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

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 (RANA)

• 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 hierarchical control
  • Optimal control of the ISI/SUS combined system
• Nonlinear feedback control (e.g. lock acquisition)
• Bilinear noise cancellation (static/adaptive)

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

Notes on American Controls Conference 2012: T1200337