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| [[AdaptiveMachines/IdeasForFutureMachineLearning|Ideas for Future Things to Implement]] | |
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| === Ideas for Future Things to Implement === ==== Near Term (0 - 2 years) ==== * 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 |
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| * 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 |
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| ==== 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 SimPlant, check against the real plant. Probably we'd give it the general shape, but it would tweak filter shapes to match real IFO. |
{{attachment:memo120328.jpg|alt text = "meeting memo Mar 28, 2012"|width=400}} |
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| ==== 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 |
=== 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
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.