From 586a5983fef21da8d66a57531bfd50a12c2c4a39 Mon Sep 17 00:00:00 2001 From: Thomas Dehaeze Date: Tue, 2 Feb 2021 19:13:50 +0100 Subject: [PATCH] Simlink index.html --- index.html | 1829 +--------------------------------------------------- 1 file changed, 1 insertion(+), 1828 deletions(-) mode change 100644 => 120000 index.html diff --git a/index.html b/index.html deleted file mode 100644 index 093e5df..0000000 --- a/index.html +++ /dev/null @@ -1,1828 +0,0 @@ - - - - - - -Sensor Fusion - Test Bench - - - - - - - - -
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Sensor Fusion - Test Bench

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Table of Contents

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This report is also available as a pdf.

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-In this document, we wish the experimentally validate sensor fusion of inertial sensors. -

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-This document is divided into the following sections: -

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1 Experimental Setup

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-The goal of this experimental setup is to experimentally merge inertial sensors. -To merge the sensors, optimal and robust complementary filters are designed. -

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-A schematic of the test-bench used is shown in Figure 1 and a picture of it is shown in Figure 2. -

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exp_setup_sensor_fusion.png -

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Figure 1: Schematic of the test-bench

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test-bench-sensor-fusion-picture.png -

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Figure 2: Picture of the test-bench

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-Two inertial sensors are used: -

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  • An vertical accelerometer PCB 393B05 (doc)
    • -
  • A vertical geophone Mark Product L-22
    • -
- -

-Basic characteristics of both sensors are shown in Tables 1 and 2. -

- - - - --- -- - - - - - - - - - - - - - - - - - - - - - - -
Table 1: Accelerometer (393B05) Specifications
SpecificationValue
Sensitivity1.02 [V/(m/s2)]
Resonant Frequency> 2.5 [kHz]
Resolution (1 to 10kHz)0.00004 [m/s2 rms]
- - - - --- -- - - - - - - - - - - - - - - - - - -
Table 2: Geophone (L22) Specifications
SpecificationValue
SensitivityTo be measured [V/(m/s)]
Resonant Frequency2 [Hz]
- -

-The ADC used are the IO131 Speedgoat module (link) with a 16bit resolution over +/- 10V. -

- -

-The geophone signals are amplified using a DLPVA-100-B-D voltage amplified from Femto (doc). -The force sensor signal is amplified using a Low Noise Voltage Preamplifier from Ametek (doc). -

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-The excitation signal is amplified by a linear amplified from Cedrat (LA75B) with a gain equals to 20 (doc). -

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-Geophone electronics: -

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  • gain: 10 (20dB)
    • -
  • low pass filter: 1.5Hz
    • -
  • hifh pass filter: 100kHz (2nd order)
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-Force Sensor electronics: -

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  • gain: 10 (20dB)
    • -
  • low pass filter: 1st order at 3Hz
    • -
  • high pass filter: 1st order at 30kHz
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2 First identification of the system

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-In this section, a first identification of each elements of the system is performed. -This include the dynamics from the actuator to the force sensor, interferometer and inertial sensors. -

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-Each of the dynamics is compared with the dynamics identified form a Simscape model. -

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2.1 Load Data

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-The data is loaded in the Matlab workspace. -

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  id_ol = load('identification_noise_bis.mat', 'd', 'acc_1', 'acc_2', 'geo_1', 'geo_2', 'f_meas', 'u', 't');
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-Then, any offset is removed. -

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  id_ol.d      = detrend(id_ol.d, 0);
-  id_ol.acc_1  = detrend(id_ol.acc_1, 0);
-  id_ol.acc_2  = detrend(id_ol.acc_2, 0);
-  id_ol.geo_1  = detrend(id_ol.geo_1, 0);
-  id_ol.geo_2  = detrend(id_ol.geo_2, 0);
-  id_ol.f_meas = detrend(id_ol.f_meas, 0);
-  id_ol.u      = detrend(id_ol.u, 0);
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2.2 Excitation Signal

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-The generated voltage used to excite the system is a white noise and can be seen in Figure 3. -

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excitation_signal_first_identification.png -

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Figure 3: Voltage excitation signal

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2.3 Identified Plant

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-The transfer function from the excitation voltage to the mass displacement and to the force sensor stack voltage are identified using the tfestimate command. -

- -
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  Ts = id_ol.t(2) - id_ol.t(1);
-  win = hann(ceil(10/Ts));
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- -
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  [tf_fmeas_est, f] = tfestimate(id_ol.u, id_ol.f_meas, win, [], [], 1/Ts); % [V/V]
-  [tf_G_ol_est,  ~] = tfestimate(id_ol.u, id_ol.d, win, [], [], 1/Ts); % [m/V]
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- -

-The bode plots of the obtained dynamics are shown in Figures 4 and 5. -

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force_sensor_bode_plot.png -

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Figure 4: Bode plot of the dynamics from excitation voltage to measured force sensor stack voltage

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displacement_sensor_bode_plot.png -

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Figure 5: Bode plot of the dynamics from excitation voltage to displacement of the mass as measured by the interferometer

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2.4 Simscape Model - Comparison

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-A simscape model representing the test-bench has been developed. -The same transfer functions as the one identified using the test-bench can be obtained thanks to the simscape model. -

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-They are compared in Figure 6 and 7. -It is shown that there is a good agreement between the model and the experiment. -

- -
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  load('piezo_amplified_3d.mat', 'int_xyz', 'int_i', 'n_xyz', 'n_i', 'nodes', 'M', 'K');
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simscape_comp_iff_plant.png -

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Figure 6: Comparison of the dynamics from excitation voltage to measured force sensor stack voltage - Identified dynamics and Simscape Model

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simscape_comp_disp_plant.png -

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Figure 7: Comparison of the dynamics from excitation voltage to measured mass displacement - Identified dynamics and Simscape Model

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2.5 Integral Force Feedback

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-The force sensor stack can be used to damp the system. -This makes the system easier to excite properly without too much amplification near resonances. -

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-This is done thanks to the integral force feedback control architecture. -

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-The force sensor stack signal is integrated (or rather low pass filtered) and fed back to the force sensor stacks. -

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-The low pass filter used as the controller is defined below: -

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  Kiff = 102/(s + 2*pi*2);
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-The integral force feedback control strategy is applied to the simscape model as well as to the real test bench. -

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-The damped system is then identified again using a noise excitation. -

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-The data is loaded into Matlab and any offset is removed. -

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  id_cl = load('identification_noise_iff_bis.mat', 'd', 'acc_1', 'acc_2', 'geo_1', 'geo_2', 'f_meas', 'u', 't');
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-  id_cl.d      = detrend(id_cl.d, 0);
-  id_cl.acc_1  = detrend(id_cl.acc_1, 0);
-  id_cl.acc_2  = detrend(id_cl.acc_2, 0);
-  id_cl.geo_1  = detrend(id_cl.geo_1, 0);
-  id_cl.geo_2  = detrend(id_cl.geo_2, 0);
-  id_cl.f_meas = detrend(id_cl.f_meas, 0);
-  id_cl.u      = detrend(id_cl.u, 0);
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-The transfer functions are estimated using tfestimate. -

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  [tf_G_cl_est, ~] = tfestimate(id_cl.u, id_cl.d, win, [], [], 1/Ts);
-  [co_G_cl_est, ~] = mscohere(  id_cl.u, id_cl.d, win, [], [], 1/Ts);
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-The dynamics from driving voltage to the measured displacement are compared both in the open-loop and IFF case, and for the test-bench experimental identification and for the Simscape model in Figure 8. -This shows that the Integral Force Feedback architecture effectively damps the first resonance of the system. -

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iff_ol_cl_identified_simscape_comp.png -

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Figure 8: Comparison of the open-loop and closed-loop (IFF) dynamics for both the real identification and the Simscape one

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2.6 Inertial Sensors

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-In order to estimate the dynamics of the inertial sensor (the transfer function from the “absolute” displacement to the measured voltage), the following experiment can be performed: -

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  • The mass is excited such that is relative displacement as measured by the interferometer is much larger that the ground “absolute” motion.
    • -
  • The transfer function from the measured displacement by the interferometer to the measured voltage generated by the inertial sensors can be estimated.
    • -
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-The first point is quite important in order to have a good coherence between the interferometer measurement and the inertial sensor measurement. -

- -

-Here, a first identification is performed were the excitation signal is a white noise. -

- - -

-As usual, the data is loaded and any offset is removed. -

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  id = load('identification_noise_opt_iff.mat', 'd', 'acc_1', 'acc_2', 'geo_1', 'geo_2', 'f_meas', 'u', 't');
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-  id.d = detrend(id.d, 0);
-  id.acc_1 = detrend(id.acc_1, 0);
-  id.acc_2 = detrend(id.acc_2, 0);
-  id.geo_1 = detrend(id.geo_1, 0);
-  id.geo_2 = detrend(id.geo_2, 0);
-  id.f_meas = detrend(id.f_meas, 0);
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- -

-Then the transfer functions from the measured displacement by the interferometer to the generated voltage of the inertial sensors are computed.. -

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  Ts = id.t(2) - id.t(1);
-  win = hann(ceil(10/Ts));
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- -
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  [tf_acc1_est, f] = tfestimate(id.d, id.acc_1, win, [], [], 1/Ts);
-  [co_acc1_est, ~] = mscohere(  id.d, id.acc_1, win, [], [], 1/Ts);
-  [tf_acc2_est, ~] = tfestimate(id.d, id.acc_2, win, [], [], 1/Ts);
-  [co_acc2_est, ~] = mscohere(  id.d, id.acc_2, win, [], [], 1/Ts);
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-  [tf_geo1_est, ~] = tfestimate(id.d, id.geo_1, win, [], [], 1/Ts);
-  [co_geo1_est, ~] = mscohere(  id.d, id.geo_1, win, [], [], 1/Ts);
-  [tf_geo2_est, ~] = tfestimate(id.d, id.geo_2, win, [], [], 1/Ts);
-  [co_geo2_est, ~] = mscohere(  id.d, id.geo_2, win, [], [], 1/Ts);
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-The same transfer functions are estimated using the Simscape model. -

- -

-The obtained dynamics of the accelerometer are compared in Figure 9 while the one of the geophones are compared in Figure 10. -

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comp_dynamics_accelerometer.png -

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Figure 9: Comparison of the measured accelerometer dynamics

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comp_dynamics_geophone.png -

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Figure 10: Comparison of the measured geophone dynamics

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3 Optimal IFF Development

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-In this section, a proper identification of the transfer function from the force actuator to the force sensor is performed. -Then, an optimal IFF controller is developed and applied experimentally. -

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-The damped system is identified to verified the effectiveness of the added method. -

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3.1 Load Data

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-The experimental data is loaded and any offset is removed. -

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  id_ol = load('identification_noise_bis.mat', 'd', 'acc_1', 'acc_2', 'geo_1', 'geo_2', 'f_meas', 'u', 't');
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- -
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  id_ol.d = detrend(id_ol.d, 0);
-  id_ol.acc_1 = detrend(id_ol.acc_1, 0);
-  id_ol.acc_2 = detrend(id_ol.acc_2, 0);
-  id_ol.geo_1 = detrend(id_ol.geo_1, 0);
-  id_ol.geo_2 = detrend(id_ol.geo_2, 0);
-  id_ol.f_meas = detrend(id_ol.f_meas, 0);
-  id_ol.u = detrend(id_ol.u, 0);
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3.2 Experimental Data

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-The transfer function from force actuator to force sensors is estimated. -

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-The coherence shown in Figure 11 shows that the excitation signal is good enough. -

- -
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  Ts = id_ol.t(2) - id_ol.t(1);
-  win = hann(ceil(10/Ts));
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- -
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  [tf_fmeas_est, f] = tfestimate(id_ol.u, id_ol.f_meas, win, [], [], 1/Ts); % [V/m]
-  [co_fmeas_est, ~] = mscohere(  id_ol.u, id_ol.f_meas, win, [], [], 1/Ts);
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iff_identification_coh.png -

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Figure 11: Coherence for the identification of the IFF plant

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-The obtained dynamics is shown in Figure 12. -

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iff_identification_bode_plot.png -

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Figure 12: Bode plot of the identified IFF plant

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3.3 Model of the IFF Plant

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-In order to plot the root locus for the IFF control strategy, a model of the identified plant is developed. -

- -

-It consists of several poles and zeros are shown below. -

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  wz = 2*pi*102;
-  xi_z = 0.01;
-  wp = 2*pi*239.4;
-  xi_p = 0.015;
-
-  Giff = 2.2*(s^2 + 2*xi_z*s*wz + wz^2)/(s^2 + 2*xi_p*s*wp + wp^2) * ... % Dynamics
-         10*(s/3/pi/(1 + s/3/pi)) * ... % Low pass filter and gain of the voltage amplifier
-         exp(-Ts*s); % Time delay induced by ADC/DAC
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-
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-The comparison of the identified dynamics and the developed model is done in Figure 13. -

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iff_plant_model.png -

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Figure 13: IFF Plant + Model

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3.4 Root Locus and optimal Controller

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-Now, the root locus for the Integral Force Feedback strategy is computed and shown in Figure 14. -

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-Note that the controller used is not a pure integrator but rather a first order low pass filter with a cut-off frequency set at 2Hz. -

- - -
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iff_root_locus.png -

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Figure 14: Root Locus for the IFF control

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-The controller that yield maximum damping (shown by the red cross in Figure 14) is: -

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  Kiff_opt = 102/(s + 2*pi*2);
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3.5 Verification of the achievable damping

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-A new identification is performed with the IFF control strategy applied to the system. -

- -

-Data is loaded and offset removed. -

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  id_cl = load('identification_noise_iff_bis.mat', 'd', 'acc_1', 'acc_2', 'geo_1', 'geo_2', 'f_meas', 'u', 't');
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- -
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  id_cl.d      = detrend(id_cl.d, 0);
-  id_cl.acc_1  = detrend(id_cl.acc_1, 0);
-  id_cl.acc_2  = detrend(id_cl.acc_2, 0);
-  id_cl.geo_1  = detrend(id_cl.geo_1, 0);
-  id_cl.geo_2  = detrend(id_cl.geo_2, 0);
-  id_cl.f_meas = detrend(id_cl.f_meas, 0);
-  id_cl.u      = detrend(id_cl.u, 0);
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- -

-The open-loop and closed-loop dynamics are estimated. -

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  [tf_G_ol_est, f] = tfestimate(id_ol.u, id_ol.d, win, [], [], 1/Ts);
-  [co_G_ol_est, ~] = mscohere(  id_ol.u, id_ol.d, win, [], [], 1/Ts);
-  [tf_G_cl_est, ~] = tfestimate(id_cl.u, id_cl.d, win, [], [], 1/Ts);
-  [co_G_cl_est, ~] = mscohere(  id_cl.u, id_cl.d, win, [], [], 1/Ts);
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-The obtained coherence is shown in Figure 15 and the dynamics in Figure 16. -

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Gd_identification_iff_coherence.png -

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Figure 15: Coherence for the transfer function from F to d, with and without IFF

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Gd_identification_iff_bode_plot.png -

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Figure 16: Coherence for the transfer function from F to d, with and without IFF

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4 Generate the excitation signal

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-In order to properly estimate the dynamics of the inertial sensor, the excitation signal must be properly chosen. -

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-The requirements on the excitation signal is: -

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  • General much larger motion that the measured motion during the huddle test
    • -
  • Don’t damage the actuator
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-To determine the perfect voltage signal to be generated, we need two things: -

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  • the transfer function from voltage to mass displacement
    • -
  • the PSD of the measured motion by the inertial sensors
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  • not saturate the sensor signals
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  • provide enough signal/noise ratio (good coherence) in the frequency band of interest (~0.5Hz to 3kHz)
    • -
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4.1 Transfer function from excitation signal to displacement

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-Let’s first estimate the transfer function from the excitation signal in [V] to the generated displacement in [m] as measured by the inteferometer. -

- -
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  id_cl = load('identification_noise_iff_bis.mat', 'd', 'acc_1', 'acc_2', 'geo_1', 'geo_2', 'f_meas', 'u', 't');
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- -
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  Ts = id_cl.t(2) - id_cl.t(1);
-  win = hann(ceil(10/Ts));
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- -
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  [tf_G_cl_est, f] = tfestimate(id_cl.u, id_cl.d, win, [], [], 1/Ts);
-  [co_G_cl_est, ~] = mscohere(  id_cl.u, id_cl.d, win, [], [], 1/Ts);
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-Approximate transfer function from voltage output to generated displacement when IFF is used, in [m/V]. -

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  G_d_est = -5e-6*(2*pi*230)^2/(s^2 + 2*0.3*2*pi*240*s + (2*pi*240)^2);
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Gd_plant_estimation.png -

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Figure 17: Estimation of the transfer function from the excitation signal to the generated displacement

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4.2 Motion measured during Huddle test

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-We now compute the PSD of the measured motion by the inertial sensors during the huddle test. -

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  ht = load('huddle_test.mat', 'd', 'acc_1', 'acc_2', 'geo_1', 'geo_2', 'f_meas', 'u', 't');
-  ht.d = detrend(ht.d, 0);
-  ht.acc_1 = detrend(ht.acc_1, 0);
-  ht.acc_2 = detrend(ht.acc_2, 0);
-  ht.geo_1 = detrend(ht.geo_1, 0);
-  ht.geo_2 = detrend(ht.geo_2, 0);
-
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- -
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  [p_d, f] = pwelch(ht.d, win, [], [], 1/Ts);
-  [p_acc1, ~] = pwelch(ht.acc_1, win, [], [], 1/Ts);
-  [p_acc2, ~] = pwelch(ht.acc_2, win, [], [], 1/Ts);
-  [p_geo1, ~] = pwelch(ht.geo_1, win, [], [], 1/Ts);
-  [p_geo2, ~] = pwelch(ht.geo_2, win, [], [], 1/Ts);
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-Using an estimated model of the sensor dynamics from the documentation of the sensors, we can compute the ASD of the motion in \(m/\sqrt{Hz}\) measured by the sensors. -

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  G_acc = 1/(1 + s/2/pi/2500); % [V/(m/s2)]
-  G_geo = -120*s^2/(s^2 + 2*0.7*2*pi*2*s + (2*pi*2)^2); % [V/(m/s)]
-
-
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huddle_test_psd_motion.png -

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Figure 18: ASD of the motion measured by the sensors

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-From the ASD of the motion measured by the sensors, we can create an excitation signal that will generate much motion motion that the motion under no excitation. -

- -

-We create G_exc that corresponds to the wanted generated motion. -

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  G_exc = 0.2e-6/(1 + s/2/pi/2)/(1 + s/2/pi/50);
-
-
- -

-And we create a time domain signal y_d that have the spectral density described by G_exc. -

-
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  Fs = 1/Ts;
-  t = 0:Ts:180; % Time Vector [s]
-  u = sqrt(Fs/2)*randn(length(t), 1); % Signal with an ASD equal to one
-
-  y_d = lsim(G_exc, u, t);
-
-
- -
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  [pxx, ~] = pwelch(y_d, win, 0, [], Fs);
-
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-

comp_huddle_test_excited_motion_psd.png -

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Figure 19: Comparison of the ASD of the motion during Huddle and the wanted generated motion

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- - -

-We can now generate the voltage signal that will generate the wanted motion. -

-
-
  y_v = lsim(G_exc * ... % from unit PSD to shaped PSD
-             (1 + s/2/pi/50) * ... % Inverse of pre-filter included in the Simulink file
-             1/G_d_est * ... % Wanted displacement => required voltage
-             1/(1 + s/2/pi/5e3), ... %  Add some high frequency filtering
-             u, t);
-
-
- - -
-

optimal_exc_signal_time.png -

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Figure 20: Generated excitation signal

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5 Identification of the Inertial Sensors Dynamics

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- -

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-Using the excitation signal generated in Section 4, the dynamics of the inertial sensors are identified. -

-
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5.1 Load Data

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-Both the measurement data during the identification test and during an “huddle test” are loaded. -

-
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  id = load('identification_noise_opt_iff.mat', 'd', 'acc_1', 'acc_2', 'geo_1', 'geo_2', 'f_meas', 'u', 't');
-  ht = load('huddle_test.mat', 'd', 'acc_1', 'acc_2', 'geo_1', 'geo_2', 'f_meas', 'u', 't');
-
-
- -
-
  ht.d = detrend(ht.d, 0);
-  ht.acc_1 = detrend(ht.acc_1, 0);
-  ht.acc_2 = detrend(ht.acc_2, 0);
-  ht.geo_1 = detrend(ht.geo_1, 0);
-  ht.geo_2 = detrend(ht.geo_2, 0);
-  ht.f_meas = detrend(ht.f_meas, 0);
-
-
- -
-
  id.d = detrend(id.d, 0);
-  id.acc_1 = detrend(id.acc_1, 0);
-  id.acc_2 = detrend(id.acc_2, 0);
-  id.geo_1 = detrend(id.geo_1, 0);
-  id.geo_2 = detrend(id.geo_2, 0);
-  id.f_meas = detrend(id.f_meas, 0);
-
-
-
-
- -
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5.2 Compare PSD during Huddle and and during identification

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-The Power Spectral Density of the measured motion during the huddle test and during the identification test are compared in Figures 21 and 22. -

- -
-
  Ts = ht.t(2) - ht.t(1);
-  win = hann(ceil(10/Ts));
-
-
- -
-
  [p_id_d, f] = pwelch(id.d, win, [], [], 1/Ts);
-  [p_id_acc1, ~] = pwelch(id.acc_1, win, [], [], 1/Ts);
-  [p_id_acc2, ~] = pwelch(id.acc_2, win, [], [], 1/Ts);
-  [p_id_geo1, ~] = pwelch(id.geo_1, win, [], [], 1/Ts);
-  [p_id_geo2, ~] = pwelch(id.geo_2, win, [], [], 1/Ts);
-
-
- -
-
  [p_ht_d, ~] = pwelch(ht.d, win, [], [], 1/Ts);
-  [p_ht_acc1, ~] = pwelch(ht.acc_1, win, [], [], 1/Ts);
-  [p_ht_acc2, ~] = pwelch(ht.acc_2, win, [], [], 1/Ts);
-  [p_ht_geo1, ~] = pwelch(ht.geo_1, win, [], [], 1/Ts);
-  [p_ht_geo2, ~] = pwelch(ht.geo_2, win, [], [], 1/Ts);
-  [p_ht_fmeas, ~] = pwelch(ht.f_meas, win, [], [], 1/Ts);
-
-
- - -
-

comp_psd_huddle_test_identification_acc.png -

-

Figure 21: Comparison of the PSD of the measured motion during the Huddle test and during the identification

-
- - -
-

comp_psd_huddle_test_identification_geo.png -

-

Figure 22: Comparison of the PSD of the measured motion during the Huddle test and during the identification

-
-
-
- -
-

5.3 Compute transfer functions

-
-

-The transfer functions from the motion as measured by the interferometer (and that should represent the absolute motion of the mass) to the inertial sensors are estimated: -

-
-
  [tf_acc1_est, f] = tfestimate(id.d, id.acc_1, win, [], [], 1/Ts);
-  [co_acc1_est, ~] = mscohere(  id.d, id.acc_1, win, [], [], 1/Ts);
-  [tf_acc2_est, ~] = tfestimate(id.d, id.acc_2, win, [], [], 1/Ts);
-  [co_acc2_est, ~] = mscohere(  id.d, id.acc_2, win, [], [], 1/Ts);
-
-  [tf_geo1_est, ~] = tfestimate(id.d, id.geo_1, win, [], [], 1/Ts);
-  [co_geo1_est, ~] = mscohere(  id.d, id.geo_1, win, [], [], 1/Ts);
-  [tf_geo2_est, ~] = tfestimate(id.d, id.geo_2, win, [], [], 1/Ts);
-  [co_geo2_est, ~] = mscohere(  id.d, id.geo_2, win, [], [], 1/Ts);
-
-
- -

-The obtained coherence are shown in Figure 23. -

- - -
-

id_sensor_dynamics_coherence.png -

-

Figure 23: Coherence for the estimation of the sensor dynamics

-
- -

-We also make a simplified model of the inertial sensors to be compared with the identified dynamics. -

-
-
  G_acc = 1/(1 + s/2/pi/2500); % [V/(m/s2)]
-  G_geo = -1200*s^2/(s^2 + 2*0.7*2*pi*2*s + (2*pi*2)^2); % [[V/(m/s)]
-
-
- -

-The model and identified dynamics show good agreement (Figures 24 and 25.) -

- - -
-

id_sensor_dynamics_accelerometers.png -

-

Figure 24: Identified dynamics of the accelerometers

-
- - -
-

id_sensor_dynamics_geophones.png -

-

Figure 25: Identified dynamics of the geophones

-
-
-
-
- -
-

6 Inertial Sensor Noise and the \(\mathcal{H}_2\) Synthesis of complementary filters

-
-

- -

-

-In this section, the noise of the inertial sensors (geophones and accelerometers) is estimated. -

-
-
-

6.1 Load Data

-
-

-As before, the identification data is loaded and any offset if removed. -

-
-
  id = load('identification_noise_opt_iff.mat', 'd', 'acc_1', 'acc_2', 'geo_1', 'geo_2', 'f_meas', 'u', 't');
-
-
- -
-
  id.d = detrend(id.d, 0);
-  id.acc_1 = detrend(id.acc_1, 0);
-  id.acc_2 = detrend(id.acc_2, 0);
-  id.geo_1 = detrend(id.geo_1, 0);
-  id.geo_2 = detrend(id.geo_2, 0);
-  id.f_meas = detrend(id.f_meas, 0);
-
-
-
-
- -
-

6.2 ASD of the Measured displacement

-
-

-The Power Spectral Density of the displacement as measured by the interferometer and the inertial sensors is computed. -

-
-
  Ts = id.t(2) - id.t(1);
-  win = hann(ceil(10/Ts));
-
-
- -
-
  [p_id_d,     f] = pwelch(id.d,      win, [], [], 1/Ts);
-  [p_id_acc1,  ~] = pwelch(id.acc_1,  win, [], [], 1/Ts);
-  [p_id_acc2,  ~] = pwelch(id.acc_2,  win, [], [], 1/Ts);
-  [p_id_geo1,  ~] = pwelch(id.geo_1,  win, [], [], 1/Ts);
-  [p_id_geo2,  ~] = pwelch(id.geo_2,  win, [], [], 1/Ts);
-
-
- -

-Let’s use a model of the accelerometer and geophone to compute the motion from the measured voltage. -

-
-
  G_acc = 1/(1 + s/2/pi/2500); % [V/(m/s2)]
-  G_geo = -1200*s^2/(s^2 + 2*0.7*2*pi*2*s + (2*pi*2)^2); % [[V/(m/s)]
-
-
- -

-The obtained ASD in \(m/\sqrt{Hz}\) is shown in Figure 26. -

- - -
-

measure_displacement_all_sensors.png -

-

Figure 26: ASD of the measured displacement as measured by all the sensors

-
-
-
- -
-

6.3 ASD of the Sensor Noise

-
-

-The noise of a sensor can be estimated using two identical sensors by computing: -

-
    -
  • the Power Spectral Density of the measured motion by the two sensors
    • -
  • the Cross Spectral Density between the two sensors (coherence)
    • -
- -

-This technique to estimate the sensor noise is described in (Barzilai, VanZandt, and Kenny 1998). -

- -

-The Power Spectral Density of the sensor noise can be estimated using the following equation: -

-\begin{equation} - |S_n(\omega)| = |S_{x_1}(\omega)| \Big( 1 - \gamma_{x_1 x_2}(\omega) \Big) -\end{equation} -

-with \(S_{x_1}\) the PSD of one of the sensor and \(\gamma_{x_1 x_2}\) the coherence between the two sensors. -

- -

-The coherence between the two accelerometers and between the two geophones is computed. -

-
-
  [coh_acc, ~] = mscohere(id.acc_1, id.acc_2, win, [], [], 1/Ts);
-  [coh_geo, ~] = mscohere(id.geo_1, id.geo_2, win, [], [], 1/Ts);
-
-
- -

-Finally, the Power Spectral Density of the sensors is computed and converted in \([m^2/Hz]\). -

-
-
  pN_acc = p_id_acc1.*(1 - coh_acc) .* ... % [V^2/Hz]
-           1./abs(squeeze(freqresp(G_acc*s^2, f, 'Hz'))).^2; % [(m/V)^2]
-  pN_geo = p_id_geo1.*(1 - coh_geo) .* ... % [V^2/Hz]
-           1./abs(squeeze(freqresp(G_geo*s, f, 'Hz'))).^2; % [(m/V)^2]
-
-
- -

-The ASD of obtained noises are compared with the ASD of the measured signals in Figure 27. -

- -
-

noise_inertial_sensors_comparison.png -

-

Figure 27: Comparison of the computed ASD of the noise of the two inertial sensors

-
-
-
- -
-

6.4 Noise Model

-
-

-Transfer functions are adjusted in order to fit the ASD of the sensor noises (expressed in \([m/s/\sqrt{Hz}]\) for more easy fitting). -

- -

-These transfer functions are defined below and compared with the measured ASD in Figure 28. -

-
-
  N_acc = 1*(s/(2*pi*2000) + 1)^2/(s + 0.1*2*pi)/(s + 1e3*2*pi); % [m/sqrt(Hz)]
-  N_geo = 4e-4*(s/(2*pi*200) + 1)/(s + 1e3*2*pi); % [m/sqrt(Hz)]
-
-
- - -
-

noise_models_velocity.png -

-

Figure 28: ASD of the velocity noise measured by the sensors and the noise models

-
-
-
- -
-

6.5 \(\mathcal{H}_2\) Synthesis of the Complementary Filters

-
-

-We now wish to synthesize two complementary filters to merge the geophone and the accelerometer signal in such a way that the fused signal has the lowest possible RMS noise. -

- -

-To do so, we use the \(\mathcal{H}_2\) synthesis where the transfer functions representing the noise density of both sensors are used as weights. -

- -

-The generalized plant used for the synthesis is defined below. -

-
-
  P = [0      N_acc  1;
-       N_geo -N_acc  0];
-
-
- -

-And the \(\mathcal{H}_2\) synthesis is done using the h2syn command. -

-
-
  [H_geo, ~, gamma] = h2syn(P, 1, 1);
-  H_acc = 1 - H_geo;
-
-
- -

-The obtained complementary filters are shown in Figure 29. -

- - -
-

complementary_filters_velocity_H2.png -

-

Figure 29: Obtained Complementary Filters

-
-
-
- -
-

6.6 Results

-
-

-Finally, the signals of both sensors are merged using the complementary filters and the super sensor noise is estimated and compared with the individual sensor noises in Figure 30. -

- - -
-

super_sensor_noise_asd_velocity.png -

-

Figure 30: ASD of the super sensor noise (velocity)

-
- -

-Finally, the Cumulative Power Spectrum is computed and compared in Figure 31. -

-
-
  [~, i_1Hz] = min(abs(f - 1));
-
-
- -
-
  CPS_acc = 1/pi*flip(-cumtrapz(2*pi*flip(f), flip((pN_acc.*(2*pi*f)).^2)));
-  CPS_geo = 1/pi*flip(-cumtrapz(2*pi*flip(f), flip((pN_geo.*(2*pi*f)).^2)));
-  CPS_SS  = 1/pi*flip(-cumtrapz(2*pi*flip(f), flip((pN_acc.*(2*pi*f)).^2.*abs(squeeze(freqresp(H_acc, f, 'Hz'))).^2 + (pN_geo.*(2*pi*f)).^2.*abs(squeeze(freqresp(H_geo, f, 'Hz'))).^2)));
-
-
- - -
-

super_sensor_noise_cas_velocity.png -

-

Figure 31: Cumulative Power Spectrum of the Sensor Noise (velocity)

-
-
-
-
- -
-

7 Inertial Sensor Dynamics Uncertainty and the \(\mathcal{H}_\infty\) Synthesis of complementary filters

-
-

- -

-

-When merging two sensors, it is important to be sure that we correctly know the sensor dynamics near the merging frequency. -Thus, identifying the uncertainty on the sensor dynamics is quite important to perform a robust merging. -

-
-
-

7.1 Load Data

-
-

-Data is loaded and offset is removed. -

- -
-
  id = load('identification_noise_opt_iff.mat', 'd', 'acc_1', 'acc_2', 'geo_1', 'geo_2', 'f_meas', 'u', 't');
-
-
- -
-
  id.d = detrend(id.d, 0);
-  id.acc_1 = detrend(id.acc_1, 0);
-  id.acc_2 = detrend(id.acc_2, 0);
-  id.geo_1 = detrend(id.geo_1, 0);
-  id.geo_2 = detrend(id.geo_2, 0);
-  id.f_meas = detrend(id.f_meas, 0);
-
-
-
-
- -
-

7.2 Compute the dynamics of both sensors

-
-

-The dynamics of inertial sensors are estimated (in \([V/m]\)). -

-
-
  Ts = id.t(2) - id.t(1);
-  win = hann(ceil(10/Ts));
-
-
- -
-
  [tf_acc1_est, f] = tfestimate(id.d, id.acc_1, win, [], [], 1/Ts);
-  [co_acc1_est, ~] = mscohere(  id.d, id.acc_1, win, [], [], 1/Ts);
-  [tf_acc2_est, ~] = tfestimate(id.d, id.acc_2, win, [], [], 1/Ts);
-  [co_acc2_est, ~] = mscohere(  id.d, id.acc_2, win, [], [], 1/Ts);
-
-  [tf_geo1_est, ~] = tfestimate(id.d, id.geo_1, win, [], [], 1/Ts);
-  [co_geo1_est, ~] = mscohere(  id.d, id.geo_1, win, [], [], 1/Ts);
-  [tf_geo2_est, ~] = tfestimate(id.d, id.geo_2, win, [], [], 1/Ts);
-  [co_geo2_est, ~] = mscohere(  id.d, id.geo_2, win, [], [], 1/Ts);
-
-
- -

-The (nominal) models of the inertial sensors from the absolute displacement to the generated voltage are defined below: -

-
-
  G_acc = 1/(1 + s/2/pi/2000)
-  G_geo = -1200*s^2/(s^2 + 2*0.7*2*pi*2*s + (2*pi*2)^2);
-
-
- -

-These models are very simplistic models, and we then take into account the un-modelled dynamics with dynamical uncertainty. -

-
-
- -
-

7.3 Dynamics uncertainty estimation

-
-

-Weights representing the dynamical uncertainty of the sensors are defined below. -

-
-
  w_acc = createWeight('n', 2, 'G0', 10,  'G1', 0.2,    'Gc', 1,     'w0', 6*2*pi) * ...
-          createWeight('n', 2, 'G0', 1,   'G1', 5/0.2,  'Gc', 1/0.2, 'w0', 1300*2*pi);
-
-  w_geo = createWeight('n', 2, 'G0', 0.6, 'G1', 0.2,    'Gc', 0.3,   'w0', 3*2*pi) * ...
-          createWeight('n', 2, 'G0', 1,   'G1', 10/0.2, 'Gc', 1/0.2, 'w0', 800*2*pi);
-
-
- -

-The measured dynamics are compared with the modelled one as well as the modelled uncertainty in Figure 32 for the accelerometers and in Figure 33 for the geophones. -

- - -
-

dyn_uncertainty_acc.png -

-

Figure 32: Modeled dynamical uncertainty and meaured dynamics of the accelerometers

-
- - -
-

dyn_uncertainty_geo.png -

-

Figure 33: Modeled dynamical uncertainty and meaured dynamics of the geophones

-
-
-
- -
-

7.4 \(\mathcal{H}_\infty\) Synthesis of Complementary Filters

-
-

-A last weight is now defined that represents the maximum dynamical uncertainty that is allowed for the super sensor. -

-
-
  wu = inv(createWeight('n', 2, 'G0', 0.7, 'G1', 0.3,   'Gc', 0.4,   'w0', 3*2*pi) * ...
-           createWeight('n', 2, 'G0', 1,   'G1', 6/0.3, 'Gc', 1/0.3, 'w0', 1200*2*pi));
-
-
- -

-This dynamical uncertainty is compared with the two sensor uncertainties in Figure 34. -

- -
-

uncertainty_weight_and_sensor_uncertainties.png -

-

Figure 34: Individual sensor uncertainty (normalized by their dynamics) and the wanted maximum super sensor noise uncertainty

-
- -

-The generalized plant used for the synthesis is defined: -

-
-
  P = [wu*w_acc -wu*w_acc;
-       0         wu*w_geo;
-       1         0];
-
-
- -

-And the \(\mathcal{H}_\infty\) synthesis using the hinfsyn command is performed. -

-
-
  [H_geo, ~, gamma, ~] = hinfsyn(P, 1, 1,'TOLGAM', 0.001, 'METHOD', 'ric', 'DISPLAY', 'on');
-
-
- -
-  Test bounds:  0.8556 <=  gamma  <=  1.34
-
-    gamma        X>=0        Y>=0       rho(XY)<1    p/f
-  1.071e+00     0.0e+00     0.0e+00     0.000e+00     p
-  9.571e-01     0.0e+00     0.0e+00     9.436e-16     p
-  9.049e-01     0.0e+00     0.0e+00     1.084e-15     p
-  8.799e-01     0.0e+00     0.0e+00     1.191e-16     p
-  8.677e-01     0.0e+00     0.0e+00     6.905e-15     p
-  8.616e-01     0.0e+00     0.0e+00     0.000e+00     p
-  8.586e-01     1.1e-17     0.0e+00     6.917e-16     p
-  8.571e-01     0.0e+00     0.0e+00     6.991e-17     p
-  8.564e-01     0.0e+00     0.0e+00     1.492e-16     p
-
-  Best performance (actual): 0.8563
-
- -

-The complementary filter is defined as follows: -

-
-
  H_acc = 1 - H_geo;
-
-
- -

-The bode plot of the obtained complementary filters is shown in Figure -

- -
-

h_infinity_obtained_complementary_filters.png -

-

Figure 35: Bode plot of the obtained complementary filters using the \(\mathcal{H}_\infty\) synthesis

-
-
-
- -
-

7.5 Obtained Super Sensor Dynamical Uncertainty

-
-

-The obtained super sensor dynamical uncertainty is shown in Figure 36. -

- - -
-

super_sensor_uncertainty_h_infinity.png -

-

Figure 36: Obtained Super sensor dynamics uncertainty

-
-
-
-
- -
-

8 Optimal and Robust sensor fusion using the \(\mathcal{H}_2/\mathcal{H}_\infty\) synthesis

-
-

- -

-
-
-

8.1 Noise and Dynamical uncertainty weights

-
-
-
  N_acc = (s/(2*pi*2000) + 1)^2/(s + 0.1*2*pi)/(s + 1e3*2*pi)/(1 + s/2/pi/1e3); % [m/sqrt(Hz)]
-  N_geo = 4e-4*((s + 2*pi)/(2*pi*200) + 1)/(s + 1e3*2*pi)/(1 + s/2/pi/1e3); % [m/sqrt(Hz)]
-
-
- -
-
  w_acc = createWeight('n', 2, 'G0', 10,  'G1', 0.2,    'Gc', 1,     'w0', 6*2*pi) * ...
-          createWeight('n', 2, 'G0', 1,   'G1', 5/0.2,  'Gc', 1/0.2, 'w0', 1300*2*pi);
-
-  w_geo = createWeight('n', 2, 'G0', 0.6, 'G1', 0.2,    'Gc', 0.3,   'w0', 3*2*pi) * ...
-          createWeight('n', 2, 'G0', 1,   'G1', 10/0.2, 'Gc', 1/0.2, 'w0', 800*2*pi);
-
-
- -
-
  wu = inv(createWeight('n', 2, 'G0', 0.7, 'G1', 0.3,   'Gc', 0.4,   'w0', 3*2*pi) * ...
-           createWeight('n', 2, 'G0', 1,   'G1', 6/0.3, 'Gc', 1/0.3, 'w0', 1200*2*pi));
-
-
- -
-
  P = [wu*w_acc -wu*w_acc;
-       0         wu*w_geo;
-       N_acc    -N_acc;
-       0         N_geo;
-       1         0];
-
-
- -

-And the mixed \(\mathcal{H}_2/\mathcal{H}_\infty\) synthesis is performed. -

-
-
  [H_geo, ~] = h2hinfsyn(ss(P), 1, 1, 2, [0, 1], 'HINFMAX', 1, 'H2MAX', Inf, 'DKMAX', 100, 'TOL', 1e-3, 'DISPLAY', 'on');
-
-
- -
-
  H_acc = 1 - H_geo;
-
-
-
-
- -
-

8.2 Obtained Super Sensor Noise

-
-
-
  freqs = logspace(0, 4, 1000);
-  PSD_Sgeo = abs(squeeze(freqresp(N_geo, freqs, 'Hz'))).^2;
-  PSD_Sacc = abs(squeeze(freqresp(N_acc, freqs, 'Hz'))).^2;
-  PSD_Hss  = abs(squeeze(freqresp(N_acc*H_acc, freqs, 'Hz'))).^2 + ...
-             abs(squeeze(freqresp(N_geo*H_geo, freqs, 'Hz'))).^2;
-
-
- - -
-

psd_sensors_htwo_hinf_synthesis.png -

-

Figure 37: Power Spectral Density of the Super Sensor obtained with the mixed \(\mathcal{H}_2/\mathcal{H}_\infty\) synthesis

-
-
-
- -
-

8.3 Obtained Super Sensor Dynamical Uncertainty

-
- -
-

super_sensor_dynamical_uncertainty_Htwo_Hinf.png -

-

Figure 38: Super sensor dynamical uncertainty (solid curve) when using the mixed \(\mathcal{H}_2/\mathcal{H}_\infty\) Synthesis

-
-
-
- -
-

8.4 Experimental Super Sensor Dynamical Uncertainty

-
-

-The super sensor dynamics is shown in Figure 39. -

- - -
-

super_sensor_optimal_uncertainty.png -

-

Figure 39: Inertial Sensor dynamics as well as the super sensor dynamics

-
-
-
- -
-

8.5 Experimental Super Sensor Noise

-
-

-The obtained super sensor noise is shown in Figure 40. -

- - -
-

super_sensor_optimal_noise.png -

-

Figure 40: ASD of the super sensor obtained using the \(\mathcal{H}_2/\mathcal{H}_\infty\) synthesis

-
-
-
-
- -
-

9 Matlab Functions

-
-

- -

-
-
-

9.1 createWeight

-
-

- -

- -

-This Matlab function is accessible here. -

- -
-
    function [W] = createWeight(args)
-    % createWeight -
-    %
-    % Syntax: [in_data] = createWeight(in_data)
-    %
-    % Inputs:
-    %    - n  - Weight Order
-    %    - G0 - Low frequency Gain
-    %    - G1 - High frequency Gain
-    %    - Gc - Gain of W at frequency w0
-    %    - w0 - Frequency at which |W(j w0)| = Gc
-    %
-    % Outputs:
-    %    - W - Generated Weight
-
-        arguments
-            args.n  (1,1) double {mustBeInteger, mustBePositive} = 1
-            args.G0 (1,1) double {mustBeNumeric, mustBePositive} = 0.1
-            args.G1 (1,1) double {mustBeNumeric, mustBePositive} = 10
-            args.Gc (1,1) double {mustBeNumeric, mustBePositive} = 1
-            args.w0 (1,1) double {mustBeNumeric, mustBePositive} = 1
-        end
-
-      mustBeBetween(args.G0, args.Gc, args.G1);
-
-      s = tf('s');
-
-      W = (((1/args.w0)*sqrt((1-(args.G0/args.Gc)^(2/args.n))/(1-(args.Gc/args.G1)^(2/args.n)))*s + (args.G0/args.Gc)^(1/args.n))/((1/args.G1)^(1/args.n)*(1/args.w0)*sqrt((1-(args.G0/args.Gc)^(2/args.n))/(1-(args.Gc/args.G1)^(2/args.n)))*s + (1/args.Gc)^(1/args.n)))^args.n;
-
-      end
-
-      % Custom validation function
-      function mustBeBetween(a,b,c)
-          if ~((a > b && b > c) || (c > b && b > a))
-              eid = 'createWeight:inputError';
-              msg = 'Gc should be between G0 and G1.';
-              throwAsCaller(MException(eid,msg))
-          end
-      end
-
-
-
-
- -
-

9.2 plotMagUncertainty

-
-

- -

- -

-This Matlab function is accessible here. -

- -
-
  function [p] = plotMagUncertainty(W, freqs, args)
-  % plotMagUncertainty -
-  %
-  % Syntax: [p] = plotMagUncertainty(W, freqs, args)
-  %
-  % Inputs:
-  %    - W     - Multiplicative Uncertainty Weight
-  %    - freqs - Frequency Vector [Hz]
-  %    - args  - Optional Arguments:
-  %      - G
-  %      - color_i
-  %      - opacity
-  %
-  % Outputs:
-  %    - p - Plot Handle
-
-  arguments
-      W
-      freqs double {mustBeNumeric, mustBeNonnegative}
-      args.G = tf(1)
-      args.color_i (1,1) double {mustBeInteger, mustBePositive} = 1
-      args.opacity (1,1) double {mustBeNumeric, mustBeNonnegative} = 0.3
-      args.DisplayName char = ''
-  end
-
-  % Get defaults colors
-  colors = get(groot, 'defaultAxesColorOrder');
-
-  p = patch([freqs flip(freqs)], ...
-            [abs(squeeze(freqresp(args.G, freqs, 'Hz'))).*(1 + abs(squeeze(freqresp(W, freqs, 'Hz')))); ...
-             flip(abs(squeeze(freqresp(args.G, freqs, 'Hz'))).*max(1 - abs(squeeze(freqresp(W, freqs, 'Hz'))), 1e-6))], 'w', ...
-            'DisplayName', args.DisplayName);
-
-  p.FaceColor = colors(args.color_i, :);
-  p.EdgeColor = 'none';
-  p.FaceAlpha = args.opacity;
-
-  end
-
-
-
-
- -
-

9.3 plotPhaseUncertainty

-
-

- -

- -

-This Matlab function is accessible here. -

- -
-
  function [p] = plotPhaseUncertainty(W, freqs, args)
-  % plotPhaseUncertainty -
-  %
-  % Syntax: [p] = plotPhaseUncertainty(W, freqs, args)
-  %
-  % Inputs:
-  %    - W     - Multiplicative Uncertainty Weight
-  %    - freqs - Frequency Vector [Hz]
-  %    - args  - Optional Arguments:
-  %      - G
-  %      - color_i
-  %      - opacity
-  %
-  % Outputs:
-  %    - p - Plot Handle
-
-  arguments
-      W
-      freqs double {mustBeNumeric, mustBeNonnegative}
-      args.G = tf(1)
-      args.color_i (1,1) double {mustBeInteger, mustBePositive} = 1
-      args.opacity (1,1) double {mustBeNumeric, mustBePositive} = 0.3
-      args.DisplayName char = ''
-  end
-
-  % Get defaults colors
-  colors = get(groot, 'defaultAxesColorOrder');
-
-  % Compute Phase Uncertainty
-  Dphi = 180/pi*asin(abs(squeeze(freqresp(W, freqs, 'Hz'))));
-  Dphi(abs(squeeze(freqresp(W, freqs, 'Hz'))) > 1) = 360;
-
-  % Compute Plant Phase
-  G_ang = 180/pi*angle(squeeze(freqresp(args.G, freqs, 'Hz')));
-
-  p = patch([freqs flip(freqs)], [G_ang+Dphi; flip(G_ang-Dphi)], 'w', ...
-            'DisplayName', args.DisplayName);
-
-  p.FaceColor = colors(args.color_i, :);
-  p.EdgeColor = 'none';
-  p.FaceAlpha = args.opacity;
-
-  end
-
-
-
-
-
- -

Bibliography

-
-
Barzilai, Aaron, Tom VanZandt, and Tom Kenny. 1998. “Technique for Measurement of the Noise of a Sensor in the Presence of Large Background Signals.” Review of Scientific Instruments 69 (7):2767–72. https://doi.org/10.1063/1.1149013.
-
-
-
-

Author: Dehaeze Thomas

-

Created: 2021-02-02 mar. 18:53

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