From 980c20f6f3ab06a17a64d4603579cf75e652be8c Mon Sep 17 00:00:00 2001 From: Thomas Dehaeze Date: Thu, 17 Jun 2021 14:48:06 +0200 Subject: [PATCH] Update analysis of APA Simscape model --- .gitignore | 1 + figs/apa_1_coh_dvf.pdf | Bin 98569 -> 184542 bytes figs/apa_1_coh_dvf.png | Bin 46175 -> 53221 bytes figs/apa_1_frf_dvf.pdf | Bin 172994 -> 172994 bytes figs/apa_act_constant_comp.pdf | 948 ++++++------- figs/apa_act_constant_comp.png | Bin 119901 -> 119816 bytes figs/apa_act_constant_comp_flex.pdf | Bin 271726 -> 271714 bytes figs/apa_act_constant_comp_flex.png | Bin 129540 -> 129552 bytes figs/apa_model_bench_bode_dl_z.pdf | Bin 49686 -> 51884 bytes figs/apa_model_bench_bode_dl_z.png | Bin 95552 -> 71774 bytes figs/apa_model_bench_bode_vs.pdf | Bin 51369 -> 50852 bytes figs/apa_model_bench_bode_vs.png | Bin 73243 -> 68466 bytes figs/apa_sens_constant_comp.pdf | Bin 260639 -> 260688 bytes figs/apa_sens_constant_comp.png | Bin 105019 -> 105025 bytes figs/comp_apa_plant_after_opt.pdf | 2036 ++++++++++++++------------- figs/comp_apa_plant_after_opt.png | Bin 202181 -> 202833 bytes figs/exc_signal_1_noise.pdf | Bin 0 -> 57075 bytes figs/exc_signal_1_noise.png | Bin 0 -> 97256 bytes figs/exc_signal_2_sweep.pdf | Bin 0 -> 28999 bytes figs/exc_signal_2_sweep.png | Bin 0 -> 16948 bytes figs/exc_signal_3_hf_noise.pdf | Bin 0 -> 46600 bytes figs/exc_signal_3_hf_noise.png | Bin 0 -> 94251 bytes matlab/test_bench_apa300ml.slx | Bin 55902 -> 55966 bytes test-bench-apa300ml.org | 677 +++++---- 24 files changed, 1864 insertions(+), 1798 deletions(-) create mode 100644 figs/exc_signal_1_noise.pdf create mode 100644 figs/exc_signal_1_noise.png create mode 100644 figs/exc_signal_2_sweep.pdf create mode 100644 figs/exc_signal_2_sweep.png create mode 100644 figs/exc_signal_3_hf_noise.pdf create mode 100644 figs/exc_signal_3_hf_noise.png diff --git a/.gitignore b/.gitignore index 8394d13..c06521c 100644 --- a/.gitignore +++ b/.gitignore @@ -4,6 +4,7 @@ auto/ *.synctex.gz 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b/test-bench-apa300ml.org @@ -216,6 +216,10 @@ Before using the measurement bench to characterize the APA300ML, first simple me - Section [[sec:spurious_resonances]]: the "spurious" resonances of the APA are investigated ** Geometrical Measurements +:PROPERTIES: +:header-args:matlab: :tangle matlab/basic_meas_geometrical.m +:header-args:matlab+: :comments no +:END: <> *** Introduction :ignore: @@ -377,6 +381,10 @@ The APA number 3 has ben sent back to Cedrat, and a new APA300ML has been shippe #+end_warning ** Stroke measurement +:PROPERTIES: +:header-args:matlab: :tangle matlab/basic_meas_stroke.m +:header-args:matlab+: :comments no +:END: <> *** Introduction :ignore: We here wish to estimate the stroke of the APA. @@ -659,6 +667,10 @@ Therefore, only two stacks can be used as an actuator. #+end_important ** Spurious resonances +:PROPERTIES: +:header-args:matlab: :tangle matlab/basic_meas_spurious_res.m +:header-args:matlab+: :comments no +:END: <> *** Introduction @@ -961,9 +973,9 @@ This include: - Stroke - Stiffness - Hysteresis -- Gain from the applied voltage $V_a$ to the generated Force $F_a$ -- Gain from the sensor stack strain $\delta L$ to the generated voltage $V_s$ -- Dynamical behavior +- "Actuator constant": Gain from the applied voltage $V_a$ to the generated Force $F_a$ +- "Sensor constant": Gain from the sensor stack strain $\delta L$ to the generated voltage $V_s$ +- Dynamical behavior from the actuator to the force sensor and to the motion of the APA The bench is shown in Figure [[fig:picture_apa_bench]], and a zoom picture on the APA and encoder is shown in Figure [[fig:picture_apa_bench_encoder]]. @@ -1006,11 +1018,10 @@ The bench is schematically shown in Figure [[fig:test_bench_apa_alone]] and the | =t= | Time | [s] | | This section is structured as follows: -- Section [[sec:meas_apa_speedgoat_setup]]: the Speedgoat setup is described (excitation signals, saved signals, etc.) - Section [[sec:meas_one_apa]]: the measurements are first performed on one APA. - Section [[sec:meas_all_apa]]: the same measurements are performed on all the APA and are compared. -** Speedgoat Setup +** Speedgoat Setup :noexport: <> *** Introduction :ignore: *** Matlab Init :noexport:ignore: @@ -1285,6 +1296,10 @@ save(sprintf('mat/frf_data_%i_huddle.mat', apa_number), 't', 'Va', 'Vs', 'de', ' #+end_src ** Measurements on APA 1 +:PROPERTIES: +:header-args:matlab: :tangle matlab/apa_meas_analysis_1.m +:header-args:matlab+: :comments no +:END: <> *** Introduction :ignore: Measurements are first performed on only *one* APA. @@ -1301,6 +1316,8 @@ Once the measurement procedure is validated, it is performed on all the other AP #+begin_src matlab colors = colororder; +Fs = 1e4; % Sampling Frequency [Hz] +Ts = 1/Fs; % Sampling Time [s] #+end_src #+begin_src matlab :tangle no @@ -1314,11 +1331,138 @@ addpath('./mat/'); addpath('./src/'); #+end_src -*** Excitation Signal -For this first measurement, a basic logarithmic sweep is used between 10Hz and 2kHz. +*** Excitation Signals +Different excitation signals are used to perform FRF estimations. + +Typically, this is done in three steps: +1. A low pass filtered white noise is used with rather small amplitudes (Figure [[fig:exc_signal_1_noise]]). + This first excitation is used to estimate the main resonance of the system. +2. A sweep-sine from 10Hz to 400Hz is used (Figure [[fig:exc_signal_2_sweep]]). + The sweep-sine is is notched around the estimated resonance of the system. +3. A band-limited white noise from 300Hz to 2kHz is used to estimate the high frequency behavior (Figure [[fig:exc_signal_3_hf_noise]]). + +For all the excitation signals, before the excitation starts, the mean voltage is slowly increased halfway between the minimum voltage (-20V) and the maximum (150V). + +The first measurement is only used to have a first estimation of the dynamics and verify that everything is setup correctly. +The second excitation is done to estimate the dynamics from 10Hz to 350Hz and the third excitation from 350Hz to 2kHz. +The second and third measurements are therefore combined in the frequency domain to form one good estimation of the dynamics from 10Hz up to 2kHz. + +#+begin_src matlab :exports none :tangle no +V_noise = generateShapedNoise('Ts', 1/Fs, ... + 'V_mean', 3.25, ... + 't_start', 5, ... + 'exc_duration', 50, ... + 'smooth_ends', true, ... + 'V_exc', 0.05/(1 + s/2/pi/10)); +#+end_src + +#+begin_src matlab :exports none :tangle no +%% Plot of the excitation signal and associated PSD +figure; +tiledlayout(1, 2, 'TileSpacing', 'Normal', 'Padding', 'None'); + +ax1 = nexttile; +plot(V_noise(1,:), V_noise(2,:)); +xlabel('Time [s]'); ylabel('Amplitude [V]'); + +ax2 = nexttile; +win = hanning(floor(length(V_noise(2,:))/8)); +[pxx, f] = pwelch(V_noise(2,:), win, 0, [], Fs); +plot(f, pxx) +xlabel('Frequency [Hz]'); ylabel('Power Spectral Density [$V^2/Hz$]'); +set(gca, 'xscale', 'log'); set(gca, 'yscale', 'log'); +xlim([1, Fs/2]); ylim([1e-8, 1e-2]); +#+end_src + +#+begin_src matlab :tangle no :exports results :results file replace +exportFig('figs/exc_signal_1_noise.pdf', 'width', 'wide', 'height', 'normal'); +#+end_src + +#+name: fig:exc_signal_1_noise +#+caption: +#+RESULTS: +[[file:figs/exc_signal_1_noise.png]] + +#+begin_src matlab :exports none :tangle no +gc = 0.1; +xi = 0.5; +wn = 2*pi*94.3; + +% Notch filter at the resonance of the APA +G_sweep = 0.2*(s^2 + 2*gc*xi*wn*s + wn^2)/(s^2 + 2*xi*wn*s + wn^2); + +V_sweep = generateSweepExc('Ts', 1/Fs, ... + 'f_start', 10, ... + 'f_end', 400, ... + 'V_mean', 3.25, ... + 't_start', 5, ... + 'exc_duration', 50, ... + 'sweep_type', 'log', ... + 'V_exc', G_sweep*1/(1 + s/2/pi/500)); +#+end_src + +#+begin_src matlab :exports none :tangle no +%% Plot of the Sweep excitation signal +figure; +plot(V_sweep(1,:), V_sweep(2,:)); +xlabel('Time [s]'); ylabel('Amplitude [V]'); +#+end_src + +#+begin_src matlab :tangle no :exports results :results file replace +exportFig('figs/exc_signal_2_sweep.pdf', 'width', 'half', 'height', 'normal'); +#+end_src + +#+name: fig:exc_signal_2_sweep +#+caption: +#+RESULTS: +[[file:figs/exc_signal_2_sweep.png]] + +#+begin_src matlab :exports none :tangle no +%% High Frequency Noise +[b,a] = cheby1(10, 2, 2*pi*[300 2e3], 'bandpass', 's'); +wL = 0.005*tf(b, a); + +V_noise_hf = generateShapedNoise('Ts', 1/Fs, ... + 'V_mean', 3.25, ... + 't_start', 5, ... + 'exc_duration', 40, ... + 'smooth_ends', true, ... + 'V_exc', wL); +#+end_src + +#+begin_src matlab :exports none :tangle no +%% Plot of the excitation signal and associated PSD +figure; +tiledlayout(1, 2, 'TileSpacing', 'Normal', 'Padding', 'None'); + +ax1 = nexttile; +plot(V_noise_hf(1,:), V_noise_hf(2,:)); +xlabel('Time [s]'); ylabel('Amplitude [V]'); + +ax2 = nexttile; +win = hanning(floor(length(V_noise_hf(2,:))/8)); +[pxx, f] = pwelch(V_noise_hf(2,:), win, 0, [], Fs); +plot(f, pxx) +xlabel('Frequency [Hz]'); ylabel('Power Spectral Density [$V^2/Hz$]'); +set(gca, 'xscale', 'log'); set(gca, 'yscale', 'log'); +xlim([1, Fs/2]); ylim([1e-8, 1e-2]); +#+end_src + +#+begin_src matlab :tangle no :exports results :results file replace +exportFig('figs/exc_signal_3_hf_noise.pdf', 'width', 'wide', 'height', 'normal'); +#+end_src + +#+name: fig:exc_signal_3_hf_noise +#+caption: +#+RESULTS: +[[file:figs/exc_signal_3_hf_noise.png]] + +*** First Measurement +For this first measurement for the first APA, a basic logarithmic sweep is used between 10Hz and 2kHz. The data are loaded. #+begin_src matlab +%% Load data apa_sweep = load(sprintf('mat/frf_data_%i_sweep.mat', 1), 't', 'Va', 'Vs', 'da', 'de'); #+end_src @@ -1331,6 +1475,7 @@ t = apa_sweep.t - apa_sweep.t(1) ; % Time vector [s] The excitation signal is shown in Figure [[fig:apa_bench_exc_sweep]]. It is a sweep sine from 10Hz up to 2kHz filtered with a notch centered with the main resonance of the system and a low pass filter. #+begin_src matlab :exports none +%% Plot the excitation signal figure; plot(t, apa_sweep.Va) xlabel('Time [s]'); ylabel('Excitation Voltage $V_a$ [V]'); @@ -1345,10 +1490,10 @@ exportFig('figs/apa_bench_exc_sweep.pdf', 'width', 'wide', 'height', 'normal'); #+RESULTS: [[file:figs/apa_bench_exc_sweep.png]] -*** FRF Identification - Setup +*** FRF - Setup Let's define the sampling time/frequency. #+begin_src matlab -%% Sampling +%% Sampling Frequency / Time Ts = (t(end) - t(1))/(length(t)-1); % Sampling Time [s] Fs = 1/Ts; % Sampling Frequency [Hz] #+end_src @@ -1364,20 +1509,23 @@ We get the frequency vector that will be the same for all the frequency domain a [~, f] = tfestimate(apa_sweep.Va, apa_sweep.de, win, [], [], 1/Ts); #+end_src -*** FRF Identification - Displacement +*** FRF - Encoder and Interferometer In this section, the transfer function from the excitation voltage $V_a$ to the encoder measured displacement $d_e$ and interferometer measurement $d_a$. -The coherence from $V_a$ to $d_e$ is computed and shown in Figure [[fig:apa_1_coh_dvf]]. -It is quite good from 10Hz up to 500Hz. +The coherence from $V_a$ to $d_e$ and from $V_a$ to $d_a$ are computed and shown in Figure [[fig:apa_1_coh_dvf]]. +They are quite good from 10Hz up to 500Hz. #+begin_src matlab -%% TF - Encoder -[coh_sweep, ~] = mscohere(apa_sweep.Va, apa_sweep.de, win, [], [], 1/Ts); +%% Compute the coherence +[enc_coh, ~] = mscohere(apa_sweep.Va, apa_sweep.de, win, [], [], 1/Ts); +[int_coh, ~] = mscohere(apa_sweep.Va, apa_sweep.da, win, [], [], 1/Ts); #+end_src #+begin_src matlab :exports none +%% Plot the coherence figure; hold on; -plot(f, coh_sweep, 'k-'); +plot(f, enc_coh, 'DisplayName', '$d_e/V_a$'); +plot(f, int_coh, 'DisplayName', '$d_a/V_a$'); hold off; set(gca, 'XScale', 'log'); set(gca, 'YScale', 'lin'); xlabel('Frequency [Hz]'); ylabel('Coherence [-]'); @@ -1395,22 +1543,23 @@ exportFig('figs/apa_1_coh_dvf.pdf', 'width', 'normal', 'height', 'normal'); The transfer functions are then estimated and shown in Figure [[fig:apa_1_frf_dvf]]. #+begin_src matlab -%% TF - Encoder -[dvf_sweep, ~] = tfestimate(apa_sweep.Va, apa_sweep.de, win, [], [], 1/Ts); - -%% TF - Interferometer -[int_sweep, ~] = tfestimate(apa_sweep.Va, apa_sweep.da, win, [], [], 1/Ts); +%% TF - Encoder and interferometer +[frf_enc, ~] = tfestimate(apa_sweep.Va, apa_sweep.de, win, [], [], 1/Ts); +[frf_int, ~] = tfestimate(apa_sweep.Va, apa_sweep.da, win, [], [], 1/Ts); #+end_src +It is shown than both the encoder and interferometers are measuring the same dynamics up to $\approx 700\,Hz$. +Above that, it is possible that there is some flexible elements apart from the APA that is adding resonances into one or the other FRF. + #+begin_src matlab :exports none figure; tiledlayout(3, 1, 'TileSpacing', 'None', 'Padding', 'None'); ax1 = nexttile([2,1]); hold on; -plot(f, abs(dvf_sweep), 'color', colors(1, :), ... +plot(f, abs(frf_enc), 'color', colors(1, :), ... 'DisplayName', 'Encoder'); -plot(f, abs(int_sweep), 'color', colors(2, :), ... +plot(f, abs(frf_int), 'color', colors(2, :), ... 'DisplayName', 'Interferometer'); hold off; set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log'); @@ -1421,8 +1570,8 @@ ylim([1e-9, 1e-3]); ax2 = nexttile; hold on; -plot(f, 180/pi*angle(dvf_sweep), 'color', colors(1, :)); -plot(f, 180/pi*angle(int_sweep), 'color', colors(2, :)); +plot(f, 180/pi*angle(frf_enc), 'color', colors(1, :)); +plot(f, 180/pi*angle(frf_int), 'color', colors(2, :)); hold off; set(gca, 'XScale', 'log'); set(gca, 'YScale', 'lin'); xlabel('Frequency [Hz]'); ylabel('Phase [deg]'); @@ -1443,22 +1592,28 @@ exportFig('figs/apa_1_frf_dvf.pdf', 'width', 'wide', 'height', 'tall'); [[file:figs/apa_1_frf_dvf.png]] #+begin_important -It seems that using the interferometer, we have a lot more time delay than when using the encoder. +The transfer functions obtained in Figure [[fig:apa_1_frf_dvf]] are very close to what was expected: +- constant gain at low frequency +- resonance at around 100Hz which corresponds to the APA axial mode +- no further resonance up until high frequency ($\approx 700\,Hz$) at which points several elements of the test bench can induces resonances in the measured FRF + +However, it was not expected to observe a "double resonance" at around 95Hz (instead of only one resonance). #+end_important -*** FRF Identification - Force Sensor +*** FRF - Force Sensor Now the dynamics from excitation voltage $V_a$ to the force sensor stack voltage $V_s$ is identified. The coherence is computed and shown in Figure [[fig:apa_1_coh_iff]] and found very good from 10Hz up to 2kHz. #+begin_src matlab -%% TF - Encoder -[coh_sweep, ~] = mscohere(apa_sweep.Va, apa_sweep.Vs, win, [], [], 1/Ts); +%% Compute the coherence from Va to Vs +[iff_coh, ~] = mscohere(apa_sweep.Va, apa_sweep.Vs, win, [], [], 1/Ts); #+end_src #+begin_src matlab :exports none +%% Plot the coherence figure; hold on; -plot(f, coh_sweep, 'k-'); +plot(f, iff_coh, 'k-'); hold off; set(gca, 'XScale', 'log'); set(gca, 'YScale', 'lin'); xlabel('Frequency [Hz]'); ylabel('Coherence [-]'); @@ -1476,11 +1631,12 @@ exportFig('figs/apa_1_coh_iff.pdf', 'width', 'normal', 'height', 'normal'); The transfer function is estimated and shown in Figure [[fig:apa_1_frf_iff]]. #+begin_src matlab -%% Transfer function estimation +%% Compute the TF from Va to Vs [iff_sweep, ~] = tfestimate(apa_sweep.Va, apa_sweep.Vs, win, [], [], 1/Ts); #+end_src #+begin_src matlab :exports none +%% Plot the TF figure; tiledlayout(3, 1, 'TileSpacing', 'None', 'Padding', 'None'); @@ -1515,12 +1671,19 @@ exportFig('figs/apa_1_frf_iff.pdf', 'width', 'wide', 'height', 'tall'); #+RESULTS: [[file:figs/apa_1_frf_iff.png]] +#+begin_important +The obtained dynamics from the excitation voltage $V_a$ to the measured sensor stack voltage $V_s$ is corresponding to what was expected: +- constant gain at low frequency +- complex conjugate zero and then complex conjugate pole +- constant gain at high frequency +#+end_important + *** Hysteresis We here wish to visually see the amount of hysteresis present in the APA. To do so, a quasi static sinusoidal excitation $V_a$ at different voltages is used. -The offset is 65V, and the sin amplitude is ranging from 1V up to 80V. +The offset is 65V (halfway between -20V and 150V), and the sin amplitude is ranging from 1V up to 80V (full range). For each excitation amplitude, the vertical displacement $d$ of the mass is measured. @@ -1535,6 +1698,7 @@ We expect to obtained something like the hysteresis shown in Figure [[fig:expect The data is loaded. #+begin_src matlab +%% Load measured data - hysteresis apa_hyst = load('frf_data_1_hysteresis.mat', 't', 'Va', 'de'); % Initial time set to zero apa_hyst.t = apa_hyst.t - apa_hyst.t(1); @@ -1547,6 +1711,7 @@ ampls = [0.1, 0.2, 0.4, 1, 2, 4]; % Excitation voltage amplitudes The excitation voltage and the measured displacement are shown in Figure [[fig:hyst_exc_signal_time]]. #+begin_src matlab :exports none +%% Plot the excitation voltages and measured displacements figure; tiledlayout(1, 2, 'TileSpacing', 'None', 'Padding', 'None'); @@ -1569,10 +1734,11 @@ exportFig('figs/hyst_exc_signal_time.pdf', 'width', 'full', 'height', 'normal'); [[file:figs/hyst_exc_signal_time.png]] For each amplitude, we only take the last sinus in order to reduce possible transients. -Also, it is centered on zero. +Also, the motion is centered on zero. The measured displacement at a function of the output voltage are shown in Figure [[fig:hyst_results_multi_ampl]]. #+begin_src matlab :exports none +%% Measured displacement as a function of the output voltage figure; tiledlayout(1, 3, 'TileSpacing', 'None', 'Padding', 'None'); @@ -1608,30 +1774,29 @@ exportFig('figs/hyst_results_multi_ampl.pdf', 'width', 'full', 'height', 'tall') [[file:figs/hyst_results_multi_ampl.png]] #+begin_important -It is quite clear that hysteresis is increasing with the excitation amplitude. +From Figure [[fig:hyst_results_multi_ampl]], it is quite clear that hysteresis is increasing with the excitation amplitude. +For small excitation amplitudes ($V_a < 0.4\,V$) the hysteresis stays reasonably small. -Also, no hysteresis is found on the sensor stack voltage. +Also, it is quite interesting to see that no hysteresis is found on the sensor stack voltage when using the same excitation signal. #+end_important *** Estimation of the APA axial stiffness In order to estimate the stiffness of the APA, a weight with known mass $m_a$ is added on top of the suspended granite and the deflection $d_e$ is measured using the encoder. -The APA stiffness is then: + +The APA stiffness can then be estimated to be: \begin{equation} k_{\text{apa}} = \frac{m_a g}{d} \end{equation} -Here, a mass of 6.4 kg is used: -#+begin_src matlab -added_mass = 6.4; % Added mass [kg] -#+end_src - The data is loaded, and the measured displacement is shown in Figure [[fig:apa_1_meas_stiffness]]. #+begin_src matlab +%% Load data for stiffness measurement apa_mass = load(sprintf('frf_data_%i_add_mass_closed_circuit.mat', 1), 't', 'de'); apa_mass.de = apa_mass.de - mean(apa_mass.de(apa_mass.t<11)); #+end_src #+begin_src matlab :exports none +%% Plot the deflection at a function of time figure; plot(apa_mass.t, apa_mass.de, 'k-'); xlabel('Time [s]'); ylabel('Displacement $d_e$ [m]'); @@ -1646,7 +1811,13 @@ exportFig('figs/apa_1_meas_stiffness.pdf', 'width', 'wide', 'height', 'normal'); #+RESULTS: [[file:figs/apa_1_meas_stiffness.png]] -There is some imprecision in the measurement as there are some drifts that are probably due to some creep. +From Figure [[fig:apa_1_meas_stiffness]], it can be seen that there are some drifts that are probably due to some creep. +This will induce some uncertainties in the measured stiffness. + +Here, a mass of 6.4 kg was used: +#+begin_src matlab +added_mass = 6.4; % Added mass [kg] +#+end_src The stiffness is then computed as follows: #+begin_src matlab @@ -1661,19 +1832,46 @@ sprintf('k = %.2f [N/um]', 1e-6*k); #+RESULTS: : k = 1.68 [N/um] +The stiffness could also be estimated based on the main vertical resonance of the system at $\omega_z = 2\pi \cdot 94 \,[rad/s]$. +The suspended mass is $m_{\text{sus}} = 5\,kg$. +And therefore, the axial stiffness of the APA can be estimated to be: +\begin{equation} +k_{\text{APA}} = m_{\text{sus}} \omega_z^2 +\end{equation} + +#+begin_src matlab +wz = 2*pi*94; % [rad/s] +msus = 5.7; % [kg] +k = msus * wz^2; +#+end_src + +#+begin_src matlab :results value replace :exports results :tangle no +sprintf('k = %.2f [N/um]', 1e-6*k); +#+end_src + +#+RESULTS: +: k = 1.99 [N/um] + +The two values are found relatively close to each other. +Anyway, the stiffness of the model will be tuned to match the measured FRF. + *** Stiffness change due to electrical connections -We wish here to see if the stiffness changes when the actuator stacks are not connected to the amplifier and the sensor stacks are not connected to the ADC. +Changes in the electrical impedance connected to the piezoelectric actuator causes changes in the mechanical compliance (or stiffness) of the piezoelectric actuator. + +In this section is measured the stiffness of the APA whether the piezoelectric actuator is connected to an open circuit or a short circuit (e.g. the output of a voltage amplifier). Note here that the resistor in parallel to the sensor stack is present in both cases. First, the data are loaded. #+begin_src matlab +%% Load Data add_mass_oc = load(sprintf('frf_data_%i_add_mass_open_circuit.mat', 1), 't', 'de'); add_mass_cc = load(sprintf('frf_data_%i_add_mass_closed_circuit.mat', 1), 't', 'de'); #+end_src And the initial displacement is set to zero. #+begin_src matlab +%% Zero displacement at initial time add_mass_oc.de = add_mass_oc.de - mean(add_mass_oc.de(add_mass_oc.t<11)); add_mass_cc.de = add_mass_cc.de - mean(add_mass_cc.de(add_mass_cc.t<11)); #+end_src @@ -1698,7 +1896,6 @@ exportFig('figs/apa_meas_k_time_oc_cc.pdf', 'width', 'wide', 'height', 'normal') #+RESULTS: [[file:figs/apa_meas_k_time_oc_cc.png]] - And the stiffness is estimated in both case. The results are shown in Table [[tab:APA_measured_k_oc_cc]]. #+begin_src matlab @@ -1721,21 +1918,23 @@ data2orgtable(1e-6*[apa_k_oc; apa_k_cc], {'Not connected', 'Connected'}, {'$k [N | Connected | 1.7 | #+begin_important -Clearly, connecting the actuator stacks to the amplified (basically equivalent as to short circuiting them) lowers the stiffness. +Clearly, connecting the actuator stacks to the amplified (basically equivalent as to short circuiting them) lowers its stiffness. #+end_important *** Effect of the resistor on the IFF Plant A resistor $R \approx 80.6\,k\Omega$ is added in parallel with the sensor stack. This has the effect to form a high pass filter with the capacitance of the stack. -We here measured the low frequency transfer function from $V_a$ to $V_s$ with and without this resistor. +This is done for two reasons (explained in details [[file:../test-bench-force-sensor/test-bench-force-sensor.org][this document]]): +1. Limit the voltage offset due to the input bias current of the ADC +2. Limit the low frequency gain + +The (low frequency) transfer function from $V_a$ to $V_s$ with and without this resistor have been measured. #+begin_src matlab -% With the resistor -wi_k = load('frf_data_1_sweep_lf_with_R.mat', 't', 'Vs', 'Va'); - -% Without the resistor -wo_k = load('frf_data_1_sweep_lf.mat', 't', 'Vs', 'Va'); +%% Load the data +wi_k = load('frf_data_1_sweep_lf_with_R.mat', 't', 'Vs', 'Va'); % With the resistor +wo_k = load('frf_data_1_sweep_lf.mat', 't', 'Vs', 'Va'); % Without the resistor #+end_src We use a very long "Hanning" window for the spectral analysis in order to estimate the low frequency behavior. @@ -1745,12 +1944,14 @@ win = hanning(ceil(50*Fs)); % Hannning Windows And we estimate the transfer function from $V_a$ to $V_s$ in both cases: #+begin_src matlab +%% Compute the transfer functions from Va to Vs [frf_wo_k, f] = tfestimate(wo_k.Va, wo_k.Vs, win, [], [], 1/Ts); [frf_wi_k, ~] = tfestimate(wi_k.Va, wi_k.Vs, win, [], [], 1/Ts); #+end_src With the following values of the resistor and capacitance, we obtain a first order high pass filter with a crossover frequency equal to: #+begin_src matlab +%% Model for the high pass filter C = 5.1e-6; % Sensor Stack capacitance [F] R = 80.6e3; % Parallel Resistor [Ohm] @@ -1771,6 +1972,7 @@ G_hpf = 0.6*(s/2*pi*f0)/(1 + s/2*pi*f0); Let's compare the transfer function from actuator stack to sensor stack with and without the added resistor in Figure [[fig:frf_iff_effect_R]]. #+begin_src matlab :exports none +%% Compare the HPF model and the measured FRF figure; tiledlayout(3, 1, 'TileSpacing', 'None', 'Padding', 'None'); @@ -1811,10 +2013,14 @@ exportFig('figs/frf_iff_effect_R.pdf', 'width', 'wide', 'height', 'tall'); [[file:figs/frf_iff_effect_R.png]] #+begin_important -The added resistor has indeed the expected effect. +The added resistor has indeed the expected effect of forming an high pass filter. #+end_important ** Comparison of all the APA +:PROPERTIES: +:header-args:matlab: :tangle matlab/apa_meas_analysis_all.m +:header-args:matlab+: :comments no +:END: <> *** Introduction :ignore: The same measurements that was performed in Section [[sec:meas_one_apa]] are now performed on all the APA and then compared. @@ -1851,13 +2057,14 @@ The added mass is: added_mass = 6.4; % Added mass [kg] #+end_src -Here are the number of the APA that have been measured: +Here are the numbers of the APA that have been measured: #+begin_src matlab apa_nums = [1 2 4 5 6 7 8]; #+end_src The data are loaded. #+begin_src matlab +%% Load Data apa_mass = {}; for i = 1:length(apa_nums) apa_mass(i) = {load(sprintf('frf_data_%i_add_mass_closed_circuit.mat', apa_nums(i)), 't', 'de')}; @@ -1867,14 +2074,16 @@ end #+end_src The raw measurements are shown in Figure [[fig:apa_meas_k_time]]. -All the APA seems to have similar stiffness except the APA 7 which should have an higher stiffness. +All the APA seems to have similar stiffness except the APA 7 which show strange behavior. #+begin_question It is however strange that the displacement $d_e$ when the mass is removed is higher for the APA 7 than for the other APA. What could cause that? +It is probably due to the fact that the mechanical element holding the granite in place was not removed. #+end_question #+begin_src matlab :exports none +%% Plot the time domain measured deflection figure; hold on; for i = 1:length(apa_nums) @@ -1897,6 +2106,7 @@ exportFig('figs/apa_meas_k_time.pdf', 'width', 'wide', 'height', 'normal'); The stiffnesses are computed for all the APA and are summarized in Table [[tab:APA_measured_k]]. #+begin_src matlab :exports none +%% Compute the stiffness apa_k = zeros(length(apa_nums), 1); for i = 1:length(apa_nums) apa_k(i) = 9.8 * added_mass / (mean(apa_mass{i}.de(apa_mass{i}.t > 12 & apa_mass{i}.t < 12.5)) - mean(apa_mass{i}.de(apa_mass{i}.t > 20 & apa_mass{i}.t < 20.5))); @@ -1924,10 +2134,10 @@ end #+begin_important The APA300ML manual specifies the nominal stiffness to be $1.8\,[N/\mu m]$ which is very close to what have been measured. -Only the APA number 7 is a little bit off. +Only the APA number 7 is a little bit off, maybe there was a problem with the experimental setup. #+end_important -*** FRF Identification - Setup +*** FRF - Setup The identification is performed in three steps: 1. White noise excitation with small amplitude. This is used to determine the main resonance of the system. @@ -1938,11 +2148,6 @@ The identification is performed in three steps: Then, the result of the second identification is used between 10Hz and 350Hz and the result of the third identification if used between 350Hz and 2kHz. -Here are the APA numbers that have been measured. -#+begin_src matlab -apa_nums = [1 2 4 5 6 7 8]; -#+end_src - The data are loaded for both the second and third identification: #+begin_src matlab %% Second identification @@ -1981,7 +2186,7 @@ i_lf = f <= 350; i_hf = f > 350; #+end_src -*** FRF Identification - DVF +*** FRF - Encoder and Interferometer In this section, the dynamics from excitation voltage $V_a$ to encoder measured displacement $d_e$ is identified. We compute the coherence for 2nd and 3rd identification: @@ -1995,8 +2200,7 @@ for i = 1:length(apa_nums) end #+end_src -The coherence is shown in Figure [[fig:apa_frf_dvf_plant_coh]]. -It is clear that the Sweep sine gives good coherence up to 400Hz and that the high frequency noise excitation signal helps increasing a little bit the coherence at high frequency. +The coherence is shown in Figure [[fig:apa_frf_dvf_plant_coh]], and it is found that the coherence is good from low frequency up to 700Hz. #+begin_src matlab :exports none figure; @@ -2035,12 +2239,8 @@ The obtained transfer functions are shown in Figure [[fig:apa_frf_dvf_plant_tf]] They are all superimposed except for the APA7. #+begin_question -Why is the APA7 off? -We could think that the APA7 is stiffer, but also the mass line is off. - -It seems that there is a "gain" problem. -The encoder seems fine (it measured the same as the Interferometer). -Maybe it could be due to the amplifier? +Why is the APA7 dynamical behavior is so different from the other? +We could think that the APA7 is stiffer, but also the mass line is off, and it should indeed be identical. #+end_question #+begin_src matlab :exports none @@ -2135,7 +2335,7 @@ exportFig('figs/apa_frf_dvf_zoom_res_plant_tf.pdf', 'width', 'wide', 'height', ' #+RESULTS: [[file:figs/apa_frf_dvf_zoom_res_plant_tf.png]] -*** FRF Identification - IFF +*** FRF - Force Sensor In this section, the dynamics from $V_a$ to $V_s$ is identified. First the coherence is computed and shown in Figure [[fig:apa_frf_iff_plant_coh]]. @@ -2143,7 +2343,7 @@ The coherence is very nice from 10Hz to 2kHz. It is only dropping near a zeros at 40Hz, and near the resonance at 95Hz (the excitation amplitude being lowered). #+begin_src matlab -%% Coherence +%% Compute the Coherence coh_iff = zeros(length(f), length(apa_nums)); for i = 1:length(apa_nums) [coh_lf, ~] = mscohere(apa_sweep{i}.Va, apa_sweep{i}.Vs, win, [], [], 1/Ts); @@ -2153,6 +2353,7 @@ end #+end_src #+begin_src matlab :exports none +%% Plot the coherence figure; hold on; for i = 1:length(apa_nums) @@ -2185,6 +2386,7 @@ end #+end_src #+begin_src matlab :exports none +%% Plot the FRF from Va to Vs figure; tiledlayout(2, 1, 'TileSpacing', 'None', 'Padding', 'None'); @@ -2225,7 +2427,14 @@ exportFig('figs/apa_frf_iff_plant_tf.pdf', 'width', 'wide', 'height', 'tall'); #+RESULTS: [[file:figs/apa_frf_iff_plant_tf.png]] -*** Save measured FRF +*** Conclusion + +#+begin_important +Except the APA 7 which shows strange behavior, all the other APA are showing a very similar behavior. + +So far, all the measured FRF are showing the dynamical behavior that was expected. +#+end_important + #+begin_src matlab %% Remove the APA 7 (6 in the list) from measurements apa_nums(6) = []; @@ -2238,14 +2447,18 @@ save('matlab/mat/meas_apa_frf.mat', 'f', 'Ts', 'enc_frf', 'iff_frf', 'apa_nums') #+end_src #+begin_src matlab :eval no +%% Save the measured FRF save('mat/meas_apa_frf.mat', 'f', 'Ts', 'enc_frf', 'iff_frf', 'apa_nums'); #+end_src * Test Bench APA300ML - Simscape Model <> ** Introduction +In this section, a simscape model (Figure [[fig:model_bench_apa]]) of the measurement bench is used to compare the model of the APA with the measured FRF. -A simscape model (Figure [[fig:model_bench_apa]]) of the measurement bench is used. +After the transfer functions are extracted from the model (Section [[sec:simscape_bench_apa_first_id]]), the comparison of the obtained dynamics with the measured FRF will permit to: +1. Estimate the "actuator constant" and "sensor constant" (Section [[sec:simscape_bench_apa_id_constants]]) +2. Tune the model of the APA to match the measured dynamics (Section [[sec:simscape_bench_apa_tune_2dof_model]]) #+name: fig:model_bench_apa #+caption: Screenshot of the Simscape model @@ -2295,28 +2508,43 @@ open(mdl) #+end_src ** First Identification -The APA is first initialized with default parameters and the transfer function from excitation voltage $V_a$ (before the amplification of 20 due to the PD200 amplifier) to the sensor stack voltage $V_s$, encoder $d_L$ and interferometer $z$ is identified. +<> + +The APA is first initialized with default parameters: #+begin_src matlab -%% Initialize the structure containing the data used in the model +%% Initialize the structure with default values n_hexapod = struct(); -n_hexapod.actuator = initializeAPA('type', '2dof'); +n_hexapod.actuator = initializeAPA(... + 'type', '2dof', ... + 'Ga', 1, ... % Actuator constant [N/V] + 'Gs', 1); % Sensor constant [V/m] #+end_src +The transfer function from excitation voltage $V_a$ (before the amplification of $20$ due to the PD200 amplifier) to: +1. the sensor stack voltage $V_s$ +2. the measured displacement by the encoder $d_e$ +3. the measured displacement by the interferometer $d_a$ #+begin_src matlab %% Input/Output definition clear io; io_i = 1; -io(io_i) = linio([mdl, '/Va'], 1, 'openinput'); io_i = io_i + 1; % Actuator Voltage +io(io_i) = linio([mdl, '/Va'], 1, 'openinput'); io_i = io_i + 1; % DAC Voltage io(io_i) = linio([mdl, '/Vs'], 1, 'openoutput'); io_i = io_i + 1; % Sensor Voltage -io(io_i) = linio([mdl, '/dL'], 1, 'openoutput'); io_i = io_i + 1; % Relative Motion Outputs -io(io_i) = linio([mdl, '/z'], 1, 'openoutput'); io_i = io_i + 1; % Vertical Motion +io(io_i) = linio([mdl, '/de'], 1, 'openoutput'); io_i = io_i + 1; % Encoder +io(io_i) = linio([mdl, '/da'], 1, 'openoutput'); io_i = io_i + 1; % Interferometer %% Run the linearization Ga = linearize(mdl, io, 0.0, options); Ga.InputName = {'Va'}; -Ga.OutputName = {'Vs', 'dL', 'z'}; +Ga.OutputName = {'Vs', 'de', 'da'}; #+end_src The obtain dynamics are shown in Figure [[fig:apa_model_bench_bode_vs]] and [[fig:apa_model_bench_bode_dl_z]]. +It can be seen that: +- the shape of these bode plots are very similar to the one measured in Section [[sec:dynamical_meas_apa]] expect from a change in gain and exact location of poles and zeros +- there is a sign error for the transfer function from $V_a$ to $V_s$. + This will be corrected by taking a negative "sensor gain". +- the low frequency zero of the transfer function from $V_a$ to $V_s$ is minimum phase as expected. + The measured FRF are showing non-minimum phase zero, but it is most likely due to measurements artifacts. #+begin_src matlab :exports none %% Bode plot of the transfer function from u to taum @@ -2340,9 +2568,10 @@ set(gca, 'XScale', 'log'); set(gca, 'YScale', 'lin'); xlabel('Frequency [Hz]'); ylabel('Phase [deg]'); hold off; yticks(-360:45:360); -ylim([0, 180]) +ylim([-180, 0]) linkaxes([ax1,ax2],'x'); +xlim([freqs(1), freqs(end)]); #+end_src #+begin_src matlab :tangle no :exports results :results file replace @@ -2355,7 +2584,7 @@ exportFig('figs/apa_model_bench_bode_vs.pdf', 'width', 'wide', 'height', 'normal [[file:figs/apa_model_bench_bode_vs.png]] #+begin_src matlab :exports none -%% Bode plot of the transfer function from u to dLm and dZm +%% Bode plot of the transfer function from Va to de and da freqs = logspace(1, 3, 1000); figure; @@ -2363,29 +2592,29 @@ tiledlayout(3, 1, 'TileSpacing', 'None', 'Padding', 'None'); ax1 = nexttile([2,1]); hold on; -plot(freqs, abs(squeeze(freqresp(Ga('dL', 'Va'), freqs, 'Hz'))), 'DisplayName', 'Encoder') -plot(freqs, abs(squeeze(freqresp(Ga('z', 'Va'), freqs, 'Hz'))), 'DisplayName', 'Interferometer') +plot(freqs, abs(squeeze(freqresp(Ga('de', 'Va'), freqs, 'Hz'))), 'DisplayName', 'Encoder') +plot(freqs, abs(squeeze(freqresp(Ga('da', 'Va'), freqs, 'Hz'))), 'DisplayName', 'Interferometer') hold off; set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log'); -ylabel('Amplitude $V_s/V_a$ [V/V]'); set(gca, 'XTickLabel',[]); +ylabel('Amplitude $d/V_a$ [m/V]'); set(gca, 'XTickLabel',[]); hold off; legend('location', 'southwest'); ax2 = nexttile; hold on; -plot(freqs, 180/pi*angle(squeeze(freqresp(Ga('dL', 'Va'), freqs, 'Hz')))) -plot(freqs, 180/pi*angle(squeeze(freqresp(Ga('z', 'Va'), freqs, 'Hz')))) +plot(freqs, 180/pi*angle(squeeze(freqresp(Ga('de', 'Va'), freqs, 'Hz')))) +plot(freqs, 180/pi*angle(squeeze(freqresp(Ga('da', 'Va'), freqs, 'Hz')))) set(gca, 'XScale', 'log'); set(gca, 'YScale', 'lin'); xlabel('Frequency [Hz]'); ylabel('Phase [deg]'); hold off; yticks(-360:45:360); -ylim([-180, 180]) +ylim([-180, 0]) linkaxes([ax1,ax2],'x'); #+end_src #+begin_src matlab :tangle no :exports results :results file replace -exportFig('figs/apa_model_bench_bode_dl_z.pdf', 'width', 'wide', 'height', 'tall'); +exportFig('figs/apa_model_bench_bode_dl_z.pdf', 'width', 'wide', 'height', 'normal'); #+end_src #+name: fig:apa_model_bench_bode_dl_z @@ -2394,6 +2623,7 @@ exportFig('figs/apa_model_bench_bode_dl_z.pdf', 'width', 'wide', 'height', 'tall [[file:figs/apa_model_bench_bode_dl_z.png]] ** Identify Sensor/Actuator constants and compare with measured FRF +<> *** How to identify these constants? **** Piezoelectric Actuator Constant @@ -2408,8 +2638,8 @@ Using the Simscape model of the APA, it is possible to determine the static gain \end{equation} From the two gains, it is then easy to determine $g_a$: -\begin{equation} - g_a = \frac{F_a}{V_a} = \frac{F_a}{d} \cdot \frac{d}{V_a} = \frac{g_{d/V_a}}{g_{d/F_a}} +\begin{equation} \label{eq:actuator_constant_formula} + \boxed{g_a = \frac{F_a}{V_a} = \frac{F_a}{d} \cdot \frac{d}{V_a} = \frac{g_{d/V_a}}{g_{d/F_a}}} \end{equation} **** Piezoelectric Sensor Constant @@ -2419,7 +2649,7 @@ Similarly, it is easy to determine the gain from the excitation voltage $V_a$ to V_s = g_{V_s/V_a} V_a \end{equation} -Note here that there is an high pass filter formed by the piezo capacitor and parallel resistor. +Note here that there is an high pass filter formed by the piezoelectric capacitor and parallel resistor. The gain can be computed from the dynamical identification and taking the gain at the wanted frequency (above the first resonance). @@ -2429,41 +2659,49 @@ Using the simscape model, compute the gain at the same frequency from the actuat \end{equation} Then, the "sensor" constant is: -\begin{equation} - g_s = \frac{V_s}{dl} = \frac{V_s}{V_a} \cdot \frac{V_a}{F_a} \cdot \frac{F_a}{dl} = \frac{g_{V_s/V_a}}{g_a \cdot g_{dl/F_a}} +\begin{equation} \label{eq:sensor_constant_formula} + \boxed{g_s = \frac{V_s}{dl} = \frac{V_s}{V_a} \cdot \frac{V_a}{F_a} \cdot \frac{F_a}{dl} = \frac{g_{V_s/V_a}}{g_a \cdot g_{dl/F_a}}} \end{equation} *** Identification Data +Let's load the measured FRF from the DAC voltage to the measured encoder and to the sensor stack voltage. #+begin_src matlab +%% Load Data load('meas_apa_frf.mat', 'f', 'Ts', 'enc_frf', 'iff_frf', 'apa_nums'); #+end_src *** 2DoF APA **** 2DoF APA +Let's initialize the APA as a 2DoF model with unity sensor and actuator gains. #+begin_src matlab %% Initialize a 2DoF APA with Ga=Gs=1 -n_hexapod.actuator = initializeAPA('type', '2dof', 'ga', 1, 'gs', 1); +n_hexapod.actuator = initializeAPA(... + 'type', '2dof', ... + 'ga', 1, ... + 'gs', 1); #+end_src **** Identification without actuator or sensor constants +The transfer function from $V_a$ to $V_s$, $d_e$ and $d_a$ is identified. #+begin_src matlab %% Input/Output definition clear io; io_i = 1; -io(io_i) = linio([mdl, '/Va'], 1, 'openinput'); io_i = io_i + 1; % Actuator Voltage -io(io_i) = linio([mdl, '/Vs'], 1, 'openoutput'); io_i = io_i + 1; % Sensor Voltage -io(io_i) = linio([mdl, '/dL'], 1, 'openoutput'); io_i = io_i + 1; % Relative Motion Outputs -io(io_i) = linio([mdl, '/z'], 1, 'openoutput'); io_i = io_i + 1; % Vertical Motion +io(io_i) = linio([mdl, '/Va'], 1, 'openinput'); io_i = io_i + 1; % Actuator Voltage +io(io_i) = linio([mdl, '/Vs'], 1, 'openoutput'); io_i = io_i + 1; % Sensor Voltage +io(io_i) = linio([mdl, '/de'], 1, 'openoutput'); io_i = io_i + 1; % Encoder +io(io_i) = linio([mdl, '/da'], 1, 'openoutput'); io_i = io_i + 1; % Attocube %% Identification Gs = linearize(mdl, io, 0.0, options); Gs.InputName = {'Va'}; -Gs.OutputName = {'Vs', 'dL', 'z'}; +Gs.OutputName = {'Vs', 'de', 'da'}; #+end_src **** Actuator Constant +Then, the actuator constant can be computed as shown in Eq. eqref:eq:actuator_constant_formula by dividing the measured DC gain of the transfer function from $V_a$ to $d_e$ by the estimated DC gain of the transfer function from $V_a$ (in truth the actuator force called $F_a$) to $d_e$ using the Simscape model. #+begin_src matlab %% Estimated Actuator Constant -ga = -mean(abs(enc_frf(f>10 & f<20)))./dcgain(Gs('dL', 'Va')); % [N/V] +ga = -mean(abs(enc_frf(f>10 & f<20)))./dcgain(Gs('de', 'Va')); % [N/V] #+end_src #+begin_src matlab :results value replace :exports results :tangle no @@ -2473,11 +2711,9 @@ sprintf('ga = %.1f [N/V]', ga); #+RESULTS: : ga = -32.2 [N/V] -#+begin_src matlab -n_hexapod.actuator.Ga = ones(6,1)*ga; % Actuator gain [N/V] -#+end_src - **** Sensor Constant +Similarly, the sensor constant can be estimated using Eq. eqref:eq:sensor_constant_formula. + #+begin_src matlab %% Estimated Sensor Constant gs = -mean(abs(iff_frf(f>400 & f<500)))./(ga*abs(squeeze(freqresp(Gs('Vs', 'Va'), 1e3, 'Hz')))); % [V/m] @@ -2490,22 +2726,31 @@ sprintf('gs = %.3f [V/m]', gs); #+RESULTS: : gs = 0.088 [V/m] +**** Comparison +Let's now initialize the APA with identified sensor and actuator constant: #+begin_src matlab -n_hexapod.actuator.Gs = ones(6,1)*gs; % Sensor gain [V/m] +%% Set the identified constants +n_hexapod.actuator = initializeAPA(... + 'type', '2dof', ... + 'ga', ga, ... % Actuator gain [N/V] + 'gs', gs); % Sensor gain [V/m] #+end_src -**** Comparison -Identify the dynamics with included constants. +And identify the dynamics with included constants. #+begin_src matlab %% Identify again the dynamics with correct Ga,Gs Gs = linearize(mdl, io, 0.0, options); Gs = Gs*exp(-Ts*s); Gs.InputName = {'Va'}; -Gs.OutputName = {'Vs', 'dL', 'z'}; +Gs.OutputName = {'Vs', 'de', 'da'}; #+end_src +The transfer functions from $V_a$ to $d_e$ are compared in Figure [[fig:apa_act_constant_comp]] and the one from $V_a$ to $V_s$ are compared in Figure [[fig:apa_sens_constant_comp]]. + #+begin_src matlab :exports none -%% Bode plot of the transfer function from u to dLm (both Simscape and measured FRF) +%% Bode plot of the transfer function from u to de +freqs = logspace(1,4,1000); + figure; tiledlayout(3, 1, 'TileSpacing', 'None', 'Padding', 'None'); @@ -2515,7 +2760,7 @@ for i = 1:length(apa_nums) plot(f, abs(enc_frf(:, i)), 'color', [0,0,0,0.2]); end set(gca,'ColorOrderIndex',1); -plot(freqs, abs(squeeze(freqresp(Gs('dL', 'Va'), freqs, 'Hz')))) +plot(freqs, abs(squeeze(freqresp(Gs('de', 'Va'), freqs, 'Hz')))) hold off; set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log'); ylabel('Amplitude $d\mathcal{L}_m/u$ [m/V]'); set(gca, 'XTickLabel',[]); @@ -2528,7 +2773,7 @@ for i = 1:length(apa_nums) plot(f, 180/pi*angle(enc_frf(:,1)), 'color', [0,0,0,0.2]); end set(gca,'ColorOrderIndex',1); -plot(freqs, 180/pi*angle(squeeze(freqresp(Gs('dL', 'Va'), freqs, 'Hz')))) +plot(freqs, 180/pi*angle(squeeze(freqresp(Gs('de', 'Va'), freqs, 'Hz')))) hold off; set(gca, 'XScale', 'log'); set(gca, 'YScale', 'lin'); xlabel('Frequency [Hz]'); ylabel('Phase [deg]'); @@ -2544,12 +2789,14 @@ exportFig('figs/apa_act_constant_comp.pdf', 'width', 'wide', 'height', 'tall'); #+end_src #+name: fig:apa_act_constant_comp -#+caption: Comparison of the experimental data and Simscape model ($u$ to $d\mathcal{L}_m$) +#+caption: Comparison of the experimental data and Simscape model ($V_a$ to $d_e$) #+RESULTS: [[file:figs/apa_act_constant_comp.png]] #+begin_src matlab :exports none -%% Bode plot of the transfer function from u to taum (both Simscape and measured FRF) +%% Bode plot of the transfer function from Va to Vs (both Simscape and measured FRF) +freqs = logspace(1,4,1000); + figure; tiledlayout(3, 1, 'TileSpacing', 'None', 'Padding', 'None'); @@ -2592,25 +2839,39 @@ exportFig('figs/apa_sens_constant_comp.pdf', 'width', 'wide', 'height', 'tall'); #+RESULTS: [[file:figs/apa_sens_constant_comp.png]] +#+begin_important +The "actuator constant" and "sensor constant" can indeed be identified using this test bench. +After identifying these constants, the 2DoF model shows good agreement with the measured dynamics. +#+end_important + *** Flexible APA +**** Introduction :ignore: +In this section, the sensor and actuator "constants" are also estimated for the flexible model of the APA. + **** Flexible APA +The Simscape APA model is initialized as a flexible one with unity "constants". #+begin_src matlab %% Initialize the APA as a flexible body -n_hexapod.actuator = initializeAPA('type', 'flexible', 'ga', 1, 'gs', 1); +n_hexapod.actuator = initializeAPA(... + 'type', 'flexible', ... + 'ga', 1, ... + 'gs', 1); #+end_src **** Identification without actuator or sensor constants +The dynamics from $V_a$ to $V_s$, $d_e$ and $d_a$ is identified. #+begin_src matlab %% Identify the dynamics Gs = linearize(mdl, io, 0.0, options); Gs.InputName = {'Va'}; -Gs.OutputName = {'Vs', 'dL', 'z'}; +Gs.OutputName = {'Vs', 'de', 'da'}; #+end_src **** Actuator Constant +Then, the actuator constant can be computed as shown in Eq. eqref:eq:actuator_constant_formula: #+begin_src matlab %% Actuator Constant -ga = -mean(abs(enc_frf(f>10 & f<20)))./dcgain(Gs('dL', 'Va')); % [N/V] +ga = -mean(abs(enc_frf(f>10 & f<20)))./dcgain(Gs('de', 'Va')); % [N/V] #+end_src #+begin_src matlab :results value replace :exports results :tangle no @@ -2620,10 +2881,6 @@ sprintf('ga = %.1f [N/V]', ga); #+RESULTS: : ga = 23.5 [N/V] -#+begin_src matlab -n_hexapod.actuator.Ga = ones(6,1)*ga; % Actuator gain [N/V] -#+end_src - **** Sensor Constant #+begin_src matlab %% Sensor Constant @@ -2637,21 +2894,29 @@ sprintf('gs = %.3f [V/m]', gs); #+RESULTS: : gs = -4839841.756 [V/m] +**** Comparison +Let's now initialize the flexible APA with identified sensor and actuator constant: #+begin_src matlab -n_hexapod.actuator.Gs = ones(6,1)*gs; % Sensor gain [V/m] +%% Set the identified constants +n_hexapod.actuator = initializeAPA(... + 'type', 'flexible', ... + 'ga', ga, ... % Actuator gain [N/V] + 'gs', gs); % Sensor gain [V/m] #+end_src -**** Comparison +And identify the dynamics with included constants. #+begin_src matlab %% Identify with updated constants Gs = linearize(mdl, io, 0.0, options); Gs = Gs*exp(-Ts*s); Gs.InputName = {'Va'}; -Gs.OutputName = {'Vs', 'dL', 'z'}; +Gs.OutputName = {'Vs', 'de', 'da'}; #+end_src +The obtained dynamics is compared with the measured one in Figures [[fig:apa_act_constant_comp_flex]] and [[fig:apa_sens_constant_comp_flex]]. + #+begin_src matlab :exports none -%% Bode plot of the transfer function from u to dLm (both Simscape and measured FRF) +%% Bode plot of the transfer function from V_a to d_e (both Simscape and measured FRF) figure; tiledlayout(3, 1, 'TileSpacing', 'None', 'Padding', 'None'); @@ -2661,7 +2926,7 @@ for i = 1:length(apa_nums) plot(f, abs(enc_frf(:, i)), 'color', [0,0,0,0.2]); end set(gca,'ColorOrderIndex',1); -plot(freqs, abs(squeeze(freqresp(Gs('dL', 'Va'), freqs, 'Hz')))) +plot(freqs, abs(squeeze(freqresp(Gs('de', 'Va'), freqs, 'Hz')))) hold off; set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log'); ylabel('Amplitude $d\mathcal{L}_m/u$ [m/V]'); set(gca, 'XTickLabel',[]); @@ -2674,7 +2939,7 @@ for i = 1:length(apa_nums) plot(f, 180/pi*angle(enc_frf(:,1)), 'color', [0,0,0,0.2]); end set(gca,'ColorOrderIndex',1); -plot(freqs, 180/pi*angle(squeeze(freqresp(Gs('dL', 'Va'), freqs, 'Hz')))) +plot(freqs, 180/pi*angle(squeeze(freqresp(Gs('de', 'Va'), freqs, 'Hz')))) hold off; set(gca, 'XScale', 'log'); set(gca, 'YScale', 'lin'); xlabel('Frequency [Hz]'); ylabel('Phase [deg]'); @@ -2695,7 +2960,7 @@ exportFig('figs/apa_act_constant_comp_flex.pdf', 'width', 'wide', 'height', 'tal [[file:figs/apa_act_constant_comp_flex.png]] #+begin_src matlab :exports none -%% Bode plot of the transfer function from u to taum (both Simscape and measured FRF) +%% Bode plot of the transfer function from Va to Vs (both Simscape and measured FRF) figure; tiledlayout(3, 1, 'TileSpacing', 'None', 'Padding', 'None'); @@ -2739,15 +3004,19 @@ exportFig('figs/apa_sens_constant_comp_flex.pdf', 'width', 'wide', 'height', 'ta [[file:figs/apa_sens_constant_comp_flex.png]] #+begin_important -The flexible model is a bit "soft" compared with the experimental results. +The flexible model is a bit "soft" as compared with the experimental results. #+end_important ** Optimize 2-DoF model to fit the experimental Data +<> +The parameters of the 2DoF model presented in Section [[sec:apa_2dof_model]] are now optimize such that the model best matches the measured FRF. + +After optimization, the following parameters are used: #+begin_src matlab %% Optimized parameters n_hexapod.actuator = initializeAPA('type', '2dof', ... - 'Ga', -30.0, ... - 'Gs', 0.098, ... + 'Ga', -32.2, ... + 'Gs', 0.088, ... 'k', ones(6,1)*0.38e6, ... 'ke', ones(6,1)*1.75e6, ... 'ka', ones(6,1)*3e7, ... @@ -2762,14 +3031,16 @@ n_hexapod.actuator = initializeAPA('type', '2dof', ... clear io; io_i = 1; io(io_i) = linio([mdl, '/Va'], 1, 'openinput'); io_i = io_i + 1; % Actuator Voltage io(io_i) = linio([mdl, '/Vs'], 1, 'openoutput'); io_i = io_i + 1; % Sensor Voltage -io(io_i) = linio([mdl, '/dL'], 1, 'openoutput'); io_i = io_i + 1; % Relative Motion Outputs +io(io_i) = linio([mdl, '/de'], 1, 'openoutput'); io_i = io_i + 1; % Encoder %% Identification with optimized parameters Gs = exp(-s*Ts)*linearize(mdl, io, 0.0, options); Gs.InputName = {'Va'}; -Gs.OutputName = {'Vs', 'dL'}; +Gs.OutputName = {'Vs', 'de'}; #+end_src +The dynamics is identified using the Simscape model and compared with the measured FRF in Figure [[fig:comp_apa_plant_after_opt]]. + #+begin_src matlab :exports none %% Comparison of the experimental data and Simscape Model freqs = 5*logspace(0, 3, 1000); @@ -2782,7 +3053,7 @@ for i = 1:length(apa_nums) plot(f, abs(enc_frf(:, i)), 'color', [0,0,0,0.2]); end set(gca,'ColorOrderIndex',1); -plot(freqs, abs(squeeze(freqresp(Gs('dL', 'Va'), freqs, 'Hz')))) +plot(freqs, abs(squeeze(freqresp(Gs('de', 'Va'), freqs, 'Hz')))) hold off; set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log'); ylabel('Amplitude $d_e/V_a$ [m/V]'); set(gca, 'XTickLabel',[]); @@ -2808,7 +3079,7 @@ for i = 1:length(apa_nums) plot(f, 180/pi*angle(enc_frf(:, i)), 'color', [0,0,0,0.2]); end set(gca,'ColorOrderIndex',1); -plot(freqs, 180/pi*angle(squeeze(freqresp(Gs('dL', 'Va'), freqs, 'Hz')))) +plot(freqs, 180/pi*angle(squeeze(freqresp(Gs('de', 'Va'), freqs, 'Hz')))) hold off; set(gca, 'XScale', 'log'); set(gca, 'YScale', 'lin'); xlabel('Frequency [Hz]'); ylabel('Phase [deg]'); @@ -2841,139 +3112,9 @@ exportFig('figs/comp_apa_plant_after_opt.pdf', 'width', 'full', 'height', 'tall' #+RESULTS: [[file:figs/comp_apa_plant_after_opt.png]] -** Compare 2-DoF with flexible :noexport: -APA - 2 DoF -#+begin_src matlab -n_hexapod.actuator = initializeAPA('type', '2dof'); - -G_2dof = linearize(mdl, io, 0.0, options); -G_2dof.InputName = {'Va'}; -G_2dof.OutputName = {'Vs', 'dL', 'z'}; -#+end_src - -APA - Fully Flexible -#+begin_src matlab -n_hexapod.actuator = initializeAPA('type', 'flexible'); - -G_flex = linearize(mdl, io, 0.0, options); -G_flex.InputName = {'Va'}; -G_flex.OutputName = {'Vs', 'dL', 'z'}; -#+end_src - -Comparison -#+begin_src matlab :exports none -freqs = logspace(1, 4, 1000); - -figure; -tiledlayout(3, 1, 'TileSpacing', 'None', 'Padding', 'None'); - -ax1 = nexttile([2,1]); -hold on; -plot(freqs, abs(squeeze(freqresp(G_2dof('Vs', 'Va'), freqs, 'Hz'))), 'DisplayName', '2DoF') -plot(freqs, abs(squeeze(freqresp(G_flex('Vs', 'Va'), freqs, 'Hz'))), 'DisplayName', 'Flexible') -hold off; -set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log'); -ylabel('Amplitude $V_s/V_a$ [V/V]'); set(gca, 'XTickLabel',[]); -hold off; -legend('location', 'southeast'); - -ax2 = nexttile; -hold on; -plot(freqs, 180/pi*angle(squeeze(freqresp(G_2dof('Vs', 'Va'), freqs, 'Hz')))) -plot(freqs, 180/pi*angle(squeeze(freqresp(G_flex('Vs', 'Va'), freqs, 'Hz')))) -set(gca, 'XScale', 'log'); set(gca, 'YScale', 'lin'); -xlabel('Frequency [Hz]'); ylabel('Phase [deg]'); -hold off; -yticks(-360:45:360); -ylim([-180, 180]) - -linkaxes([ax1,ax2],'x'); -#+end_src - -#+begin_src matlab :tangle no :exports results :results file replace -exportFig('figs/apa_effect_joint_comp_vs.pdf', 'width', 'wide', 'height', 'tall'); -#+end_src - -#+name: fig:apa_effect_joint_comp_vs -#+caption: Effect of the joint's flexibility on the transfer function from $V_a$ to $V_s$ -#+RESULTS: -[[file:figs/apa_effect_joint_comp_vs.png]] - -#+begin_src matlab :exports none -freqs = logspace(1, 4, 1000); - -figure; -tiledlayout(3, 1, 'TileSpacing', 'None', 'Padding', 'None'); - -ax1 = nexttile([2,1]); -hold on; -plot(freqs, abs(squeeze(freqresp(G_2dof('dL', 'Va'), freqs, 'Hz'))), 'DisplayName', '2DoF') -plot(freqs, abs(squeeze(freqresp(G_flex('dL', 'Va'), freqs, 'Hz'))), 'DisplayName', 'Flexible') -hold off; -set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log'); -ylabel('Amplitude $d_L/V_a$ [m/V]'); set(gca, 'XTickLabel',[]); -hold off; -legend('location', 'southwest'); - -ax2 = nexttile; -hold on; -plot(freqs, 180/pi*angle(squeeze(freqresp(G_2dof('dL', 'Va'), freqs, 'Hz')))) -plot(freqs, 180/pi*angle(squeeze(freqresp(G_flex('dL', 'Va'), freqs, 'Hz')))) -set(gca, 'XScale', 'log'); set(gca, 'YScale', 'lin'); -xlabel('Frequency [Hz]'); ylabel('Phase [deg]'); -hold off; -yticks(-360:45:360); -ylim([-180, 180]) - -linkaxes([ax1,ax2],'x'); -#+end_src - -#+begin_src matlab :tangle no :exports results :results file replace -exportFig('figs/apa_effect_joint_comp_dl.pdf', 'width', 'wide', 'height', 'tall'); -#+end_src - -#+name: fig:apa_effect_joint_comp_dl -#+caption: Effect of the joint's flexibility on the transfer function from $V_a$ to $d_L$ (encoder) -#+RESULTS: -[[file:figs/apa_effect_joint_comp_dl.png]] - -#+begin_src matlab :exports none -freqs = logspace(1, 4, 1000); - -figure; -tiledlayout(3, 1, 'TileSpacing', 'None', 'Padding', 'None'); - -ax1 = nexttile([2,1]); -hold on; -plot(freqs, abs(squeeze(freqresp(G_2dof('z', 'Va'), freqs, 'Hz'))), 'DisplayName', '2DoF') -plot(freqs, abs(squeeze(freqresp(G_flex('z', 'Va'), freqs, 'Hz'))), 'DisplayName', 'Flexible') -hold off; -set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log'); -ylabel('Amplitude $z/V_a$ [m/V]'); set(gca, 'XTickLabel',[]); -hold off; -legend('location', 'southwest'); - -ax2 = nexttile; -hold on; -plot(freqs, 180/pi*angle(squeeze(freqresp(G_2dof('z', 'Va'), freqs, 'Hz')))) -plot(freqs, 180/pi*angle(squeeze(freqresp(G_flex('z', 'Va'), freqs, 'Hz')))) -set(gca, 'XScale', 'log'); set(gca, 'YScale', 'lin'); -xlabel('Frequency [Hz]'); ylabel('Phase [deg]'); -hold off; -yticks(-360:45:360); -ylim([-180, 180]) - -linkaxes([ax1,ax2],'x'); -#+end_src - -#+begin_src matlab :tangle no :exports results :results file replace -exportFig('figs/apa_effect_joint_comp_z.pdf', 'width', 'wide', 'height', 'tall'); -#+end_src - -#+name: fig:apa_effect_joint_comp_z -#+caption: Effect of the joint's flexibility on the transfer function from $V_a$ to $z$ (interferometer) -#+RESULTS: -[[file:figs/apa_effect_joint_comp_z.png]] +#+begin_important +The tuned 2DoF is very well representing the (axial) dynamics of the APA. +#+end_important * Dynamical measurements - Struts <>

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