Differences between revisions 5 and 11 (spanning 6 versions)
Revision 5 as of 2012-06-12 02:34:49
Size: 3735
Comment:
Revision 11 as of 2012-07-27 02:44:59
Size: 958
Comment:
Deletions are marked like this. Additions are marked like this.
Line 5: Line 5:

=== Ideas for Future Things to Implement ===
==== Near Term (0 - 2 years) ====
 * Frame work: State Space implementation of the servo
   * What is the motivation to use the SS model???
 * Frame work: Simulink modeling of modern control
 * Frame work: Simulated plant for rapid development of control system
 * Frame work: Unification of modern control theory and simulated plant => integrated control development environment

 * 40m: Modern control realization of local control (OSEM/OPLEV/QUAD) w/o or w estimators.
 * 40m: combination of local/global control (LSC/ASC/TCS?)

 * 40m: implement LMS based FF for seismic subtraction on multi DOF for LSC controls
 * 40m: adaptive subtraction of OL controls to reduce angle to length coupling noise
 * 40m: static + adaptive subtraction of aux. controls (PRC/SRC/MICH) from DARM
 * 40m: acoustic noise subtraction from MC_F / IFO common mode
 * 40m: optimal feedback design for simple loops based on cost function

 * adaptive adjustment of input/output matrix
 * modern control version of ASS

 * LIGO: copy/paste of eLIGO FF to multi length DOFs
 * LIGO: adaptive subtraction of WFS controls to reduce angle to length coupling noise (ala Dooley)
 * LIGO: static + adaptive subtraction of aux. controls (PRC/SRC/MICH) from DARM
 * LIGO: optimal feedback design for simple loops based on cost function
   * Optimization of the hieracheal control
   * Optimal control of the ISI/SUS combined system

 * Nonlinear feedback control (e.g. lock acquisition)
 * Bilinear noise cancellation (static/adaptive)
[[AdaptiveMachines/IdeasForFutureMachineLearning|Ideas for Future Things to Implement]]
Line 37: Line 8:
==== Medium Term (2 - 5 years) ====
 * PEM channel based feedback states (train mode, EQ mode)
 * Training of LSC/ASC loop shapes to minimize glitch rate at low frequencies (I have no idea how this would be implemented)
 * Optimal length of time to "rest" between locking sequences
 * TCS heating - how much TCS for how much PSL power, and for how long
 * SUS input matrix - constant update??
 * Know when something (oplevs, bad servo, etc.) is kicking something up. Turn down gain / fix loop, or just turn off until it's fixed.
 * Build a simulated IFO plant, check against the real plant. Probably we'd give it the general shape, but it would tweak filter shapes to match real IFO.

==== Long Term (5 - 10 years) ====
 * Learning system uses free hanging IFO time series to design its own lock acquisition algorithms
 * Online simulation uses actual PEM inputs to predict glitch rates, does MCMC to find optimal feedback/feedforward solutions and then tries them out on the real system to learn more
 * Large array of seismic / acoustic sensors placed all over the vacuum system can be used to find sources of backscattered light / phase noise
 * Machine has list of people / skills. Can send SMS to ask for help
 * Pre-emptive prediction of failure of facility systems (e.g. power line fluctuations, weather related power failures, vibrations in HVAC indicating fan bearing failures,...)
 * monotonic increase in environmental coupling indicates optics getting dirty, photodiodes getting damaged, etc.
 * bad IFO alignment = operator getting sleepy
 * Initial alignment of IFO, even before there is any flashing in cavities
Line 57: Line 10:

=== Papers and Technical Notes that we should all read ===

Notes on American Controls Conference 2012: [[https://dcc.ligo.org/cgi-bin/private/DocDB/ShowDocument?docid=94047|T1200337]]

[[http://rmp.aps.org/abstract/RMP/v77/i3/p783_1|Feedback for physicists: A tutorial essay on control]] Section III.D.1 is an example of LIGO as a device under control.

Coalition to Make LIGO Sentient

Our goal is to collect a bunch of concrete ideas for what (adaptive control / advanced control techniques / machine learning techniques) we can/should do in the near term as well as what we should start to think about for the longer term. The near term output of this brainstorming will be a white paper that we write on the topic in collaboration with MIT.

Ideas for Future Things to Implement

alt text = "meeting memo Mar 28, 2012"

Papers and Technical Notes that we should all read

Notes on American Controls Conference 2012: T1200337

Feedback for physicists: A tutorial essay on control Section III.D.1 is an example of LIGO as a device under control.

AdaptiveMachines (last edited 2013-09-23 01:18:18 by RanaadhikariATligoDOTorg)