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Thomas Dehaeze 2020-11-10 12:55:51 +01:00
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<title>Piezoelectric Force Sensor - Test Bench</title>
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</div><div id="content">
<h1 class="title">Piezoelectric Force Sensor - Test Bench</h1>
<div id="table-of-contents">
<h2>Table of Contents</h2>
<div id="text-table-of-contents">
<ul>
<li><a href="#orga1465ad">1. Change of Stiffness due to Sensors stack being open/closed circuit</a>
<ul>
<li><a href="#orgd924c73">1.1. Load Data</a></li>
<li><a href="#org59cc20a">1.2. Transfer Functions</a></li>
</ul>
</li>
<li><a href="#org76a1832">2. Generated Number of Charge / Voltage</a>
<ul>
<li><a href="#org1fa991d">2.1. Steps</a></li>
<li><a href="#org5e9eb44">2.2. Add Parallel Resistor</a></li>
<li><a href="#org15676e1">2.3. Sinus</a></li>
</ul>
</li>
</ul>
</div>
</div>
<p>
In this document is studied how a piezoelectric stack can be used to measured the force.
</p>
<ul class="org-ul">
<li>Section <a href="#org887b61a">1</a>: the effect of the input impedance of the electronics connected to the force sensor stack on the stiffness of the stack is studied</li>
<li>Section <a href="#org2b5f630">2</a>:</li>
</ul>
<div id="outline-container-orga1465ad" class="outline-2">
<h2 id="orga1465ad"><span class="section-number-2">1</span> Change of Stiffness due to Sensors stack being open/closed circuit</h2>
<div class="outline-text-2" id="text-1">
<p>
<a id="org887b61a"></a>
</p>
</div>
<div id="outline-container-orgd924c73" class="outline-3">
<h3 id="orgd924c73"><span class="section-number-3">1.1</span> Load Data</h3>
<div class="outline-text-3" id="text-1-1">
<div class="org-src-container">
<pre class="src src-matlab">oc = load(<span class="org-string">'identification_open_circuit.mat'</span>, <span class="org-string">'t'</span>, <span class="org-string">'encoder'</span>, <span class="org-string">'u'</span>);
sc = load(<span class="org-string">'identification_short_circuit.mat'</span>, <span class="org-string">'t'</span>, <span class="org-string">'encoder'</span>, <span class="org-string">'u'</span>);
</pre>
</div>
</div>
</div>
<div id="outline-container-org59cc20a" class="outline-3">
<h3 id="org59cc20a"><span class="section-number-3">1.2</span> Transfer Functions</h3>
<div class="outline-text-3" id="text-1-2">
<div class="org-src-container">
<pre class="src src-matlab">Ts = 1e<span class="org-type">-</span>4; <span class="org-comment">% Sampling Time [s]</span>
win = hann(ceil(10<span class="org-type">/</span>Ts));
</pre>
</div>
<div class="org-src-container">
<pre class="src src-matlab">[tf_oc_est, f] = tfestimate(oc.u, oc.encoder, win, [], [], 1<span class="org-type">/</span>Ts);
[co_oc_est, <span class="org-type">~</span>] = mscohere( oc.u, oc.encoder, win, [], [], 1<span class="org-type">/</span>Ts);
[tf_sc_est, <span class="org-type">~</span>] = tfestimate(sc.u, sc.encoder, win, [], [], 1<span class="org-type">/</span>Ts);
[co_sc_est, <span class="org-type">~</span>] = mscohere( sc.u, sc.encoder, win, [], [], 1<span class="org-type">/</span>Ts);
</pre>
</div>
<div id="org3c75143" class="figure">
<p><img src="figs/stiffness_force_sensor_coherence.png" alt="stiffness_force_sensor_coherence.png" />
</p>
</div>
<div id="org4424b1c" class="figure">
<p><img src="figs/stiffness_force_sensor_bode.png" alt="stiffness_force_sensor_bode.png" />
</p>
</div>
<div id="org216fcc3" class="figure">
<p><img src="figs/stiffness_force_sensor_bode_zoom.png" alt="stiffness_force_sensor_bode_zoom.png" />
</p>
<p><span class="figure-number">Figure 3: </span>Zoom on the change of resonance</p>
</div>
<div class="important" id="org9ea3712">
<p>
The change of resonance frequency / stiffness is very small and is not important here.
</p>
</div>
</div>
</div>
</div>
<div id="outline-container-org76a1832" class="outline-2">
<h2 id="org76a1832"><span class="section-number-2">2</span> Generated Number of Charge / Voltage</h2>
<div class="outline-text-2" id="text-2">
<p>
<a id="org2b5f630"></a>
</p>
<p>
Two stacks are used as actuator (in parallel) and one stack is used as sensor.
</p>
<p>
The amplifier gain is 20V/V (Cedrat LA75B).
</p>
</div>
<div id="outline-container-org1fa991d" class="outline-3">
<h3 id="org1fa991d"><span class="section-number-3">2.1</span> Steps</h3>
<div class="outline-text-3" id="text-2-1">
<div class="org-src-container">
<pre class="src src-matlab">load(<span class="org-string">'force_sensor_steps.mat'</span>, <span class="org-string">'t'</span>, <span class="org-string">'encoder'</span>, <span class="org-string">'u'</span>, <span class="org-string">'v'</span>);
</pre>
</div>
<div class="org-src-container">
<pre class="src src-matlab"><span class="org-type">figure</span>;
tiledlayout(2, 1, <span class="org-string">'TileSpacing'</span>, <span class="org-string">'None'</span>, <span class="org-string">'Padding'</span>, <span class="org-string">'None'</span>);
nexttile;
plot(t, v);
xlabel(<span class="org-string">'Time [s]'</span>); ylabel(<span class="org-string">'Measured voltage [V]'</span>);
nexttile;
plot(t, u);
xlabel(<span class="org-string">'Time [s]'</span>); ylabel(<span class="org-string">'Actuator Voltage [V]'</span>);
</pre>
</div>
<div id="orgf889803" class="figure">
<p><img src="figs/force_sen_steps_time_domain.png" alt="force_sen_steps_time_domain.png" />
</p>
<p><span class="figure-number">Figure 4: </span>Time domain signal during the 3 actuator voltage steps</p>
</div>
<p>
Three steps are performed at the following time intervals:
</p>
<div class="org-src-container">
<pre class="src src-matlab">t_s = [ 2.5, 23;
23.8, 35;
35.8, 50];
</pre>
</div>
<p>
Fit function:
</p>
<div class="org-src-container">
<pre class="src src-matlab">f = @(b,x) b(1)<span class="org-type">.*</span>exp(b(2)<span class="org-type">.*</span>x) <span class="org-type">+</span> b(3);
</pre>
</div>
<p>
We are interested by the <code>b(2)</code> term, which is the time constant of the exponential.
</p>
<div class="org-src-container">
<pre class="src src-matlab">tau = zeros(size(t_s, 1),1);
V0 = zeros(size(t_s, 1),1);
</pre>
</div>
<div class="org-src-container">
<pre class="src src-matlab"><span class="org-keyword">for</span> <span class="org-variable-name">t_i</span> = <span class="org-constant">1:size(t_s, 1)</span>
t_cur = t(t_s(t_i, 1) <span class="org-type">&lt;</span> t <span class="org-type">&amp;</span> t <span class="org-type">&lt;</span> t_s(t_i, 2));
t_cur = t_cur <span class="org-type">-</span> t_cur(1);
y_cur = v(t_s(t_i, 1) <span class="org-type">&lt;</span> t <span class="org-type">&amp;</span> t <span class="org-type">&lt;</span> t_s(t_i, 2));
nrmrsd = @(b) norm(y_cur <span class="org-type">-</span> f(b,t_cur)); <span class="org-comment">% Residual Norm Cost Function</span>
B0 = [0.5, <span class="org-type">-</span>0.15, 2.2]; <span class="org-comment">% Choose Appropriate Initial Estimates</span>
[B,rnrm] = fminsearch(nrmrsd, B0); <span class="org-comment">% Estimate Parameters &#8216;B&#8217;</span>
tau(t_i) = 1<span class="org-type">/</span>B(2);
V0(t_i) = B(3);
<span class="org-keyword">end</span>
</pre>
</div>
<table border="2" cellspacing="0" cellpadding="6" rules="groups" frame="hsides">
<colgroup>
<col class="org-right" />
<col class="org-right" />
</colgroup>
<thead>
<tr>
<th scope="col" class="org-right">\(tau\) [s]</th>
<th scope="col" class="org-right">\(V_0\) [V]</th>
</tr>
</thead>
<tbody>
<tr>
<td class="org-right">6.47</td>
<td class="org-right">2.26</td>
</tr>
<tr>
<td class="org-right">6.76</td>
<td class="org-right">2.26</td>
</tr>
<tr>
<td class="org-right">6.49</td>
<td class="org-right">2.25</td>
</tr>
</tbody>
</table>
<p>
With the capacitance being \(C = 4.4 \mu F\), the internal impedance of the Speedgoat ADC can be computed as follows:
</p>
<div class="org-src-container">
<pre class="src src-matlab">Cp = 4.4e<span class="org-type">-</span>6; <span class="org-comment">% [F]</span>
Rin = abs(mean(tau))<span class="org-type">/</span>Cp;
</pre>
</div>
<pre class="example">
1494100.0
</pre>
<p>
The input impedance of the Speedgoat&rsquo;s ADC should then be close to \(1.5\,M\Omega\) (specified at \(1\,M\Omega\)).
</p>
<div class="important" id="org572654b">
<p>
How can we explain the voltage offset?
</p>
</div>
<p>
As shown in Figure <a href="#org7c2c57f">5</a> (taken from (<a href="#citeproc_bib_item_1">Reza and Andrew 2006</a>)), an input voltage offset is due to the input bias current \(i_n\).
</p>
<div id="org7c2c57f" class="figure">
<p><img src="figs/force_sensor_model_electronics.png" alt="force_sensor_model_electronics.png" />
</p>
<p><span class="figure-number">Figure 5: </span>Model of a piezoelectric transducer (left) and instrumentation amplifier (right)</p>
</div>
<p>
The estimated input bias current is then:
</p>
<div class="org-src-container">
<pre class="src src-matlab">in = mean(V0)<span class="org-type">/</span>Rin;
</pre>
</div>
<pre class="example">
1.5119e-06
</pre>
<p>
An additional resistor in parallel with \(R_{in}\) would have two effects:
</p>
<ul class="org-ul">
<li>reduce the input voltage offset
\[ V_{off} = \frac{R_a R_{in}}{R_a + R_{in}} i_n \]</li>
<li>increase the high pass corner frequency \(f_c\)
\[ C_p \frac{R_{in}R_a}{R_{in} + R_a} = \tau_c = \frac{1}{f_c} \]
\[ R_a = \frac{R_i}{f_c C_p R_i - 1} \]</li>
</ul>
<p>
If we allow the high pass corner frequency to be equals to 3Hz:
</p>
<div class="org-src-container">
<pre class="src src-matlab">fc = 3;
Ra = Rin<span class="org-type">/</span>(fc<span class="org-type">*</span>Cp<span class="org-type">*</span>Rin <span class="org-type">-</span> 1);
</pre>
</div>
<pre class="example">
79804
</pre>
<p>
With this parallel resistance value, the voltage offset would be:
</p>
<div class="org-src-container">
<pre class="src src-matlab">V_offset = Ra<span class="org-type">*</span>Rin<span class="org-type">/</span>(Ra <span class="org-type">+</span> Rin) <span class="org-type">*</span> in;
</pre>
</div>
<pre class="example">
0.11454
</pre>
<p>
Which is much more acceptable.
</p>
</div>
</div>
<div id="outline-container-org5e9eb44" class="outline-3">
<h3 id="org5e9eb44"><span class="section-number-3">2.2</span> Add Parallel Resistor</h3>
<div class="outline-text-3" id="text-2-2">
<p>
A resistor \(R_p \approx 100\,k\Omega\) is added in parallel with the force sensor as shown in Figure <a href="#org1fac5a7">6</a>.
</p>
<div id="org1fac5a7" class="figure">
<p><img src="figs/force_sensor_model_electronics_without_R.png" alt="force_sensor_model_electronics_without_R.png" />
</p>
<p><span class="figure-number">Figure 6: </span>Model of a piezoelectric transducer (left) and instrumentation amplifier (right) with added resistor \(R_p\)</p>
</div>
<div class="org-src-container">
<pre class="src src-matlab">load(<span class="org-string">'force_sensor_steps_R_82k7.mat'</span>, <span class="org-string">'t'</span>, <span class="org-string">'encoder'</span>, <span class="org-string">'u'</span>, <span class="org-string">'v'</span>);
</pre>
</div>
<div class="org-src-container">
<pre class="src src-matlab"><span class="org-type">figure</span>;
tiledlayout(2, 1, <span class="org-string">'TileSpacing'</span>, <span class="org-string">'None'</span>, <span class="org-string">'Padding'</span>, <span class="org-string">'None'</span>);
nexttile;
plot(t, v);
xlabel(<span class="org-string">'Time [s]'</span>); ylabel(<span class="org-string">'Measured voltage [V]'</span>);
nexttile;
plot(t, u);
xlabel(<span class="org-string">'Time [s]'</span>); ylabel(<span class="org-string">'Actuator Voltage [V]'</span>);
</pre>
</div>
<div id="org29964b5" class="figure">
<p><img src="figs/force_sen_steps_time_domain_par_R.png" alt="force_sen_steps_time_domain_par_R.png" />
</p>
<p><span class="figure-number">Figure 7: </span>Time domain signal during the actuator voltage steps</p>
</div>
<p>
Three steps are performed at the following time intervals:
</p>
<div class="org-src-container">
<pre class="src src-matlab">t_s = [1.9, 6;
8.5, 13;
15.5, 21;
22.6, 26;
30.0, 36;
37.5, 41;
46.2, 49.5]
</pre>
</div>
<p>
Fit function:
</p>
<div class="org-src-container">
<pre class="src src-matlab">f = @(b,x) b(1)<span class="org-type">.*</span>exp(b(2)<span class="org-type">.*</span>x) <span class="org-type">+</span> b(3);
</pre>
</div>
<p>
We are interested by the <code>b(2)</code> term, which is the time constant of the exponential.
</p>
<div class="org-src-container">
<pre class="src src-matlab">tau = zeros(size(t_s, 1),1);
V0 = zeros(size(t_s, 1),1);
</pre>
</div>
<div class="org-src-container">
<pre class="src src-matlab"><span class="org-keyword">for</span> <span class="org-variable-name">t_i</span> = <span class="org-constant">1:size(t_s, 1)</span>
t_cur = t(t_s(t_i, 1) <span class="org-type">&lt;</span> t <span class="org-type">&amp;</span> t <span class="org-type">&lt;</span> t_s(t_i, 2));
t_cur = t_cur <span class="org-type">-</span> t_cur(1);
y_cur = v(t_s(t_i, 1) <span class="org-type">&lt;</span> t <span class="org-type">&amp;</span> t <span class="org-type">&lt;</span> t_s(t_i, 2));
nrmrsd = @(b) norm(y_cur <span class="org-type">-</span> f(b,t_cur)); <span class="org-comment">% Residual Norm Cost Function</span>
B0 = [0.5, <span class="org-type">-</span>0.2, 0.2]; <span class="org-comment">% Choose Appropriate Initial Estimates</span>
[B,rnrm] = fminsearch(nrmrsd, B0); <span class="org-comment">% Estimate Parameters &#8216;B&#8217;</span>
tau(t_i) = 1<span class="org-type">/</span>B(2);
V0(t_i) = B(3);
<span class="org-keyword">end</span>
</pre>
</div>
<p>
And indeed, we obtain a much smaller offset voltage and a much faster time constant.
</p>
<table border="2" cellspacing="0" cellpadding="6" rules="groups" frame="hsides">
<colgroup>
<col class="org-right" />
<col class="org-right" />
</colgroup>
<thead>
<tr>
<th scope="col" class="org-right">\(tau\) [s]</th>
<th scope="col" class="org-right">\(V_0\) [V]</th>
</tr>
</thead>
<tbody>
<tr>
<td class="org-right">0.43</td>
<td class="org-right">0.15</td>
</tr>
<tr>
<td class="org-right">0.45</td>
<td class="org-right">0.16</td>
</tr>
<tr>
<td class="org-right">0.43</td>
<td class="org-right">0.15</td>
</tr>
<tr>
<td class="org-right">0.43</td>
<td class="org-right">0.15</td>
</tr>
<tr>
<td class="org-right">0.45</td>
<td class="org-right">0.15</td>
</tr>
<tr>
<td class="org-right">0.46</td>
<td class="org-right">0.16</td>
</tr>
<tr>
<td class="org-right">0.48</td>
<td class="org-right">0.16</td>
</tr>
</tbody>
</table>
<p>
Knowing the capacitance value, we can estimate the value of the added resistor (neglecting the input impedance of \(\approx 1\,M\Omega\)):
</p>
<div class="org-src-container">
<pre class="src src-matlab">Cp = 4.4e<span class="org-type">-</span>6; <span class="org-comment">% [F]</span>
Rin = abs(mean(tau))<span class="org-type">/</span>Cp;
</pre>
</div>
<pre class="example">
101200.0
</pre>
<p>
And we can verify that the bias current estimation stays the same:
</p>
<div class="org-src-container">
<pre class="src src-matlab">in = mean(V0)<span class="org-type">/</span>Rin;
</pre>
</div>
<pre class="example">
1.5305e-06
</pre>
<p>
This validates the model of the ADC and the effectiveness of the added resistor.
</p>
</div>
</div>
<div id="outline-container-org15676e1" class="outline-3">
<h3 id="org15676e1"><span class="section-number-3">2.3</span> Sinus</h3>
<div class="outline-text-3" id="text-2-3">
<div class="org-src-container">
<pre class="src src-matlab">load(<span class="org-string">'force_sensor_sin.mat'</span>, <span class="org-string">'t'</span>, <span class="org-string">'encoder'</span>, <span class="org-string">'u'</span>, <span class="org-string">'v'</span>);
u = u(t<span class="org-type">&gt;</span>25);
v = v(t<span class="org-type">&gt;</span>25);
encoder = encoder(t<span class="org-type">&gt;</span>25) <span class="org-type">-</span> mean(encoder(t<span class="org-type">&gt;</span>25));
t = t(t<span class="org-type">&gt;</span>25);
</pre>
</div>
<p>
The driving voltage is a sinus at 0.5Hz centered on 3V and with an amplitude of 3V (Figure <a href="#org1fbf89d">8</a>).
</p>
<div id="org1fbf89d" class="figure">
<p><img src="figs/force_sensor_sin_u.png" alt="force_sensor_sin_u.png" />
</p>
<p><span class="figure-number">Figure 8: </span>Driving Voltage</p>
</div>
<p>
The full stroke as measured by the encoder is:
</p>
<div class="org-src-container">
<pre class="src src-matlab">max(encoder)<span class="org-type">-</span>min(encoder)
</pre>
</div>
<pre class="example">
5.005e-05
</pre>
<p>
Its signal is shown in Figure <a href="#org1d74efa">9</a>.
</p>
<div id="org1d74efa" class="figure">
<p><img src="figs/force_sensor_sin_encoder.png" alt="force_sensor_sin_encoder.png" />
</p>
<p><span class="figure-number">Figure 9: </span>Encoder measurement</p>
</div>
<p>
The generated voltage by the stack is shown in Figure
</p>
<div id="org077a6d7" class="figure">
<p><img src="figs/force_sensor_sin_stack.png" alt="force_sensor_sin_stack.png" />
</p>
<p><span class="figure-number">Figure 10: </span>Voltage measured on the stack used as a sensor</p>
</div>
<p>
The capacitance of the stack is
</p>
<div class="org-src-container">
<pre class="src src-matlab">Cp = 4.4e<span class="org-type">-</span>6; <span class="org-comment">% [F]</span>
</pre>
</div>
<p>
The corresponding generated charge is then shown in Figure <a href="#org4baf062">11</a>.
</p>
<div id="org4baf062" class="figure">
<p><img src="figs/force_sensor_sin_charge.png" alt="force_sensor_sin_charge.png" />
</p>
<p><span class="figure-number">Figure 11: </span>Generated Charge</p>
</div>
<p>
The relation between the generated voltage and the measured displacement is almost linear as shown in Figure <a href="#org8b9df34">12</a>.
</p>
<div class="org-src-container">
<pre class="src src-matlab">b1 = encoder<span class="org-type">\</span>(v<span class="org-type">-</span>mean(v));
</pre>
</div>
<div id="org8b9df34" class="figure">
<p><img src="figs/force_sensor_linear_relation.png" alt="force_sensor_linear_relation.png" />
</p>
<p><span class="figure-number">Figure 12: </span>Almost linear relation between the relative displacement and the generated voltage</p>
</div>
<p>
With a 16bits ADC, the resolution will then be equals to (in [nm]):
</p>
<div class="org-src-container">
<pre class="src src-matlab">abs((20<span class="org-type">/</span>2<span class="org-type">^</span>16)<span class="org-type">/</span>(b1<span class="org-type">/</span>1e9))
</pre>
</div>
<pre class="example">
3.9838
</pre>
<style>.csl-entry{text-indent: -1.5em; margin-left: 1.5em;}</style><h2 class='citeproc-org-bib-h2'>Bibliography</h2>
<div class="csl-bib-body">
<div class="csl-entry"><a name="citeproc_bib_item_1"></a>Reza, Moheimani, and Fleming Andrew. 2006. <i>Piezoelectric Transducers for Vibration Control and Damping</i>. London: Springer.</div>
</div>
</div>
</div>
</div>
</div>
<div id="postamble" class="status">
<p class="author">Author: Dehaeze Thomas</p>
<p class="date">Created: 2020-11-10 mar. 12:55</p>
</div>
</body>
</html>

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#+TITLE: Piezoelectric Force Sensor - Test Bench
:DRAWER:
#+LANGUAGE: en
#+EMAIL: dehaeze.thomas@gmail.com
#+AUTHOR: Dehaeze Thomas
#+HTML_LINK_HOME: ../index.html
#+HTML_LINK_UP: ../index.html
#+HTML_HEAD: <link rel="stylesheet" type="text/css" href="./css/htmlize.css"/>
#+HTML_HEAD: <link rel="stylesheet" type="text/css" href="./css/readtheorg.css"/>
#+HTML_HEAD: <link rel="stylesheet" type="text/css" href="./css/custom.css"/>
#+HTML_HEAD: <script type="text/javascript" src="./js/jquery.min.js"></script>
#+HTML_HEAD: <script type="text/javascript" src="./js/bootstrap.min.js"></script>
#+HTML_HEAD: <script type="text/javascript" src="./js/readtheorg.js"></script>
#+PROPERTY: header-args:latex :headers '("\\usepackage{tikz}" "\\usepackage{import}" "\\import{$HOME/Cloud/tikz/org/}{config.tex}")
#+PROPERTY: header-args:latex+ :imagemagick t :fit yes
#+PROPERTY: header-args:latex+ :iminoptions -scale 100% -density 150
#+PROPERTY: header-args:latex+ :imoutoptions -quality 100
#+PROPERTY: header-args:latex+ :results raw replace :buffer no
#+PROPERTY: header-args:latex+ :eval no-export
#+PROPERTY: header-args:latex+ :exports both
#+PROPERTY: header-args:latex+ :mkdirp yes
#+PROPERTY: header-args:latex+ :output-dir figs
#+PROPERTY: header-args:latex+ :post pdf2svg(file=*this*, ext="png")
#+PROPERTY: header-args:matlab :session *MATLAB*
#+PROPERTY: header-args:matlab+ :comments org
#+PROPERTY: header-args:matlab+ :exports both
#+PROPERTY: header-args:matlab+ :results none
#+PROPERTY: header-args:matlab+ :eval no-export
#+PROPERTY: header-args:matlab+ :noweb yes
#+PROPERTY: header-args:matlab+ :mkdirp yes
#+PROPERTY: header-args:matlab+ :output-dir figs
:END:
* Introduction :ignore:
In this document is studied how a piezoelectric stack can be used to measured the force.
- Section [[sec:open_closed_circuit]]: the effect of the input impedance of the electronics connected to the force sensor stack on the stiffness of the stack is studied
- Section [[sec:charge_voltage_estimation]]:
* Change of Stiffness due to Sensors stack being open/closed circuit
:PROPERTIES:
:header-args:matlab+: :tangle matlab/open_closed_circuit.m
:END:
<<sec:open_closed_circuit>>
** Introduction :ignore:
** Matlab Init :noexport:ignore:
#+begin_src matlab :tangle no :exports none :results silent :noweb yes :var current_dir=(file-name-directory buffer-file-name)
<<matlab-dir>>
#+end_src
#+begin_src matlab :exports none :results silent :noweb yes
<<matlab-init>>
#+end_src
#+begin_src matlab :tangle no
addpath('./matlab/mat/');
#+end_src
#+begin_src matlab :eval no
addpath('./mat/');
#+end_src
** Load Data
#+begin_src matlab
oc = load('identification_open_circuit.mat', 't', 'encoder', 'u');
sc = load('identification_short_circuit.mat', 't', 'encoder', 'u');
#+end_src
** Transfer Functions
#+begin_src matlab
Ts = 1e-4; % Sampling Time [s]
win = hann(ceil(10/Ts));
#+end_src
#+begin_src matlab
[tf_oc_est, f] = tfestimate(oc.u, oc.encoder, win, [], [], 1/Ts);
[co_oc_est, ~] = mscohere( oc.u, oc.encoder, win, [], [], 1/Ts);
[tf_sc_est, ~] = tfestimate(sc.u, sc.encoder, win, [], [], 1/Ts);
[co_sc_est, ~] = mscohere( sc.u, sc.encoder, win, [], [], 1/Ts);
#+end_src
#+begin_src matlab :exports none
figure;
hold on;
plot(f, co_oc_est, '-')
plot(f, co_sc_est, '-')
set(gca, 'Xscale', 'log'); set(gca, 'Yscale', 'lin');
ylabel('Coherence'); xlabel('Frequency [Hz]');
hold off;
xlim([0.5, 5e3]);
#+end_src
#+begin_src matlab :tangle no :exports results :results file replace
exportFig('figs/stiffness_force_sensor_coherence.pdf', 'width', 'wide', 'height', 'normal');
#+end_src
#+name: fig:stiffness_force_sensor_coherence
#+caption:
#+RESULTS:
[[file:figs/stiffness_force_sensor_coherence.png]]
#+begin_src matlab :exports none
figure;
tiledlayout(2, 1, 'TileSpacing', 'None', 'Padding', 'None');
ax1 = nexttile;
hold on;
plot(f, abs(tf_oc_est), '-', 'DisplayName', 'Open-Circuit')
plot(f, abs(tf_sc_est), '-', 'DisplayName', 'Short-Circuit')
set(gca, 'Xscale', 'log'); set(gca, 'Yscale', 'log');
ylabel('Amplitude'); set(gca, 'XTickLabel',[]);
hold off;
ylim([1e-7, 3e-4]);
legend('location', 'southwest');
ax2 = nexttile;
hold on;
plot(f, 180/pi*angle(tf_oc_est), '-')
plot(f, 180/pi*angle(tf_sc_est), '-')
set(gca, 'Xscale', 'log'); set(gca, 'Yscale', 'lin');
ylabel('Phase'); xlabel('Frequency [Hz]');
hold off;
yticks(-360:90:360);
axis padded 'auto x'
linkaxes([ax1,ax2], 'x');
xlim([0.5, 5e3]);
#+end_src
#+begin_src matlab :tangle no :exports results :results file replace
exportFig('figs/stiffness_force_sensor_bode.pdf', 'width', 'wide', 'height', 'tall');
#+end_src
#+name: fig:stiffness_force_sensor_bode
#+caption:
#+RESULTS:
[[file:figs/stiffness_force_sensor_bode.png]]
#+begin_src matlab :tangle no :exports results :results file replace
xlim([180, 280]);
exportFig('figs/stiffness_force_sensor_bode_zoom.pdf', 'width', 'small', 'height', 'tall');
#+end_src
#+name: fig:stiffness_force_sensor_bode_zoom
#+caption: Zoom on the change of resonance
#+RESULTS:
[[file:figs/stiffness_force_sensor_bode_zoom.png]]
#+begin_important
The change of resonance frequency / stiffness is very small and is not important here.
#+end_important
* Generated Number of Charge / Voltage
:PROPERTIES:
:header-args:matlab+: :tangle matlab/charge_voltage_estimation.m
:END:
<<sec:charge_voltage_estimation>>
** Introduction :ignore:
Two stacks are used as actuator (in parallel) and one stack is used as sensor.
The amplifier gain is 20V/V (Cedrat LA75B).
** Matlab Init :noexport:ignore:
#+begin_src matlab :tangle no :exports none :results silent :noweb yes :var current_dir=(file-name-directory buffer-file-name)
<<matlab-dir>>
#+end_src
#+begin_src matlab :exports none :results silent :noweb yes
<<matlab-init>>
#+end_src
#+begin_src matlab :tangle no
addpath('./matlab/mat/');
#+end_src
#+begin_src matlab :eval no
addpath('./mat/');
#+end_src
** Steps
#+begin_src matlab
load('force_sensor_steps.mat', 't', 'encoder', 'u', 'v');
#+end_src
#+begin_src matlab
figure;
tiledlayout(2, 1, 'TileSpacing', 'None', 'Padding', 'None');
nexttile;
plot(t, v);
xlabel('Time [s]'); ylabel('Measured voltage [V]');
nexttile;
plot(t, u);
xlabel('Time [s]'); ylabel('Actuator Voltage [V]');
#+end_src
#+begin_src matlab :tangle no :exports results :results file replace
exportFig('figs/force_sen_steps_time_domain.pdf', 'width', 'wide', 'height', 'tall');
#+end_src
#+name: fig:force_sen_steps_time_domain
#+caption: Time domain signal during the 3 actuator voltage steps
#+RESULTS:
[[file:figs/force_sen_steps_time_domain.png]]
Three steps are performed at the following time intervals:
#+begin_src matlab
t_s = [ 2.5, 23;
23.8, 35;
35.8, 50];
#+end_src
Fit function:
#+begin_src matlab
f = @(b,x) b(1).*exp(b(2).*x) + b(3);
#+end_src
We are interested by the =b(2)= term, which is the time constant of the exponential.
#+begin_src matlab
tau = zeros(size(t_s, 1),1);
V0 = zeros(size(t_s, 1),1);
#+end_src
#+begin_src matlab
for t_i = 1:size(t_s, 1)
t_cur = t(t_s(t_i, 1) < t & t < t_s(t_i, 2));
t_cur = t_cur - t_cur(1);
y_cur = v(t_s(t_i, 1) < t & t < t_s(t_i, 2));
nrmrsd = @(b) norm(y_cur - f(b,t_cur)); % Residual Norm Cost Function
B0 = [0.5, -0.15, 2.2]; % Choose Appropriate Initial Estimates
[B,rnrm] = fminsearch(nrmrsd, B0); % Estimate Parameters B
tau(t_i) = 1/B(2);
V0(t_i) = B(3);
end
#+end_src
#+begin_src matlab :exports results :results value table replace :tangle no :post addhdr(*this*)
data2orgtable([abs(tau), V0], {}, {'$tau$ [s]', '$V_0$ [V]'}, ' %.2f ');
#+end_src
#+RESULTS:
| $tau$ [s] | $V_0$ [V] |
|-----------+-----------|
| 6.47 | 2.26 |
| 6.76 | 2.26 |
| 6.49 | 2.25 |
With the capacitance being $C = 4.4 \mu F$, the internal impedance of the Speedgoat ADC can be computed as follows:
#+begin_src matlab
Cp = 4.4e-6; % [F]
Rin = abs(mean(tau))/Cp;
#+end_src
#+begin_src matlab :results value replace :exports results
ans = Rin
#+end_src
#+RESULTS:
: 1494100.0
The input impedance of the Speedgoat's ADC should then be close to $1.5\,M\Omega$ (specified at $1\,M\Omega$).
#+begin_important
How can we explain the voltage offset?
#+end_important
As shown in Figure [[fig:force_sensor_model_electronics]] (taken from cite:reza06_piezoel_trans_vibrat_contr_dampin), an input voltage offset is due to the input bias current $i_n$.
#+name: fig:force_sensor_model_electronics
#+caption: Model of a piezoelectric transducer (left) and instrumentation amplifier (right)
[[file:figs/force_sensor_model_electronics.png]]
The estimated input bias current is then:
#+begin_src matlab
in = mean(V0)/Rin;
#+end_src
#+begin_src matlab :results value replace :exports results
ans = in
#+end_src
#+RESULTS:
: 1.5119e-06
An additional resistor in parallel with $R_{in}$ would have two effects:
- reduce the input voltage offset
\[ V_{off} = \frac{R_a R_{in}}{R_a + R_{in}} i_n \]
- increase the high pass corner frequency $f_c$
\[ C_p \frac{R_{in}R_a}{R_{in} + R_a} = \tau_c = \frac{1}{f_c} \]
\[ R_a = \frac{R_i}{f_c C_p R_i - 1} \]
If we allow the high pass corner frequency to be equals to 3Hz:
#+begin_src matlab
fc = 3;
Ra = Rin/(fc*Cp*Rin - 1);
#+end_src
#+begin_src matlab :results value replace :exports results
ans = Ra
#+end_src
#+RESULTS:
: 79804
With this parallel resistance value, the voltage offset would be:
#+begin_src matlab
V_offset = Ra*Rin/(Ra + Rin) * in;
#+end_src
#+begin_src matlab :results value replace :exports results
ans = V_offset
#+end_src
#+RESULTS:
: 0.11454
Which is much more acceptable.
** Add Parallel Resistor
A resistor $R_p \approx 100\,k\Omega$ is added in parallel with the force sensor as shown in Figure [[fig:force_sensor_model_electronics_without_R]].
#+name: fig:force_sensor_model_electronics_without_R
#+caption: Model of a piezoelectric transducer (left) and instrumentation amplifier (right) with added resistor $R_p$
[[file:figs/force_sensor_model_electronics_without_R.png]]
#+begin_src matlab
load('force_sensor_steps_R_82k7.mat', 't', 'encoder', 'u', 'v');
#+end_src
#+begin_src matlab
figure;
tiledlayout(2, 1, 'TileSpacing', 'None', 'Padding', 'None');
nexttile;
plot(t, v);
xlabel('Time [s]'); ylabel('Measured voltage [V]');
nexttile;
plot(t, u);
xlabel('Time [s]'); ylabel('Actuator Voltage [V]');
#+end_src
#+begin_src matlab :tangle no :exports results :results file replace
exportFig('figs/force_sen_steps_time_domain_par_R.pdf', 'width', 'wide', 'height', 'tall');
#+end_src
#+name: fig:force_sen_steps_time_domain_par_R
#+caption: Time domain signal during the actuator voltage steps
#+RESULTS:
[[file:figs/force_sen_steps_time_domain_par_R.png]]
Three steps are performed at the following time intervals:
#+begin_src matlab
t_s = [1.9, 6;
8.5, 13;
15.5, 21;
22.6, 26;
30.0, 36;
37.5, 41;
46.2, 49.5]
#+end_src
Fit function:
#+begin_src matlab
f = @(b,x) b(1).*exp(b(2).*x) + b(3);
#+end_src
We are interested by the =b(2)= term, which is the time constant of the exponential.
#+begin_src matlab
tau = zeros(size(t_s, 1),1);
V0 = zeros(size(t_s, 1),1);
#+end_src
#+begin_src matlab
for t_i = 1:size(t_s, 1)
t_cur = t(t_s(t_i, 1) < t & t < t_s(t_i, 2));
t_cur = t_cur - t_cur(1);
y_cur = v(t_s(t_i, 1) < t & t < t_s(t_i, 2));
nrmrsd = @(b) norm(y_cur - f(b,t_cur)); % Residual Norm Cost Function
B0 = [0.5, -0.2, 0.2]; % Choose Appropriate Initial Estimates
[B,rnrm] = fminsearch(nrmrsd, B0); % Estimate Parameters B
tau(t_i) = 1/B(2);
V0(t_i) = B(3);
end
#+end_src
And indeed, we obtain a much smaller offset voltage and a much faster time constant.
#+begin_src matlab :exports results :results value table replace :tangle no :post addhdr(*this*)
data2orgtable([abs(tau), V0], {}, {'$tau$ [s]', '$V_0$ [V]'}, ' %.2f ');
#+end_src
#+RESULTS:
| $tau$ [s] | $V_0$ [V] |
|-----------+-----------|
| 0.43 | 0.15 |
| 0.45 | 0.16 |
| 0.43 | 0.15 |
| 0.43 | 0.15 |
| 0.45 | 0.15 |
| 0.46 | 0.16 |
| 0.48 | 0.16 |
Knowing the capacitance value, we can estimate the value of the added resistor (neglecting the input impedance of $\approx 1\,M\Omega$):
#+begin_src matlab
Cp = 4.4e-6; % [F]
Rin = abs(mean(tau))/Cp;
#+end_src
#+begin_src matlab :results value replace :exports results
ans = Rin
#+end_src
#+RESULTS:
: 101200.0
And we can verify that the bias current estimation stays the same:
#+begin_src matlab
in = mean(V0)/Rin;
#+end_src
#+begin_src matlab :results value replace :exports results
ans = in
#+end_src
#+RESULTS:
: 1.5305e-06
This validates the model of the ADC and the effectiveness of the added resistor.
** Sinus
#+begin_src matlab
load('force_sensor_sin.mat', 't', 'encoder', 'u', 'v');
u = u(t>25);
v = v(t>25);
encoder = encoder(t>25) - mean(encoder(t>25));
t = t(t>25);
#+end_src
The driving voltage is a sinus at 0.5Hz centered on 3V and with an amplitude of 3V (Figure [[fig:force_sensor_sin_u]]).
#+begin_src matlab :exports none
figure;
plot(t, u)
xlabel('Time [s]'); ylabel('Control Voltage [V]');
#+end_src
#+begin_src matlab :tangle no :exports results :results file replace
exportFig('figs/force_sensor_sin_u.pdf', 'width', 'normal', 'height', 'small');
#+end_src
#+name: fig:force_sensor_sin_u
#+caption: Driving Voltage
#+RESULTS:
[[file:figs/force_sensor_sin_u.png]]
The full stroke as measured by the encoder is:
#+begin_src matlab :results value replace
max(encoder)-min(encoder)
#+end_src
#+RESULTS:
: 5.005e-05
Its signal is shown in Figure [[fig:force_sensor_sin_encoder]].
#+begin_src matlab :exports none
figure;
plot(t, encoder)
xlabel('Time [s]'); ylabel('Encoder [m]');
#+end_src
#+begin_src matlab :tangle no :exports results :results file replace
exportFig('figs/force_sensor_sin_encoder.pdf', 'width', 'normal', 'height', 'small');
#+end_src
#+name: fig:force_sensor_sin_encoder
#+caption: Encoder measurement
#+RESULTS:
[[file:figs/force_sensor_sin_encoder.png]]
The generated voltage by the stack is shown in Figure
#+begin_src matlab :exports none
figure;
plot(t, v)
xlabel('Time [s]'); ylabel('Force Sensor Output [V]');
#+end_src
#+begin_src matlab :tangle no :exports results :results file replace
exportFig('figs/force_sensor_sin_stack.pdf', 'width', 'normal', 'height', 'small');
#+end_src
#+name: fig:force_sensor_sin_stack
#+caption: Voltage measured on the stack used as a sensor
#+RESULTS:
[[file:figs/force_sensor_sin_stack.png]]
The capacitance of the stack is
#+begin_src matlab
Cp = 4.4e-6; % [F]
#+end_src
The corresponding generated charge is then shown in Figure [[fig:force_sensor_sin_charge]].
#+begin_src matlab :exports none
figure;
plot(t, 1e6*Cp*(v-mean(v)))
xlabel('Time [s]'); ylabel('Generated Charge [$\mu C$]');
#+end_src
#+begin_src matlab :tangle no :exports results :results file replace
exportFig('figs/force_sensor_sin_charge.pdf', 'width', 'normal', 'height', 'small');
#+end_src
#+name: fig:force_sensor_sin_charge
#+caption: Generated Charge
#+RESULTS:
[[file:figs/force_sensor_sin_charge.png]]
The relation between the generated voltage and the measured displacement is almost linear as shown in Figure [[fig:force_sensor_linear_relation]].
#+begin_src matlab
b1 = encoder\(v-mean(v));
#+end_src
#+begin_src matlab :exports none
figure;
hold on;
plot(encoder, v-mean(v), 'DisplayName', 'Measured Voltage');
plot(encoder, encoder*b1, 'DisplayName', sprintf('Linear Fit: $U_s \\approx %.3f [V/\\mu m] \\cdot d$', 1e-6*abs(b1)));
hold off;
xlabel('Measured Displacement [m]'); ylabel('Generated Voltage [V]');
legend();
#+end_src
#+begin_src matlab :tangle no :exports results :results file replace
exportFig('figs/force_sensor_linear_relation.pdf', 'width', 'normal', 'height', 'small');
#+end_src
#+name: fig:force_sensor_linear_relation
#+caption: Almost linear relation between the relative displacement and the generated voltage
#+RESULTS:
[[file:figs/force_sensor_linear_relation.png]]
With a 16bits ADC, the resolution will then be equals to (in [nm]):
#+begin_src matlab :results value replace
abs((20/2^16)/(b1/1e9))
#+end_src
#+RESULTS:
: 3.9838

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%% Clear Workspace and Close figures
clear; close all; clc;
%% Intialize Laplace variable
s = zpk('s');
addpath('./mat/');
% Steps
load('force_sensor_steps.mat', 't', 'encoder', 'u', 'v');
figure;
tiledlayout(2, 1, 'TileSpacing', 'None', 'Padding', 'None');
nexttile;
plot(t, v);
xlabel('Time [s]'); ylabel('Measured voltage [V]');
nexttile;
plot(t, u);
xlabel('Time [s]'); ylabel('Actuator Voltage [V]');
% #+name: fig:force_sen_steps_time_domain
% #+caption: Time domain signal during the 3 actuator voltage steps
% #+RESULTS:
% [[file:figs/force_sen_steps_time_domain.png]]
% Three steps are performed at the following time intervals:
t_s = [ 2.5, 23;
23.8, 35;
35.8, 50];
% Fit function:
f = @(b,x) b(1).*exp(b(2).*x) + b(3);
% We are interested by the =b(2)= term, which is the time constant of the exponential.
tau = zeros(size(t_s, 1),1);
V0 = zeros(size(t_s, 1),1);
for t_i = 1:size(t_s, 1)
t_cur = t(t_s(t_i, 1) < t & t < t_s(t_i, 2));
t_cur = t_cur - t_cur(1);
y_cur = v(t_s(t_i, 1) < t & t < t_s(t_i, 2));
nrmrsd = @(b) norm(y_cur - f(b,t_cur)); % Residual Norm Cost Function
B0 = [0.5, -0.15, 2.2]; % Choose Appropriate Initial Estimates
[B,rnrm] = fminsearch(nrmrsd, B0); % Estimate Parameters B
tau(t_i) = 1/B(2);
V0(t_i) = B(3);
end
% #+RESULTS:
% | $tau$ [s] | $V_0$ [V] |
% |-----------+-----------|
% | 6.47 | 2.26 |
% | 6.76 | 2.26 |
% | 6.49 | 2.25 |
% With the capacitance being $C = 4.4 \mu F$, the internal impedance of the Speedgoat ADC can be computed as follows:
Cp = 4.4e-6; % [F]
Rin = abs(mean(tau))/Cp;
ans = Rin
% #+RESULTS:
% : 1494100.0
% The input impedance of the Speedgoat's ADC should then be close to $1.5\,M\Omega$ (specified at $1\,M\Omega$).
% #+begin_important
% How can we explain the voltage offset?
% #+end_important
% As shown in Figure [[fig:force_sensor_model_electronics]] (taken from cite:reza06_piezoel_trans_vibrat_contr_dampin), an input voltage offset is due to the input bias current $i_n$.
% #+name: fig:force_sensor_model_electronics
% #+caption: Model of a piezoelectric transducer (left) and instrumentation amplifier (right)
% [[file:figs/force_sensor_model_electronics.png]]
% The estimated input bias current is then:
in = mean(V0)/Rin;
ans = in
% #+RESULTS:
% : 1.5119e-06
% An additional resistor in parallel with $R_{in}$ would have two effects:
% - reduce the input voltage offset
% \[ V_{off} = \frac{R_a R_{in}}{R_a + R_{in}} i_n \]
% - increase the high pass corner frequency $f_c$
% \[ C_p \frac{R_{in}R_a}{R_{in} + R_a} = \tau_c = \frac{1}{f_c} \]
% \[ R_a = \frac{R_i}{f_c C_p R_i - 1} \]
% If we allow the high pass corner frequency to be equals to 3Hz:
fc = 3;
Ra = Rin/(fc*Cp*Rin - 1);
ans = Ra
% #+RESULTS:
% : 79804
% With this parallel resistance value, the voltage offset would be:
V_offset = Ra*Rin/(Ra + Rin) * in;
ans = V_offset
% Add Parallel Resistor
% A resistor $R_p \approx 100\,k\Omega$ is added in parallel with the force sensor as shown in Figure [[fig:force_sensor_model_electronics_without_R]].
% #+name: fig:force_sensor_model_electronics_without_R
% #+caption: Model of a piezoelectric transducer (left) and instrumentation amplifier (right) with added resistor $R_p$
% [[file:figs/force_sensor_model_electronics_without_R.png]]
load('force_sensor_steps_R_82k7.mat', 't', 'encoder', 'u', 'v');
figure;
tiledlayout(2, 1, 'TileSpacing', 'None', 'Padding', 'None');
nexttile;
plot(t, v);
xlabel('Time [s]'); ylabel('Measured voltage [V]');
nexttile;
plot(t, u);
xlabel('Time [s]'); ylabel('Actuator Voltage [V]');
% #+name: fig:force_sen_steps_time_domain_par_R
% #+caption: Time domain signal during the actuator voltage steps
% #+RESULTS:
% [[file:figs/force_sen_steps_time_domain_par_R.png]]
% Three steps are performed at the following time intervals:
t_s = [1.9, 6;
8.5, 13;
15.5, 21;
22.6, 26;
30.0, 36;
37.5, 41;
46.2, 49.5]
% Fit function:
f = @(b,x) b(1).*exp(b(2).*x) + b(3);
% We are interested by the =b(2)= term, which is the time constant of the exponential.
tau = zeros(size(t_s, 1),1);
V0 = zeros(size(t_s, 1),1);
for t_i = 1:size(t_s, 1)
t_cur = t(t_s(t_i, 1) < t & t < t_s(t_i, 2));
t_cur = t_cur - t_cur(1);
y_cur = v(t_s(t_i, 1) < t & t < t_s(t_i, 2));
nrmrsd = @(b) norm(y_cur - f(b,t_cur)); % Residual Norm Cost Function
B0 = [0.5, -0.2, 0.2]; % Choose Appropriate Initial Estimates
[B,rnrm] = fminsearch(nrmrsd, B0); % Estimate Parameters B
tau(t_i) = 1/B(2);
V0(t_i) = B(3);
end
% #+RESULTS:
% | $tau$ [s] | $V_0$ [V] |
% |-----------+-----------|
% | 0.43 | 0.15 |
% | 0.45 | 0.16 |
% | 0.43 | 0.15 |
% | 0.43 | 0.15 |
% | 0.45 | 0.15 |
% | 0.46 | 0.16 |
% | 0.48 | 0.16 |
% Knowing the capacitance value, we can estimate the value of the added resistor (neglecting the input impedance of $\approx 1\,M\Omega$):
Cp = 4.4e-6; % [F]
Rin = abs(mean(tau))/Cp;
ans = Rin
% #+RESULTS:
% : 101200.0
% And we can verify that the bias current estimation stays the same:
in = mean(V0)/Rin;
ans = in
% Sinus
load('force_sensor_sin.mat', 't', 'encoder', 'u', 'v');
u = u(t>25);
v = v(t>25);
encoder = encoder(t>25) - mean(encoder(t>25));
t = t(t>25);
% The driving voltage is a sinus at 0.5Hz centered on 3V and with an amplitude of 3V (Figure [[fig:force_sensor_sin_u]]).
figure;
plot(t, u)
xlabel('Time [s]'); ylabel('Control Voltage [V]');
% #+name: fig:force_sensor_sin_u
% #+caption: Driving Voltage
% #+RESULTS:
% [[file:figs/force_sensor_sin_u.png]]
% The full stroke as measured by the encoder is:
max(encoder)-min(encoder)
% #+RESULTS:
% : 5.005e-05
% Its signal is shown in Figure [[fig:force_sensor_sin_encoder]].
figure;
plot(t, encoder)
xlabel('Time [s]'); ylabel('Encoder [m]');
% #+name: fig:force_sensor_sin_encoder
% #+caption: Encoder measurement
% #+RESULTS:
% [[file:figs/force_sensor_sin_encoder.png]]
% The generated voltage by the stack is shown in Figure
figure;
plot(t, v)
xlabel('Time [s]'); ylabel('Force Sensor Output [V]');
% #+name: fig:force_sensor_sin_stack
% #+caption: Voltage measured on the stack used as a sensor
% #+RESULTS:
% [[file:figs/force_sensor_sin_stack.png]]
% The capacitance of the stack is
Cp = 4.4e-6; % [F]
% The corresponding generated charge is then shown in Figure [[fig:force_sensor_sin_charge]].
figure;
plot(t, 1e6*Cp*(v-mean(v)))
xlabel('Time [s]'); ylabel('Generated Charge [$\mu C$]');
% #+name: fig:force_sensor_sin_charge
% #+caption: Generated Charge
% #+RESULTS:
% [[file:figs/force_sensor_sin_charge.png]]
% The relation between the generated voltage and the measured displacement is almost linear as shown in Figure [[fig:force_sensor_linear_relation]].
b1 = encoder\(v-mean(v));
figure;
hold on;
plot(encoder, v-mean(v), 'DisplayName', 'Measured Voltage');
plot(encoder, encoder*b1, 'DisplayName', sprintf('Linear Fit: $U_s \\approx %.3f [V/\\mu m] \\cdot d$', 1e-6*abs(b1)));
hold off;
xlabel('Measured Displacement [m]'); ylabel('Generated Voltage [V]');
legend();
% #+name: fig:force_sensor_linear_relation
% #+caption: Almost linear relation between the relative displacement and the generated voltage
% #+RESULTS:
% [[file:figs/force_sensor_linear_relation.png]]
% With a 16bits ADC, the resolution will then be equals to (in [nm]):
abs((20/2^16)/(b1/1e9))

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%% Clear Workspace and Close figures
clear; close all; clc;
%% Intialize Laplace variable
s = zpk('s');
addpath('./mat/');
% Load Data
oc = load('identification_open_circuit.mat', 't', 'encoder', 'u');
sc = load('identification_short_circuit.mat', 't', 'encoder', 'u');
% Transfer Functions
Ts = 1e-4; % Sampling Time [s]
win = hann(ceil(10/Ts));
[tf_oc_est, f] = tfestimate(oc.u, oc.encoder, win, [], [], 1/Ts);
[co_oc_est, ~] = mscohere( oc.u, oc.encoder, win, [], [], 1/Ts);
[tf_sc_est, ~] = tfestimate(sc.u, sc.encoder, win, [], [], 1/Ts);
[co_sc_est, ~] = mscohere( sc.u, sc.encoder, win, [], [], 1/Ts);
figure;
hold on;
plot(f, co_oc_est, '-')
plot(f, co_sc_est, '-')
set(gca, 'Xscale', 'log'); set(gca, 'Yscale', 'lin');
ylabel('Coherence'); xlabel('Frequency [Hz]');
hold off;
xlim([0.5, 5e3]);
% #+name: fig:stiffness_force_sensor_coherence
% #+caption:
% #+RESULTS:
% [[file:figs/stiffness_force_sensor_coherence.png]]
figure;
tiledlayout(2, 1, 'TileSpacing', 'None', 'Padding', 'None');
ax1 = nexttile;
hold on;
plot(f, abs(tf_oc_est), '-', 'DisplayName', 'Open-Circuit')
plot(f, abs(tf_sc_est), '-', 'DisplayName', 'Short-Circuit')
set(gca, 'Xscale', 'log'); set(gca, 'Yscale', 'log');
ylabel('Amplitude'); set(gca, 'XTickLabel',[]);
hold off;
ylim([1e-7, 3e-4]);
legend('location', 'southwest');
ax2 = nexttile;
hold on;
plot(f, 180/pi*angle(tf_oc_est), '-')
plot(f, 180/pi*angle(tf_sc_est), '-')
set(gca, 'Xscale', 'log'); set(gca, 'Yscale', 'lin');
ylabel('Phase'); xlabel('Frequency [Hz]');
hold off;
yticks(-360:90:360);
axis padded 'auto x'
linkaxes([ax1,ax2], 'x');
xlim([0.5, 5e3]);

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matlab/runtest.m Normal file
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tg = slrt;
%%
f = SimulinkRealTime.openFTP(tg);
mget(f, 'int_enc.dat', 'data');
close(f);
%% Convert the Data
data = SimulinkRealTime.utils.getFileScopeData('data/int_enc.dat').data;
interferometer = data(:, 1);
encoder = data(:, 2);
u = data(:, 3);
t = data(:, 4);
save('./mat/int_enc_id_noise_bis.mat', 'interferometer', 'encoder', 'u' , 't');

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matlab/setup.m Normal file
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%%
s = tf('s');
Ts = 1e-4; % [s]
%% Pre-Filter
G_pf = 1/(1 + s/2/pi/20);
G_pf = c2d(G_pf, Ts, 'tustin');
% %% Force Sensor Filter (HPF)
% Gf_hpf = s/(s + 2*pi*2);
% Gf_hpf = tf(1);
% Gf_hpf = c2d(Gf_hpf, Ts, 'tustin');
%
% %% IFF Controller
% Kiff = 1/(s + 2*pi*2);
% Kiff = c2d(Kiff, Ts, 'tustin');
%
% %% Excitation Signal
Tsim = 100; % Excitation time + Measurement time [s]
t = 0:Ts:Tsim;
u_exc = timeseries(chirp(t, 10, Tsim, 40, 'logarithmic'), t);
% u_exc = timeseries(y_v, t);

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