control: For high power pulse accelerator, known disturbance to RF field is beam. Using information from beam timing system, output from LLRF can be adjusted to compensate influence from beam

  17. PIDControl[1] M. Ikegami, December 4 2018 PHY862 • Systemtobecontrolledofteninclude“dynamics” • “Dynamics” in this context is attribute of system that action (input) in the past affects behavior (output) of the system in future • Time dependent nature of correlation between input and outputs • Dynamics can be complicated • Optimal behavior of controlledvariable • Regulation (disturbance rejection) – staying at given set point • Command tracking – implementing set point change • Due to dynamic nature of system, simple proportional feedback may not provide sufficient control Error e(t) = SP – PV(t) (SP: set point, PV: process variable, t: time) In proportional feedback, controller outputtocompensatethe error is u(t) = Kpe(t) (Kp: proportionalgain)

  18. PIDControl[2] FiguresfromWikipedia M. Ikegami, December 4 2018 PHY862 • WidelyusedfeedbackcontrolisPID(Proportional-Integral-Derivative) control Kp: proportionalgain, Ki: integral gain, Kd: Derivative gain • Example of PID gain tuning(stepresponse)

  19. PIDControl[3] M. Ikegami, December 4 2018 PHY862 • IdealresponseistohavePVtoexactlyfollowSPchange • Itisdifficulttorealizedduetodynamicnatureofsystem • Goal of PID tuning depends on the system • Goal may be fast response allowing overshooting • Goal may be to avoid overshooting tolerating slow response • Model-free tuning • Trial and error, based on system response to step change, etc. • Time consuming • Involve risk of inadequate setting • Model-based tuning • Model to simulate system response • Performance limited by accuracy of model

  20. Logic Control[1] M. Ikegami, December 4 2018 PHY862 • Controls treatedsofariscontinuouslymodulatedcontrol • Controlleroutputiscontinuouslymodulatedforregulationandcommandtrackingofcontrolledvariable • Anothercategoryofcontrolislogiccontrol • Example: • Onlywhen valve A is closed, operator can closevalve B • When valve C is closed, valve D is closed also • Historically implemented by interconnected electrical relays and cam timers using ladder logic • Logic controllers may respond to switches and sensors, and can cause the machinery to start and stop various operations through the use of actuators • Interlock to protect personnel or machinery is an important role of logic control • Logic control is often implemented in PLC (Programmable Logic Controller)

  21. LogicControl[2] M. Ikegami, December 4 2018 PHY862 • Ladderlogic • A "rung" in ladder represents a rule • A rung has contacts (checkers) that make or break circuits to control coils (actuators) • Each coil or contact corresponds to the status of a single bit in the programmable controller's memory • Each rung of ladder typically has one coil at the far right • If a path can be traced between the left side of the rung and the output, through asserted (true or "closed") contacts, the rung is true and the output coil storage bit is asserted (1) or true • If no path can be traced, then the output is false (0) • Rules are executed sequentially by software, in a continuous loop (scan). • Proper use of PLC requires understanding the limitations of the execution speed (scan time) and order of rungs

  22. LogicControl[3] M. Ikegami, December 4 2018 PHY862 • Simpleexampleofladderlogic -[ ]- : Normally open contact -[\]- : Normally closed (“not”) contact -( )- : Normally inactive coil -(\)- : Normally active (“not”) coil Logical AND -------[ ]-----------------------[ ]--------------------------( ) Key switch1 Key switch2 Door motor -------[ ]-----------------------[\]--------------------------( ) Key switch1 Obstacle Door motor Logical OR ---+------------[ ]------------+--------------------( ) | Exterior unlock |Door motor +------------[ ]------------+ Interior unlock

  23. ControlofAccelerator M. Ikegami, December 4 2018 PHY862 • Bothcontinuouslymodulatedcontrolandlogiccontrolareusedinaccelerator • Continuouslymodulatedcontrol: regulation and command tracking • Logic control: implementation of sequence, definition of allowed state change by operator, interlock to protect personnel/devices

  24. GoalofAcceleratorControlSystem • Goal of accelerator control system is to control elements of accelerator to achieve goals of accelerator • We need to define what “control” mean M. Ikegami, December 4 2018 PHY862

  25. GoalofAcceleratorControlSystem • Goal of accelerator control system is to control elements of accelerator to achieve goals of accelerator • Control here is defined to provideregulation (stability) and command tracking (tunability) for operation parameters and to implement operation sequence and interlock (protection of personnel and machinery) M. Ikegami, December 4 2018 PHY862

  26. Challengesin Control of Large Scale Accelerator [1] M. Ikegami, December 4 2018 PHY862 • Accelerator consists of wide variety of devices to control • RF amplifier • Power supply • Vacuum pump • Vacuum gauge • Gate valve • Data acquisition electronics for diagnostics • Actuator for insertion devices • Etc. • Devices have variety for interfaces to control • Required response time is different • Commercially-Off-The-Shelf (COTS) products often adopted to save cost • Updated devices and legacy devices are often mixed • Life time of accelerator facility is longer than replacement cycle of some devices

  27. Challengesin Control of Large Scale Accelerator [2] M. Ikegami, December 4 2018 PHY862 Devicestocontrolaregeometricallydistributed

  28. EPICS [1] Ref: https://epics-controls.org M. Ikegami, December 4 2018 PHY862 Oneofthesolutionswidely used to build control system for large scale accelerator is EPICS (Experimental Physics and Industrial Control System) EPICS is a set of software tools and applications which provide a software infrastructure for use in building distributed control systems to operate devices such as Particle Accelerators, Large Experiments and major Telescopes Input/Output Controllers (IOCs) perform real-world I/O and local control tasks, and publish this information asProcessVariable(PV)to clients using EPICS specific network protocols Channel Access

  29. EPICS[2] Workstation in control room Workstation in control room … Client level Channel Access over network IOC IOC IOC level … PLC LLRF controller Field level RF amplifier Gate valve Vacuum gauge M. Ikegami, December 4 2018 PHY862

  30. EPICS[3] M. Ikegami, December 4 2018 PHY862 • SimpleexampleofChannelAccess • ExampleofPVname • C1.current • BMP1.position • ExampleofPVnameinFRIB • LS1_CA01:PSOL_D1132:I_CSET • FE_MEBT:BPM_D1056:XPOS_RD • ExampleofChannelAccess command > caputC1.current1 > cagetBMP1.position

  31. EPICS[4] M. Ikegami, December 4 2018 PHY862 • Complexityfromvarietyof interfaces is absorbed by IOC • Device support developed for IOC • Standardizedprotocol (Channel Access)betweenclientandIOC • IOCcanbegeometricallydistributedto coverwidearea • IOCsare connected with network • Architecture is flexible and scalable • IOC and client can be added

  32. ExampleofAcceleratorControl:Beam TrajectoryCollection [1] • In real accelerator, injection beam has some deviation of beam trajectory from design trajectory • Some optical elements have transverse (perpendicular to design beam trajectory) kick to the beam that has deviation from design trajectory • It can cause larger deviation of beam trajectory, or beam loss at the worst case Beam loss Optical element Optical element Optical element Vacuum chamber wall Beam axis (Design beam trajectory) Beam orbit Vacuum chamber wall M. Ikegami, December 4 2018 PHY862

  33. ExampleofAcceleratorControl:Beam TrajectoryCollection [2] Beam loss Optical element Optical element Optical element Vacuum chamber wall Beam axis (Design beam trajectory) Beam orbit Vacuum chamber wall Corrector (C) C C BPM BPM BPM M. Ikegami, December 4 2018 PHY862 Beam Position Monitor (BPM) measure transverse location of beam center with respect to beam axis Corrector magnet provides transverse kick to beam to minimize deviation of beam trajectory from design trajectory

  34. ExampleofAcceleratorControl:Beam TrajectoryCollection [3] Beam loss Optical element Optical element Optical element Vacuum chamber wall Beam axis (Design beam trajectory) Beam orbit Vacuum chamber wall Corrector (C) C C BPM BPM BPM M. Ikegami, December 4 2018 PHY862 Beam Trajectory Collection:Adjuststrengthofcollectormagnettominimizedeviationofbeamtrajectoryfromdesigntrajectory

  35. ExampleofAcceleratorControl:Beam TrajectoryCollection [4] dBPM1:ChangeofBPM1readingbyapplying corrector M. Ikegami, December 4 2018 PHY862 • Example of model-free tuning: method to use response matrix • Determine BPM response to corrector setting (response matrix) experimentally > caget BPM1:position > cagetBPM2:position > cagetBPM3:position > caput C1:current 1 > cagetBPM1:position > caget BPM2:position > caget BPM3:position > caput C1:current 0 > caput C2:current 1 > caget BPM1:position > caget BPM2:position > caget BPM3:position …

  36. ExampleofAcceleratorControl:Beam TrajectoryCollection [5] or BPM1:Reading of BPM1without applying corrector M. Ikegami, December 4 2018 PHY862 • Example of model-free tuning: method to use response matrix (cont.) • Determine corrector setting to compensate initial beam trajectory deviation mathematically • Solve simultaneous equation or matrix equation • Set found corrector setting to correctors > caput C1:current 2.2 > caput C2:current -0.5 > caput C3:current 1.3

  37. ExampleofAcceleratorControl:Beam TrajectoryCollection [6] M. Ikegami, December 4 2018 PHY862 CharacteristicsofEPICS • Operator doesn’t have to care about interface to hardware • Complexityfromvarietyof interfaces is absorbed by IOC • Operator doesn’t have to care about location of hardware or IOC • IOCcanbegeometricallydistributedto coverwidearea • Operatordoesn’thavetocareaboutdynamicsinthesystem(orassumesregulationandcommandtracking) • ContinuouslymodulationcontrolisfunctioninginIOCorlowerlevel • Operator can extend the tuning sequence to more correctors and more BPMs • EPICSarchitecture is flexible and scalable

  38. ExampleofAcceleratorControl:Beam TrajectoryCollection [7] M. Ikegami, December 4 2018 PHY862 Characteristicsofmodel-freetuning • This method assumes linear response of beam position to corrector strength • In this method, operator needs to set trial current to corrector to obtain response matrix • As BPM has limited resolution, trial current should be large enough to measure change of beam position by corrector accurately • Too large trial current may cause beam loss • If beam loss occur before BPM, the response of the BPM to corrector strength will not be linear • It may take time if the number of correctors is increased • This method does not require detailed information on optical elements

  39. ExampleofAcceleratorControl:Beam TrajectoryCollection [8] Position and angle unknown Beam loss Optical element Optical element Optical element Vacuum chamber wall Beam axis (Design beam trajectory) Beam orbit Vacuum chamber wall Corrector (C) C C BPM BPM BPM • Example of model-based tuning: method to use on-line model • Propertiesofoptical elements (including location) and their functions are known • Unknown parameters are transverse position and angle to design trajectory of beam at certain location M. Ikegami, December 4 2018 PHY862

  40. ExampleofAcceleratorControl:Beam TrajectoryCollection [9] Position and angle unknown Beam loss Optical element Optical element Optical element Vacuum chamber wall Beam axis (Design beam trajectory) Beam orbit Vacuum chamber wall Corrector (C) C C BPM BPM BPM • Example of model-based tuning: method to use on-line model (cont.) • If you find transverse position and angle to design trajectory of beam at certain location, we can calculate strength of correctors to minimize trajectory deviationusingabeamopticssimulation M. Ikegami, December 4 2018 PHY862

  41. ExampleofAcceleratorControl:Beam TrajectoryCollection [10] Position and angle unknown Beam loss Optical element Optical element Optical element Vacuum chamber wall Beam axis (Design beam trajectory) Beam orbit Vacuum chamber wall Corrector (C) C C BPM BPM BPM • Example of model-based tuning: method to use on-line model (cont.) • You can find transverse position and angle to design trajectory of beam at certain location from readings of two or more BPMs using a beamopticssimulation M. Ikegami, December 4 2018 PHY862

  42. ExampleofAcceleratorControl:Beam TrajectoryCollection [11] M. Ikegami, December 4 2018 PHY862 • Example of model-based tuning: method to use on-line model (cont.) • Beamopticssimulation codes developed to use during beam operation is called “Online Model” • Traditionally Beam optics simulation codes are used before or after operation (off-line) as it took time to calculate • As faster computers have become available in control room, online model has become in wider use • Online Model must be fast enough to accomplish tuning in reasonable time • Accelerator usually has availability goal • Required accuracy depends on goals of intended tuning • Accuracy of monitor or stability of beam may determine tuning goal • Online Model is becoming indispensable to accelerator operation • FRIB presently has two online models developed • Linear beam envelope and beam center trajectory calculation (typical execution time 20-30 msec) • Particle tracking without space charge (typical execution time ~ 1 sec) • LANCE has a particle tracking code with space charge calculation on GPU

  43. ExampleofAcceleratorControl:Beam TrajectoryCollection [12] Known parameter Unknown parameter Initial beam position an angle Online Model Beam positions at BPMs Properties of optical elements Strength of correctors M. Ikegami, December 4 2018 PHY862 • Example of model-based tuning: method to use on-line model (cont.) • Obtain BMP reading experimentally > caget BPM1:position > caget BPM2:position > cagetBPM3:position • Find initial beam position and angle reproducing the experimentally measured BPM readings with Online Model (with help of optimization routines)

  44. ExampleofAcceleratorControl:Beam TrajectoryCollection [13] Known parameter Unknown parameter Initial beam position an angle Online Model Idealbeam positions at BPMs Properties of optical elements Strength of correctors M. Ikegami, December 4 2018 PHY862 • Example of model-based tuning: method to use on-line model (cont.) • Find strength of correctors minimize deviation of trajectory with Online Model (with help of optimization routines) • SetCorrectors strength experimentally > caput C1:current 2.2 > caput C2:current -0.5 > caput C3:current 1.3

  45. Computational Optimization Simpleexampleoftwoparameteroptimization M. Ikegami, December 4 2018 PHY862 • Optimization problem consists of maximizing or minimizing a real function by systematically choosing input values from within an allowed set and computing the value of the function • In the first step for trajectorycorrection: • Input parameter: initial beam position and angle • Evaluation function: difference between calculated beam position and measured position • In the second step of trajectorycorrection: • Input parameter: strengthofcorrectors • Evaluation function: difference between calculated beam position and ideal position • Variouscomputationaloptimizationtechniquesdeveloped and available • Computational optimization involves a number of evaluation of evaluation function, and it determines required computation time

  46. Online Model M. Ikegami, December 4 2018 PHY862 • Required accuracy for online mode depends on the intended tuning • If the goal of trajectory tuning is 0.1 mm, required accuracy for online model for beam center position will be around 0.01 mm • Online model must be fast enough to be used with computational optimization routine • Computational optimization routine require a large number of evaluations (order of 100 or more) • It will not be practical for on-line use if optimization routine cannot provide solution in minutes • Online model is becoming indispensable tool for accelerator tuning • Historically,many of beam optics simulation codes are developed to design optics for accelerator, and the same codes are used to support beam tuning • Recently, we started to have beam optics simulation codes dedicated for online purpose • Expanding scope of online model by introducing high performance computation (such as GPU computation) into control room

  47. ExampleofAcceleratorControl:Beam TrajectoryCollection [14] M. Ikegami, December 4 2018 PHY862 • Characteristics ofmodel-based tuning • This method doesnotnecessarilyassumes linear response of beam position to corrector strength (depends on model) • In this method, operator doesnot set trial current to corrector experimentally • It may take time if the number of evaluations in optimization routine is large and online model take time to evaluate the function • This method requires detailed information on optical elements

  48. ExampleofAcceleratorControl:Stabilization of Beam Energy [1] Controller Controller Controller RF amplifier RF amplifier RF amplifier … RF cavity RF cavity RF cavity Beam M. Ikegami, December 4 2018 PHY862 • Output beam energy from accelerator has fluctuation or drift due to disturbances • Phase and amplitude of RF amplitude for each RF cavity has feed back control system • After acceleration with multiple RF cavities, the beam energy has fluctuation with small residuals of feed back control accumulated

  49. ExampleofAcceleratorControl:Stabilization of Beam Energy [2] Controller Controller Controller RF amplifier RF amplifier RF amplifier … RF cavity RF cavity RF cavity Energy monitor Beam M. Ikegami, December 4 2018 PHY862 • Straightforward way to stabilize beam is to have additional feedback loop to the last RF cavity with measuring the output beam energy • This approach does not assume model for the effect of disturbance to the output beam energy • There is a possibility to improve stability by introducing a model

  50. ExampleofAcceleratorControl:Stabilization of Beam Energy [3] M. Ikegami, December 4 2018 PHY862 • Accelerator is a complex system consists of a large number of coupled sub-systems • Each sub-system is affected by fluctuations to cause fluctuation of beam quality • It is a challenge to identify dominant cause for fluctuation of beam quality analyzing interdependencies • Example of interdependencies • If room temperature fluctuates (due to weather change for example), it may activate HVAC system • Activation of HVAC system may affect voltage of power line • Fluctuation of voltage of power line mayaffect gain of RF amplifier • Change of gain of RF amplifier may affect heat load of dummy load (to absorb reflected RF power) • Change of heat load of dummy load may increase load to cooling water system • Increase of load to cooling water system may cause fluctuation of voltage of power line

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