Cercalo Test Bench
Table of Contents
- 1. Introduction
- 2. Identification of the system dynamics
- 3. Active Damping
- 4. TODO Huddle Test
- 5. Plant Scaling
- 6. Plant Analysis
- 7. Control Objective
- 8. Decentralized Control
- 9. Measurement of the non-repeatability
1 Introduction
1.1 Block Diagram
The block diagram of the setup to be controlled is shown in Fig. 1.
Figure 1: Block Diagram of the Experimental Setup
The transfer functions in the system are:
- Current Amplifier: from the voltage set by the DAC to the current going to the Cercalo's inductors \[ G_i = \begin{bmatrix} G_{i,h} & 0 \\ 0 & G_{i,v} \end{bmatrix} \text{ in } \left[ \frac{A}{V} \right] \] \[ \begin{bmatrix} I_{c,h} \\ I_{c,v} \end{bmatrix} = G_i \begin{bmatrix} U_{c,h} \\ U_{c,v} \end{bmatrix} \]
- Impedance of the Cercalo that converts the current going to the cercalo to the voltage across the cercalo: \[ Z_c = \begin{bmatrix} Z_{c,h} & 0 \\ 0 & Z_{c,v} \end{bmatrix} \text{ in } \left[ \frac{V}{A} \right] \] \[ \begin{bmatrix} \tilde{V}_{c,h} \\ \tilde{V}_{c,v} \end{bmatrix} = Z_c \begin{bmatrix} I_{c,h} \\ I_{c,v} \end{bmatrix} \]
- Voltage Amplifier: from the voltage across the Cercalo inductors to the measured voltage \[ G_a = \begin{bmatrix} G_{a,h} & 0 \\ 0 & G_{a,v} \end{bmatrix} \text{ in } \left[ \frac{V}{V} \right] \] \[ \begin{bmatrix} V_{c,h} \\ V_{c,v} \end{bmatrix} = G_a \begin{bmatrix} \tilde{V}_{c,h} \\ \tilde{V}_{c,v} \end{bmatrix} \]
- Cercalo: Transfer function from the current going through the cercalo inductors to the 4 quadrant measurement \[ G_c = \begin{bmatrix} G_{\frac{V_{p,h}}{\tilde{U}_{c,h}}} & G_{\frac{V_{p,h}}{\tilde{U}_{c,v}}} \\ G_{\frac{V_{p,v}}{\tilde{U}_{c,h}}} & G_{\frac{V_{p,v}}{\tilde{U}_{c,v}}} \end{bmatrix} \text{ in } \left[ \frac{V}{A} \right] \] \[ \begin{bmatrix} V_{p,h} \\ V_{p,v} \end{bmatrix} = G_c \begin{bmatrix} I_{c,h} \\ I_{c,v} \end{bmatrix} \]
- Newport Transfer function from the command signal of the Newport to the 4 quadrant measurement \[ G_n = \begin{bmatrix} G_{\frac{V_{p,h}}{U_{n,h}}} & G_{\frac{V_{p,h}}{U_{n,v}}} \\ G_{\frac{V_{p,v}}{U_{n,h}}} & G_{\frac{V_{n,v}}{U_{n,v}}} \end{bmatrix} \text{ in } \left[ \frac{V}{V} \right] \] \[ \begin{bmatrix} V_{p,h} \\ V_{p,v} \end{bmatrix} = G_c \begin{bmatrix} V_{n,h} \\ V_{n,v} \end{bmatrix} \]
- 4 Quadrant Diode: the gain of the 4 quadrant diode in [V/rad] is inverse in order to obtain the physical angle of the beam \[ G_d = \begin{bmatrix} G_{d,h} & 0 \\ 0 & G_{d,v} \end{bmatrix} \text{ in } \left[\frac{V}{rad}\right] \]
The block diagram with each transfer function is shown in Fig. 2.
Figure 2: Block Diagram of the Experimental Setup with detailed dynamics
1.2 Cercalo
From the Cercalo documentation, we have the parameters shown on table 1.
Maximum Stroke [deg] | Resonance Frequency [Hz] | DC Gain [mA/deg] | Gain at resonance [deg/V] | RC Resistance [Ohm] | |
---|---|---|---|---|---|
AX1 (Horizontal) | 5 | 411.13 | 28.4 | 382.9 | 9.41 |
AX2 (Vertical) | 5 | 252.5 | 35.2 | 350.4 |
The Inductance and DC resistance of the two axis of the Cercalo have been measured:
- \(L_{c,h} = 0.1\ \text{mH}\)
- \(L_{c,v} = 0.1\ \text{mH}\)
- \(R_{c,h} = 9.3\ \Omega\)
- \(R_{c,v} = 8.3\ \Omega\)
Let's first consider the horizontal direction and we try to model the Cercalo by a spring/mass/damper system (Fig. 3).
Figure 3: 1 degree-of-freedom model of the Cercalo
The equation of motion is:
\begin{align*} \frac{x}{F} &= \frac{1}{k + c s + m s^2} \\ &= \frac{G_0}{1 + 2 \xi \frac{s}{\omega_0} + \frac{s^2}{\omega_0^2}} \end{align*}with:
- \(G_0 = 1/k\) is the gain at DC in rad/N
- \(\xi = \frac{c}{2 \sqrt{km}}\) is the damping ratio of the system
- \(\omega_0 = \sqrt{\frac{k}{m}}\) is the resonance frequency in rad
The force \(F\) applied to the mass is proportional to the current \(I\) flowing through the voice coils: \[ \frac{F}{I} = \alpha \] with \(\alpha\) is in \(N/A\) and is to be determined.
The current \(I\) is also proportional to the voltage at the output of the buffer:
\begin{align*} \frac{I_c}{U_c} &= \frac{1}{(R + R_c) + L_c s} \\ &\approx 0.02 \left[ \frac{A}{V} \right] \end{align*}Let's try to determine the equivalent mass and spring values. From table 1, for the horizontal direction: \[ \left| \frac{x}{I} \right|(0) = \left| \alpha \frac{x}{F} \right|(0) = 28.4\ \frac{mA}{deg} = 1.63\ \frac{A}{rad} \]
So: \[ \alpha \frac{1}{k} = 1.63 \Longleftrightarrow k = \frac{\alpha}{1.63} \left[\frac{N}{rad}\right] \]
We also know the resonance frequency: \[ \omega_0 = 411.1\ \text{Hz} = 2583\ \frac{rad}{s} \]
And the gain at resonance:
\begin{align*} \left| \frac{x}{U_c} \right|(j\omega_0) &= \left| 0.02 \frac{x}{I_c} \right| (j\omega_0) \\ &= \left| 0.02 \alpha \frac{x}{F} \right| (j\omega_0) \\ &= 0.02 \alpha \frac{1/k}{2\xi} \\ &= 282.9\ \left[\frac{deg}{V}\right] \\ &= 4.938\ \left[\frac{rad}{V}\right] \end{align*}Thus:
\begin{align*} & \frac{\alpha}{2 \xi k} = 245 \\ \Leftrightarrow & \frac{1.63}{2 \xi} = 245 \\ \Leftrightarrow & \xi = 0.0033 \\ \Leftrightarrow & \xi = 0.33 \% \end{align*}and in terms of the physical properties:
\begin{align*} k &= \frac{\alpha}{1.63}\ \frac{N}{rad} \\ \xi &= 0.0033 \\ m &= \frac{\alpha}{1.1 \cdot 10^7}\ \frac{kg}{m^2} \end{align*}Thus, we have to determine \(\alpha\). This can be done experimentally by determining the gain at DC or at resonance of the system. For that, we need to know the angle of the mirror, thus we need to calibrate the photo-diodes. This will be done using the Newport.
1.3 Optical Setup
1.4 Newport
Parameters of the Newport are shown in Fig. 4.
It's dynamics for small angle excitation is shown in Fig. 5.
And we have:
\begin{align*} G_{n, h}(0) &= 2.62 \cdot 10^{-3}\ \frac{rad}{V} \\ G_{n, v}(0) &= 2.62 \cdot 10^{-3}\ \frac{rad}{V} \end{align*}
Figure 4: Documentation of the Newport
Figure 5: Transfer function of the Newport
1.5 4 quadrant Diode
1.6 ADC/DAC
Let's compute the theoretical noise of the ADC/DAC.
\begin{align*} \Delta V &= 20 V \\ n &= 16bits \\ q &= \Delta V/2^n = 305 \mu V \\ f_N &= 10kHz \\ \Gamma_n &= \frac{q^2}{12 f_N} = 7.76 \cdot 10^{-13} \frac{V^2}{Hz} \end{align*}with \(\Delta V\) the total range of the ADC, \(n\) its number of bits, \(q\) the quantization, \(f_N\) the sampling frequency and \(\Gamma_n\) its theoretical Power Spectral Density.
2 Identification of the system dynamics
In this section, we seek to identify all the blocks as shown in Fig. 1.
Signal | Name | Unit |
---|---|---|
Voltage Sent to Cercalo - Horizontal | Uch |
[V] |
Voltage Sent to Cercalo - Vertical | Ucv |
[V] |
Voltage Sent to Newport - Horizontal | Unh |
[V] |
Voltage Sent to Newport - Vertical | Unv |
[V] |
4Q Photodiode Measurement - Horizontal | Vph |
[V] |
4Q Photodiode Measurement - Vertical | Vpv |
[V] |
Measured Voltage across the Inductance - Horizontal | Vch |
[V] |
Measured Voltage across the Inductance - Vertical | Vcv |
[V] |
Newport Metrology - Horizontal | Vnh |
[V] |
Newport Metrology - Vertical | Vnv |
[V] |
Attocube Measurement | Va |
[m] |
All the files (data and Matlab scripts) are accessible here.
2.1 Calibration of the 4 Quadrant Diode
Prior to any dynamic identification, we would like to be able to determine the meaning of the 4 quadrant diode measurement. For instance, instead of obtaining transfer function in [V/V] from the input of the cercalo to the measurement voltage of the 4QD, we would like to obtain the transfer function in [rad/V]. This will give insight to physical interpretation.
To calibrate the 4 quadrant photo-diode, we can use the metrology included in the Newport. We can choose precisely the angle of the Newport mirror and see what is the value measured by the 4 Quadrant Diode. We then should be able to obtain the "gain" of the 4QD in [V/rad].
2.1.1 Input / Output data
The identification data is loaded
uh = load('mat/data_cal_pd_h.mat', 't', 'Vph', 'Vpv', 'Vnh'); uv = load('mat/data_cal_pd_v.mat', 't', 'Vph', 'Vpv', 'Vnv');
We remove the first seconds where the Cercalo is turned on.
t0 = 1; uh.Vph(uh.t<t0) = []; uh.Vpv(uh.t<t0) = []; uh.Vnh(uh.t<t0) = []; uh.t(uh.t<t0) = []; uh.t = uh.t - uh.t(1); % We start at t=0 t0 = 1; uv.Vph(uv.t<t0) = []; uv.Vpv(uv.t<t0) = []; uv.Vnv(uv.t<t0) = []; uv.t(uv.t<t0) = []; uv.t = uv.t - uv.t(1); % We start at t=0
2.1.2 Linear Regression to obtain the gain of the 4QD
We plot the angle of mirror
Gain of the Newport metrology in [rad/V].
gn0 = 2.62e-3;
The angular displacement of the beam is twice the angular displacement of the Newport mirror.
We do a linear regression \[ y = a x + b \] where:
- \(y\) is the measured voltage of the 4QD in [V]
- \(x\) is the beam angle (twice the mirror angle) in [rad]
- \(a\) is the identified gain of the 4QD in [rad/V]
The linear regression is shown in Fig. 10.
bh = [ones(size(uh.Vnh)) 2*gn0*uh.Vnh]\uh.Vph; bv = [ones(size(uv.Vnv)) 2*gn0*uv.Vnv]\uv.Vpv;
Thus, we obtain the "gain of the 4 quadrant photo-diode as shown on table 2.
Horizontal [V/rad] | Vertical [V/rad] |
---|---|
-31.0 | 36.3 |
Gd = tf([bh(2) 0 ; 0 bv(2)]);
We obtain:
\begin{align*} \frac{V_{qd,h}}{\alpha_{0,h}} &\approx 0.032\ \left[ \frac{rad}{V} \right] \\ &\approx 32.3\ \left[ \frac{\mu rad}{mV} \right] \end{align*} \begin{align*} \frac{V_{qd,v}}{\alpha_{0,v}} &\approx 0.028\ \left[ \frac{rad}{V} \right] \\ &\approx 27.6\ \left[ \frac{\mu rad}{mV} \right] \end{align*}2.2 Identification of the Cercalo Impedance, Current Amplifier and Voltage Amplifier dynamics
We wish here to determine \(G_i\) and \(G_a\) shown in Fig. 1.
We ignore the electro-mechanical coupling.
2.2.1 Electrical Schematic
The schematic of the electrical circuit used to drive the Cercalo is shown in Fig. 11.
Figure 11: Current Amplifier Schematic
The elements are:
- \(U_c\): the voltage generated by the DAC
- BUF: is a unity-gain open-loop buffer that allows to increase the output current
- \(R\): a chosen resistor that will determine the gain of the current amplifier
- \(L_c\): inductor present in the Cercalo
- \(R_c\): resistance of the inductor
- \(\tilde{V}_c\): voltage measured across the Cercalo's inductor
- \(V_c\): amplified voltage measured across the Cercalo's inductor
- \(I_c\) is the current going through the Cercalo's inductor
The values of the components have been measured for the horizontal and vertical directions:
- \(R_h = 41 \Omega\)
- \(L_{c,h} = 0.1 mH\)
- \(R_{c,h} = 9.3 \Omega\)
- \(R_v = 41 \Omega\)
- \(L_{c,v} = 0.1 mH\)
- \(R_{c,v} = 8.3 \Omega\)
Let's first determine the transfer function from \(U_c\) to \(I_c\).
We have that: \[ U_c = (R + R_c) I_c + L_c s I_c \]
Thus:
\begin{align} G_i(s) &= \frac{I_c}{U_c} \\ &= \frac{1}{(R + R_c) + L_c s} \\ &= \frac{G_{i,0}}{1 + s/\omega_0} \end{align}with
- \(G_{i,0} = \frac{1}{R + R_c}\)
- \(\omega_0 = \frac{R + R_c}{L_c}\)
Now, determine the transfer function from \(I_c\) to \(\tilde{V}_c\): \[ \tilde{V}_C = R_c I_c + L_c s I_c \] Thus:
\begin{align} Z_c(s) &= \frac{\tilde{V}_c}{I_c} \\ &= R_c + L_c s \end{align}Finally, the transfer function of the voltage amplifier \(G_a\) is simply a low pass filter:
\begin{align} G_a(s) &= \frac{V_c}{\tilde{V}_c} \\ &= \frac{G_{a,0}}{1 + s/\omega_c} \end{align}with
- \(G_{a,0}\) is the gain 1000 (60dB)
- \(\omega_c\) is the cut-off frequency of the voltage amplifier set to 1000Hz
2.2.2 Theoretical Transfer Functions
The values of the components in the current amplifier have been measured.
Rh = 41; % [Ohm] Lch = 0.1e-3; % [H] Rch = 9.3; % [Ohm] Rv = 41; % [Ohm] Lcv = 0.1e-3; % [H] Rcv = 8.3; % [Ohm]
Gi = blkdiag(1/(Rh + Rch + Lch * s), 1/(Rv + Rcv + Lcv * s)); Zc = blkdiag(Rch+Lch*s, Rcv+Lcv*s); Ga = blkdiag(1000/(1 + s/2/pi/1000), 1000/(1 + s/2/pi/1000));
Over the frequency band of interest, the current amplifier transfer function \(G_i\) can be considered as constant. This is the same for the impedance \(Z_c\).
Gi = tf(blkdiag(1/(Rh + Rch), 1/(Rv + Rcv))); Zc = tf(blkdiag(Rch, Rcv));
2.2.3 Identified Transfer Functions
Noise is generated using the DAC (\([U_{c,h}\ U_{c,v}]\)) and we measure the output of the voltage amplifier \([V_{c,h}, V_{c,v}]\). From that, we should be able to identify \(G_a Z_c G_i\).
The identification data is loaded.
uh = load('mat/data_uch.mat', 't', 'Uch', 'Vch'); uv = load('mat/data_ucv.mat', 't', 'Ucv', 'Vcv');
We remove the first seconds where the Cercalo is turned on.
win = hanning(ceil(1*fs)); [GaZcGi_h, f] = tfestimate(uh.Uch, uh.Vch, win, [], [], fs); [GaZcGi_v, ~] = tfestimate(uv.Ucv, uv.Vcv, win, [], [], fs);
There is a gain mismatch, that is probably due to bad identification of the inductance and resistance measurement of the cercalo inductors. Thus, we suppose \(G_a\) is perfectly known (the gain and cut-off frequency of the voltage amplifier is very accurate) and that \(G_i\) is also well determined as it mainly depends on the resistor used in the amplifier that is well measured.
Gi_resp_h = abs(GaZcGi_h)./squeeze(abs(freqresp(Ga(1,1)*Zc(1,1), f, 'Hz'))); Gi_resp_v = abs(GaZcGi_v)./squeeze(abs(freqresp(Ga(2,2)*Zc(2,2), f, 'Hz'))); Gi = tf(blkdiag(mean(Gi_resp_h(f>20 & f<200)), mean(Gi_resp_v(f>20 & f<200))));
Finally, we have the following transfer functions:
ans = filepath; if ischar(ans), fid = fopen('/tmp/babel-ZKMGJu/matlab-FA7h5L', 'w'); fprintf(fid, '%s\n', ans); fclose(fid); else, dlmwrite('/tmp/babel-ZKMGJu/matlab-FA7h5L', ans, '\t') end 'org_babel_eoe' Gi,Zc,Ga 'org_babel_eoe' ans = filepath; if ischar(ans), fid = fopen('/tmp/babel-ZKMGJu/matlab-FA7h5L', 'w'); fprintf(fid, '%s\n', ans); fclose(fid); else, dlmwrite('/tmp/babel-ZKMGJu/matlab-FA7h5L', ans, '\t') end 'org_babel_eoe' ans = 'org_babel_eoe' Gi,Zc,Ga Gi = From input 1 to output... 1: 0.01275 2: 0 From input 2 to output... 1: 0 2: 0.01382 Static gain. Zc = From input 1 to output... 1: 9.3 2: 0 From input 2 to output... 1: 0 2: 8.3 Static gain. Ga = From input 1 to output... 6.2832e+06 1: ---------- (s+6283) 2: 0 From input 2 to output... 1: 0 6.2832e+06 2: ---------- (s+6283) Continuous-time zero/pole/gain model.
2.3 Identification of the Cercalo Dynamics
We now wish to identify the dynamics of the Cercalo identified by \(G_c\) on the block diagram in Fig. 1.
To do so, we inject some noise at the input of the current amplifier \([U_{c,h},\ U_{c,v}]\) (one input after the other) and we measure simultaneously the output of the 4QD \([V_{p,h},\ V_{p,v}]\).
The transfer function obtained will be \(G_c G_i\), and because we have already identified \(G_i\), we can obtain \(G_c\) by multiplying the obtained transfer function matrix by \({G_i}^{-1}\).
2.3.1 Input / Output data
The identification data is loaded
uh = load('mat/data_uch.mat', 't', 'Uch', 'Vph', 'Vpv'); uv = load('mat/data_ucv.mat', 't', 'Ucv', 'Vph', 'Vpv');
We remove the first seconds where the Cercalo is turned on.
t0 = 1; uh.Uch(uh.t<t0) = []; uh.Vph(uh.t<t0) = []; uh.Vpv(uh.t<t0) = []; uh.t(uh.t<t0) = []; uh.t = uh.t - uh.t(1); % We start at t=0 t0 = 1; uv.Ucv(uv.t<t0) = []; uv.Vph(uv.t<t0) = []; uv.Vpv(uv.t<t0) = []; uv.t(uv.t<t0) = []; uv.t = uv.t - uv.t(1); % We start at t=0
2.3.2 Coherence
The window used for the spectral analysis is an hanning
windows with temporal size equal to 1 second.
win = hanning(ceil(1*fs));
[coh_Uch_Vph, f] = mscohere(uh.Uch, uh.Vph, win, [], [], fs); [coh_Uch_Vpv, ~] = mscohere(uh.Uch, uh.Vpv, win, [], [], fs); [coh_Ucv_Vph, ~] = mscohere(uv.Ucv, uv.Vph, win, [], [], fs); [coh_Ucv_Vpv, ~] = mscohere(uv.Ucv, uv.Vpv, win, [], [], fs);
2.3.3 Estimation of the Frequency Response Function Matrix
We compute an estimate of the transfer functions.
[tf_Uch_Vph, f] = tfestimate(uh.Uch, uh.Vph, win, [], [], fs); [tf_Uch_Vpv, ~] = tfestimate(uh.Uch, uh.Vpv, win, [], [], fs); [tf_Ucv_Vph, ~] = tfestimate(uv.Ucv, uv.Vph, win, [], [], fs); [tf_Ucv_Vpv, ~] = tfestimate(uv.Ucv, uv.Vpv, win, [], [], fs);
2.3.4 Time Delay
Now, we would like to remove the time delay included in the FRF prior to the model extraction.
Estimation of the time delay:
Ts_delay = Ts; % [s] G_delay = tf(1, 1, 'InputDelay', Ts_delay); G_delay_resp = squeeze(freqresp(G_delay, f, 'Hz'));
We then remove the time delay from the frequency response function.
tf_Uch_Vph = tf_Uch_Vph./G_delay_resp; tf_Uch_Vpv = tf_Uch_Vpv./G_delay_resp; tf_Ucv_Vph = tf_Ucv_Vph./G_delay_resp; tf_Ucv_Vpv = tf_Ucv_Vpv./G_delay_resp;
2.3.5 Extraction of a transfer function matrix
First we define the initial guess for the resonance frequencies and the weights associated.
freqs_res_uh = [410]; % [Hz] freqs_res_uv = [250]; % [Hz]
We then make an initial guess on the complex values of the poles.
xi = 0.001; % Approximate modal damping poles_uh = [2*pi*freqs_res_uh*(xi + 1i), 2*pi*freqs_res_uh*(xi - 1i)]; poles_uv = [2*pi*freqs_res_uv*(xi + 1i), 2*pi*freqs_res_uv*(xi - 1i)];
We then define the weight that will be used for the fitting. Basically, we want more weight around the resonance and at low frequency (below the first resonance). Also, we want more importance where we have a better coherence. Finally, we ignore data above some frequency.
weight_Uch_Vph = coh_Uch_Vph'; weight_Uch_Vpv = coh_Uch_Vpv'; weight_Ucv_Vph = coh_Ucv_Vph'; weight_Ucv_Vpv = coh_Ucv_Vpv'; alpha = 0.1; for freq_i = 1:length(freqs_res_uh) weight_Uch_Vph(f>(1-alpha)*freqs_res_uh(freq_i) & f<(1 + alpha)*freqs_res_uh(freq_i)) = 10; weight_Uch_Vpv(f>(1-alpha)*freqs_res_uh(freq_i) & f<(1 + alpha)*freqs_res_uh(freq_i)) = 10; weight_Ucv_Vph(f>(1-alpha)*freqs_res_uv(freq_i) & f<(1 + alpha)*freqs_res_uv(freq_i)) = 10; weight_Ucv_Vpv(f>(1-alpha)*freqs_res_uv(freq_i) & f<(1 + alpha)*freqs_res_uv(freq_i)) = 10; end weight_Uch_Vph(f>1000) = 0; weight_Uch_Vpv(f>1000) = 0; weight_Ucv_Vph(f>1000) = 0; weight_Ucv_Vpv(f>1000) = 0;
The weights are shown in Fig. 20.
When we set some options for vfit3
.
opts = struct(); opts.stable = 1; % Enforce stable poles opts.asymp = 1; % Force D matrix to be null opts.relax = 1; % Use vector fitting with relaxed non-triviality constraint opts.skip_pole = 0; % Do NOT skip pole identification opts.skip_res = 0; % Do NOT skip identification of residues (C,D,E) opts.cmplx_ss = 0; % Create real state space model with block diagonal A opts.spy1 = 0; % No plotting for first stage of vector fitting opts.spy2 = 0; % Create magnitude plot for fitting of f(s)
We define the number of iteration.
Niter = 5;
An we run the vectfit3
algorithm.
for iter = 1:Niter [SER_Uch_Vph, poles, ~, fit_Uch_Vph] = vectfit3(tf_Uch_Vph.', 1i*2*pi*f, poles_uh, weight_Uch_Vph, opts); end for iter = 1:Niter [SER_Uch_Vpv, poles, ~, fit_Uch_Vpv] = vectfit3(tf_Uch_Vpv.', 1i*2*pi*f, poles_uh, weight_Uch_Vpv, opts); end for iter = 1:Niter [SER_Ucv_Vph, poles, ~, fit_Ucv_Vph] = vectfit3(tf_Ucv_Vph.', 1i*2*pi*f, poles_uv, weight_Ucv_Vph, opts); end for iter = 1:Niter [SER_Ucv_Vpv, poles, ~, fit_Ucv_Vpv] = vectfit3(tf_Ucv_Vpv.', 1i*2*pi*f, poles_uv, weight_Ucv_Vpv, opts); end
And finally, we create the identified \(G_c\) matrix by multiplying by \({G_i}^{-1}\).
G_Uch_Vph = tf(minreal(ss(full(SER_Uch_Vph.A),SER_Uch_Vph.B,SER_Uch_Vph.C,SER_Uch_Vph.D))); G_Ucv_Vph = tf(minreal(ss(full(SER_Ucv_Vph.A),SER_Ucv_Vph.B,SER_Ucv_Vph.C,SER_Ucv_Vph.D))); G_Uch_Vpv = tf(minreal(ss(full(SER_Uch_Vpv.A),SER_Uch_Vpv.B,SER_Uch_Vpv.C,SER_Uch_Vpv.D))); G_Ucv_Vpv = tf(minreal(ss(full(SER_Ucv_Vpv.A),SER_Ucv_Vpv.B,SER_Ucv_Vpv.C,SER_Ucv_Vpv.D))); Gc = [G_Uch_Vph, G_Ucv_Vph; G_Uch_Vpv, G_Ucv_Vpv]*inv(Gi);
2.4 Identification of the Newport Dynamics
We here identify the transfer function from a reference sent to the Newport \([U_{n,h},\ U_{n,v}]\) to the measurement made by the 4QD \([V_{p,h},\ V_{p,v}]\).
To do so, we inject noise to the Newport \([U_{n,h},\ U_{n,v}]\) and we record the 4QD measurement \([V_{p,h},\ V_{p,v}]\).
2.4.1 Input / Output data
The identification data is loaded
uh = load('mat/data_unh.mat', 't', 'Unh', 'Vph', 'Vpv'); uv = load('mat/data_unv.mat', 't', 'Unv', 'Vph', 'Vpv');
We remove the first seconds where the Cercalo is turned on.
t0 = 3; uh.Unh(uh.t<t0) = []; uh.Vph(uh.t<t0) = []; uh.Vpv(uh.t<t0) = []; uh.t(uh.t<t0) = []; uh.t = uh.t - uh.t(1); % We start at t=0 t0 = 1.5; uv.Unv(uv.t<t0) = []; uv.Vph(uv.t<t0) = []; uv.Vpv(uv.t<t0) = []; uv.t(uv.t<t0) = []; uv.t = uv.t - uv.t(1); % We start at t=0
2.4.2 Coherence
The window used for the spectral analysis is an hanning
windows with temporal size equal to 1 second.
win = hanning(ceil(1*fs));
[coh_Unh_Vph, f] = mscohere(uh.Unh, uh.Vph, win, [], [], fs); [coh_Unh_Vpv, ~] = mscohere(uh.Unh, uh.Vpv, win, [], [], fs); [coh_Unv_Vph, ~] = mscohere(uv.Unv, uv.Vph, win, [], [], fs); [coh_Unv_Vpv, ~] = mscohere(uv.Unv, uv.Vpv, win, [], [], fs);
2.4.3 Estimation of the Frequency Response Function Matrix
We compute an estimate of the transfer functions.
[tf_Unh_Vph, f] = tfestimate(uh.Unh, uh.Vph, win, [], [], fs); [tf_Unh_Vpv, ~] = tfestimate(uh.Unh, uh.Vpv, win, [], [], fs); [tf_Unv_Vph, ~] = tfestimate(uv.Unv, uv.Vph, win, [], [], fs); [tf_Unv_Vpv, ~] = tfestimate(uv.Unv, uv.Vpv, win, [], [], fs);
2.4.4 Time Delay
Now, we would like to remove the time delay included in the FRF prior to the model extraction.
Estimation of the time delay:
Ts_delay = 0.0005; % [s] G_delay = tf(1, 1, 'InputDelay', Ts_delay); G_delay_resp = squeeze(freqresp(G_delay, f, 'Hz'));
We then remove the time delay from the frequency response function.
2.4.5 Extraction of a transfer function matrix
From Fig. 26, it seems reasonable to model the Newport dynamics as diagonal and constant.
Gn = blkdiag(tf(mean(abs(tf_Unh_Vph(f>10 & f<100)))), tf(mean(abs(tf_Unv_Vpv(f>10 & f<100)))));
2.5 Full System
We now have identified:
- \(G_i\)
- \(G_a\)
- \(G_c\)
- \(G_n\)
- \(G_d\)
We name the input and output of each transfer function:
Gi.InputName = {'Uch', 'Ucv'}; Gi.OutputName = {'Ich', 'Icv'}; Zc.InputName = {'Ich', 'Icv'}; Zc.OutputName = {'Vtch', 'Vtcv'}; Ga.InputName = {'Vtch', 'Vtcv'}; Ga.OutputName = {'Vch', 'Vcv'}; Gc.InputName = {'Ich', 'Icv'}; Gc.OutputName = {'Vpch', 'Vpcv'}; Gn.InputName = {'Unh', 'Unv'}; Gn.OutputName = {'Vpnh', 'Vpnv'}; Gd.InputName = {'Rh', 'Rv'}; Gd.OutputName = {'Vph', 'Vpv'};
Sh = sumblk('Vph = Vpch + Vpnh'); Sv = sumblk('Vpv = Vpcv + Vpnv');
inputs = {'Uch', 'Ucv', 'Unh', 'Unv'}; outputs = {'Vch', 'Vcv', 'Ich', 'Icv', 'Rh', 'Rv', 'Vph', 'Vpv'}; sys = connect(Gi, Zc, Ga, Gc, Gn, inv(Gd), Sh, Sv, inputs, outputs);
The file mat/plant.mat
is accessible here.
save('mat/plant.mat', 'sys', 'Gi', 'Zc', 'Ga', 'Gc', 'Gn', 'Gd');
3 Active Damping
3.1 Load Plant
load('mat/plant.mat', 'sys', 'Gi', 'Zc', 'Ga', 'Gc', 'Gn', 'Gd');
3.2 Test
bode(sys({'Vch', 'Vcv'}, {'Uch', 'Ucv'}));
Kppf = blkdiag(-10000/s, tf(0)); Kppf.InputName = {'Vch', 'Vcv'}; Kppf.OutputName = {'Uch', 'Ucv'};
inputs = {'Uch', 'Ucv', 'Unh', 'Unv'}; outputs = {'Ich', 'Icv', 'Rh', 'Rv', 'Vph', 'Vpv'}; sys_cl = connect(sys, Kppf, inputs, outputs); figure; bode(sys_cl({'Vph', 'Vpv'}, {'Uch', 'Ucv'}), sys({'Vph', 'Vpv'}, {'Uch', 'Ucv'}))
4 TODO Huddle Test
We load the data taken during the Huddle Test.
load('mat/data_huddle_test.mat', ... 't', 'Uch', 'Ucv', ... 'Unh', 'Unv', ... 'Vph', 'Vpv', ... 'Vch', 'Vcv', ... 'Vnh', 'Vnv', ... 'Va');
We remove the first second of data where everything is settling down.
t0 = 1; Uch(t<t0) = []; Ucv(t<t0) = []; Unh(t<t0) = []; Unv(t<t0) = []; Vph(t<t0) = []; Vpv(t<t0) = []; Vch(t<t0) = []; Vcv(t<t0) = []; Vnh(t<t0) = []; Vnv(t<t0) = []; Va(t<t0) = []; t(t<t0) = []; t = t - t(1); % We start at t=0
We compute the Power Spectral Density of the horizontal and vertical positions of the beam as measured by the 4 quadrant diode.
[psd_Vph, f] = pwelch(Vph, hanning(ceil(1*fs)), [], [], fs); [psd_Vpv, ~] = pwelch(Vpv, hanning(ceil(1*fs)), [], [], fs);
figure; hold on; plot(f, sqrt(psd_Vph), 'DisplayName', '$\Gamma_{Vp_h}$'); plot(f, sqrt(psd_Vpv), 'DisplayName', '$\Gamma_{Vp_v}$'); hold off; set(gca, 'xscale', 'log'); set(gca, 'yscale', 'log'); xlabel('Frequency [Hz]'); ylabel('ASD $\left[\frac{V}{\sqrt{Hz}}\right]$') legend('Location', 'southwest'); xlim([1, 1000]);
We compute the Power Spectral Density of the voltage across the inductance used for horizontal and vertical positioning of the Cercalo.
[psd_Vch, f] = pwelch(Vch, hanning(ceil(1*fs)), [], [], fs); [psd_Vcv, ~] = pwelch(Vcv, hanning(ceil(1*fs)), [], [], fs);
figure; hold on; plot(f, sqrt(psd_Vch), 'DisplayName', '$\Gamma_{Vc_h}$'); plot(f, sqrt(psd_Vcv), 'DisplayName', '$\Gamma_{Vc_v}$'); hold off; set(gca, 'xscale', 'log'); set(gca, 'yscale', 'log'); xlabel('Frequency [Hz]'); ylabel('ASD $\left[\frac{V}{\sqrt{Hz}}\right]$') legend('Location', 'southwest'); xlim([1, 1000]);
5 Plant Scaling
- measured noise
- expected perturbations
- maximum input usage
- maximum wanted error
6 Plant Analysis
6.1 Load Plant
load('mat/plant.mat', 'G');
6.2 RGA-Number
freqs = logspace(2, 4, 1000); G_resp = freqresp(G, freqs, 'Hz'); A = zeros(size(G_resp)); RGAnum = zeros(1, length(freqs)); for i = 1:length(freqs) A(:, :, i) = G_resp(:, :, i).*inv(G_resp(:, :, i))'; RGAnum(i) = sum(sum(abs(A(:, :, i)-eye(2)))); end % RGA = G0.*inv(G0)';
figure; plot(freqs, RGAnum); set(gca, 'xscale', 'log');
U = zeros(2, 2, length(freqs)); S = zeros(2, 2, length(freqs)) V = zeros(2, 2, length(freqs)); for i = 1:length(freqs) [Ui, Si, Vi] = svd(G_resp(:, :, i)); U(:, :, i) = Ui; S(:, :, i) = Si; V(:, :, i) = Vi; end
6.3 Rotation Matrix
G0 = freqresp(G, 0);
7 Control Objective
The maximum expected stroke is \(y_\text{max} = 3mm \approx 5e^{-2} rad\) at \(1Hz\). The maximum wanted error is \(e_\text{max} = 10 \mu rad\).
Thus, we require the sensitivity function at \(\omega_0 = 1\text{ Hz}\):
\begin{align*} |S(j\omega_0)| &< \left| \frac{e_\text{max}}{y_\text{max}} \right| \\ &< 2 \cdot 10^{-4} \end{align*}In terms of loop gain, this is equivalent to: \[ |L(j\omega_0)| > 5 \cdot 10^{3} \]
8 Decentralized Control
In this section, we try to implement a simple decentralized controller.
All the files (data and Matlab scripts) are accessible here.
8.1 Load Plant
load('mat/plant.mat', 'sys', 'Gi', 'Zc', 'Ga', 'Gc', 'Gn', 'Gd');
8.2 Diagonal Controller
Using SISOTOOL
, a diagonal controller is designed.
The two SISO loop gains are shown in Fig. 29.
Kh = -0.25598*(s+112)*(s^2 + 15.93*s + 6.686e06)/((s^2*(s+352.5)*(1+s/2/pi/2000))); Kv = 10207*(s+55.15)*(s^2 + 17.45*s + 2.491e06)/(s^2*(s+491.2)*(s+7695)); K = blkdiag(Kh, Kv); K.InputName = {'Rh', 'Rv'}; K.OutputName = {'Uch', 'Ucv'};
We then close the loop and we look at the transfer function from the Newport rotation signal to the beam angle (Fig. 30).
inputs = {'Uch', 'Ucv', 'Unh', 'Unv'}; outputs = {'Vch', 'Vcv', 'Ich', 'Icv', 'Rh', 'Rv', 'Vph', 'Vpv'}; sys_cl = connect(sys, -K, inputs, outputs);