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Verification of the Design of the Beam-based Controller

Verification of the Design of the Beam-based Controller. Jürgen Pfingstner 31. March 2009. Content. Analysis of the contoller with standard control engineering techniques Uncertanty studies of the response matix and the according control performance.

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Verification of the Design of the Beam-based Controller

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  1. Verification of the Design of the Beam-based Controller Jürgen Pfingstner 31. March 2009

  2. Content • Analysis of the contoller with standard control engineering techniques • Uncertanty studies of the response matix and the according control performance

  3. Analysis of the contoller with standard control engineering techniques • Daniel developed a controller, with common sense and feeling for the system • I tried to verify this intuitive design with, more abstract and standardized methods: • Standard nomenclature • z – transformation • Time-discrete transfer functions • Pole-zero plots

  4. The model of the accelerator 1.) Perfect aligned beam line • a.) 2 times xi -> 2 times amplitude • -> 2 times yj • b.) xi and xj are independent • Linear system without ‘memory’ Laser-straight beam QPi BPMi 2.) One misaligned QP Betatron- oscillation (given by the beta function of the lattice) y … vector of BPM readings x … vector of the QP displacements R … response matrix xi xj(=0) yi yj

  5. Mathematical model of the controlled system ni ui ui+1 G(z) z-1 vi C(z) xi+1 yi xi ri z-1 (R*)-1 + R + + + - ui, ui+1 … controller state variables xi, xi+1 … plant state variables (QP position) C(z) … Controller G(z) … Plant ri … set value (0) yi, … BPM measurements vi … ground motion ni … BPM noise

  6. System elements(SISO analogon!) • I controller • simple • allpass • non minimum phase Be aware about the mathematical not correct writing of the TF (matrix instead of scalar)

  7. Stability and Performance • Stability • necessary attenuation at high frequencies • all poles at zero • Performance of the interesting transfer functions

  8. Important transfer functions (error behaviour) (set point following)

  9. Conclusions • Controller is: • very stable and robust (all poles at zero) • integrating behavior (errors will die out) • good general performance • simple (in most cases a good sign for robustness) • seams to be a very good design in general Further work • H∞ optimal control design, to double check

  10. Uncertanty studies of the response matix and the according control performance Controller is robust, but is it robust enough? yes no done • Plan A: • Use methods from robust control to adjust the controller to the properties of the uncertainties (e.g. pole shift) • Plan B: • Use adaptive control techniques to estimate R first and than control accordingly • Answer to that question by simulations in PLACET

  11. Tests in PLACET • Script in PLACET where the following disturbances can be switched on and off: • Initial energy Einit • Energy spread ΔE • QP gradient jitter and systematic errors • Acceleration gradient and phase jitter • BPM noise and failures • Corrector errors • Ground motion • Additional PLACET function PhaseAdvance • 2 Test series (Robustness according to machine drift): • Robustness regarding to machine imperfection with perfect controller model • Robustness regarding to controller model imperfections with perfect machine • Analysis of the controller performance and the resulting R

  12. Results *1 … Values are according to the multi-pulse projected emittance *2 … Failed BPMs that deliver zeros are worse then the one giving random values.

  13. Measures for the performance • Goal: Find properties of Rdist that correspond with the controller performance evnorm … eigenvalue norm L … matrix that determines the poles of the control loop Rdist … disturbed matrix Rnom … nominal matrix absnorm … absolut matrix norm

  14. Information from absnorm and evnorm • evnorm absnorm • Controller works well for: • absnorm = 0.0 – 0.4 • Controller works well for: • evnorm = 0.5 – 1.0

  15. Conclusions • Controller is very robust against disturbances • Also without simulations we can say, how good the controller performs, with the defined measures.

  16. Thank you for your attention!

  17. z – Transformation • Method to solve recursive equations • Equivalent to the Laplace – transformation for time-discrete, linear systems • Allows frequency domain analysis z – transfer function G(z) Discrete Bode plot Discrete System

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