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Message ID: 22     Entry time: Tue Aug 24 08:15:37 2021
 Author: Jiri Smetana Type: General Category: General Subject: Actuation Feedback Model

I'm posting a summary of the work I've done on the Lagrangian analysis of the Mariner suspension design and a state space model of the actuator control loop. The whole feedback mechanism can be understood with reference to the block diagram in attachment 1.

The dynamics of the suspension are contained within the Plant block. To obtain these, I derived the system Lagrangian, solved the Euler-Lagrange equations for each generalised coordinate and solved the set of simultaneous equations to get the transfer functions from each input parameter to each generalised coordinate. From these, I can obtain the transfer functions from each input to each observable output. In this case, I inserted horizontal ground motion at the pivot point (top of suspension) and a generic horizontal force applied to at the intermediate mass. These two drives become the two inputs to the Plant block. The two observables are xi - the position of the intermediate mass, which is sensed and fed to the actuator servo, and xt - the test mass position that we are most interested in. I obtained the transfer functions from each input to each output using a symbolic solver in Python and then constructed a MIMO state space representation of these transfer functions in MATLAB. For this initial investigation, I've modelled the suspension in the Lagrangian as a lossless point-mass double pendulum with two degrees of freedom - the angle to the horizontal of the first mass and the angle to the horizontal of the second mass. The transfer functions are very similar to the more advanced treatment with elastic restoring forces and moments of inertia and the system can always be expanded in a later analysis.

For the sensor block I assumed a very simple model given by

$x_s = G_s(x_i - x_g) + n_s$

where G_s is the conversion factor from the physical distance in metres to the electronic signal (in, for example, volts or ADC counts) and n_s is the added sensor noise. A more general sensor model can easily be added at a later date to account for, say, a diminishing sensor response over different frequency ranges.

The actuator block converts the measured displacement of the intermediate mass into an actuation force, with some added actuator noise. The servo transfer function can be tuned to whatever filter we find works best but for now I've made two quite basic suggestions: a simple servo that actuates on the velocity of the intermediate mass, given by

$\frac{F(s)}{x_s(s)} = G_as$

and an 'improved' servo, which includes a roll-off after the resonances, given by

$\frac{F(s)}{x_s(s)} = \frac{G_as}{(s-p)^2}$

where p is the pole frequency at which we want the roll-off to occur. Attachment 2 shows the two servo transfer functions for comparison.

The state space models can then be connected to close the loop and create a single state space model for the transfer functions of the ground and each noise source to the horizontal test mass displacement. Attachment 3 contains the transfer functions from xg to xt and shows the effect of closing the loop with the two servo choices compared to the transfer function through just the Plant alone. We can see that the closed loop system does damp away the resonances as we want for both servo choices. The basic servo, howerver, loses us a factor of 1/f^2 in suppression at high frequencies, as it approximates the effect of viscous damping. The improved servo gives us the damping but also recovers the original suppression at high frequencies due to the roll-off. I can now provide the ground and noise spectra and propagate them through to work out the fluctuations of the test mass position.

 Attachment 1: actuator_feedback_diagram.png  8 kB  Uploaded Tue Aug 24 10:02:13 2021
 Attachment 2: bode_servo.png  36 kB  Uploaded Tue Aug 24 10:02:34 2021
 Attachment 3: bode_plant.png  63 kB  Uploaded Tue Aug 24 10:02:48 2021
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