SURF 2015 lecture: Jun 24, 2015 Koji Arai – LIGO Laboratory / Caltech LIGO-G1500817-v1.

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Presentation transcript:

SURF 2015 lecture: Jun 24, 2015 Koji Arai – LIGO Laboratory / Caltech LIGO-G v1

2  A “system” A physical instance that has internal DOFs (states) Here we are interested in the response of the system i.e. excitation -> response Because... - we can develop a better understanding of the system (i.e. modeling) - it has a lot of applications Particularly, we are interested in Linear Time Invariant (LTI) systems G

3  Raw detector output ≠ GW waveforms We need to consider: - interferometer / sensor / actuator responses - signal conditioning filters - effect of feedback controls  Understanding the dynamics of various systems - mechanics, electronics, thermodynamics, optics,...  Designing feedback/feedforward control system  Signal processing: calibration / signal filtering G

4  “Filters” and “Transducers” If the input and output have: a same unit, the system is categorized as a “filter” - electrical filter (V->V, A->A), - mechanical filter (m->m), optical filter (E->E), - digital filter (number -> number) different units, the system is categorized as a “transducer” - force-to-displacement actuator (m/s^2->m), - electro-magnetic actuator (V->m/s^2), - displacement sensors (m->V) - electrostatic transducer (C (charge) -> N/m^2 (sound)) - transimpedance amplifier (A->V), current driver (V->A) - gravitational wave detector (“gravito-optic modulator”) G

5  LTI systems fullfil the following two conditions  Linearity (super position)  Time invariance H H System Input u(t) System Output y(t) G

6  Why do we limit the discussion within LTI systems?  They are simple, but still gives a lot of characteristics about them  Nonlinear systems can be reduced to a linear system at a local region of the state space (cf. a pendulum)  If change of the system states is slow, most of the LTI arguments are still applicable G

7  Impulse response: A way to characterize the system H:  Constructing an arbitrary response: (Convolution) G

8  Arbitrary response:  What is the system response to a sinusoidal excitation? Fourier Transform of h(t) G

9  Important consequences for LTI systems  Sinusoidal excitation induces sinusoidal response at the same frequency  The frequency response H(w) is, in fact, the fourier transform of the impulse response h(t) G

10  Impulse response:  Frequency response: (aka transfer function in freq domain)  Transfer function in Laplace domain s is a natural extension of “frequency” in a complex plane for most of the applications, we can just use G

Impulse response transfer function G

12  In many cases, an LTI system can be described by a linear ODE  It is easy to convert from an ODE to a transfer function  e.g. Forced oscillation of a damped oscillator Laplace Transform Fourier Transform G

13  e.g. Forced oscillation of a damped oscillator Bode diagram log-log log-lin G

14  e.g. RC filter cut-off freq f = 1/(2*pi*R*C) cut-off freq f = 1/(2*pi*R*C) log-log log-lin G

15  The transfer function of a system with an ODEcan be expressed as:  The roots of the numerator are called as “zeros” and the roots of the denominator are called as “poles”  Zeros (s zi ) and poles (s pi ) are real numbers (single zeros/poles) orpairs of complex conjugates (complex zeros/poles) (fundamental theorem of algebra) G

16  Poles rule the stability of the system! H(s) can be rewritten as (partial fraction decomposition)  Each term imposes exponential time impulse response  Therefore, if there is ANY pole with the response of the system diverges G

17  Poles rule the stability of the system!  Location of the pole (pair) and the impulse response Re(s) Im(s) Unstable response G

18  Modeling of the system: usually done in the freq. domain System Identification Tools: e.g. LISO, Vectfit G

19  Building blocks (“zpk” representation)  Single pole  Single zero  A pair of complex poles  A pair of complex zeros  Gain G

20  Summary  LTI systems  Description of the LTI system: Impulse response transfer function  Zero, Pole, Gain representation of transfer functions  Pole locations determine the stability of the system System identification G

21  Relationship between pole/zero locations and w0&Q  To be compared with G

22 G