build based on 3205102

dev/.documenter-siteinfo.json

@@ -1 +1 @@
-{"documenter":{"julia_version":"1.10.5","generation_timestamp":"2024-09-07T10:44:41","documenter_version":"1.7.0"}}
+{"documenter":{"julia_version":"1.10.5","generation_timestamp":"2024-09-14T09:13:54","documenter_version":"1.7.0"}}

dev/examples/dc_motor_pi/index.html

@@ -66,7 +66,7 @@
  label = "Measurement", title = "DC Motor with Speed Controller")
 Plots.plot!(sol.t, sol[ref.output.u], label = "Reference")
 p2 = Plots.plot(sol.t, sol[load.tau.u], ylabel = "Disturbance in Nm", label = "")
-Plots.plot(p1, p2, layout = (2, 1))</code></pre><img src="ff0c4871.svg" alt="Example block output"/><h2 id="Discrete-time-controller"><a class="docs-heading-anchor" href="#Discrete-time-controller">Discrete-time controller</a><a id="Discrete-time-controller-1"></a><a class="docs-heading-anchor-permalink" href="#Discrete-time-controller" title="Permalink"></a></h2><p>Until now, we have modeled both the physical part of the system, the DC motor, and the control systems, in continuous time. In practice, it is common to implement control systems on a computer operating at a fixed sample rate, i.e, in discrete time. A system containing both continuous-time parts and discrete-time parts is often referred to as a &quot;sampled-data system&quot;, and the ModelingToolkit standard library together with ModelingToolkitSampledData.jl contain several components to model such systems.</p><p>Below, we re-model the system, this time with a discrete-time controller: <a href="../../blocks/#ModelingToolkitSampledData.DiscretePIDStandard"><code>DiscretePIDStandard</code></a>. To interface between the continuous and discrete parts of the model, we make use of a <a href="../../blocks/#ModelingToolkitSampledData.Sampler"><code>Sampler</code></a> and <a href="../../blocks/#ModelingToolkitSampledData.ZeroOrderHold"><code>ZeroOrderHold</code></a> blocks. Apart from the three aforementioned components, the model is the same as before.</p><pre><code class="language-julia hljs">using ModelingToolkitSampledData
+Plots.plot(p1, p2, layout = (2, 1))</code></pre><img src="79716301.svg" alt="Example block output"/><h2 id="Discrete-time-controller"><a class="docs-heading-anchor" href="#Discrete-time-controller">Discrete-time controller</a><a id="Discrete-time-controller-1"></a><a class="docs-heading-anchor-permalink" href="#Discrete-time-controller" title="Permalink"></a></h2><p>Until now, we have modeled both the physical part of the system, the DC motor, and the control systems, in continuous time. In practice, it is common to implement control systems on a computer operating at a fixed sample rate, i.e, in discrete time. A system containing both continuous-time parts and discrete-time parts is often referred to as a &quot;sampled-data system&quot;, and the ModelingToolkit standard library together with ModelingToolkitSampledData.jl contain several components to model such systems.</p><p>Below, we re-model the system, this time with a discrete-time controller: <a href="../../blocks/#ModelingToolkitSampledData.DiscretePIDStandard"><code>DiscretePIDStandard</code></a>. To interface between the continuous and discrete parts of the model, we make use of a <a href="../../blocks/#ModelingToolkitSampledData.Sampler"><code>Sampler</code></a> and <a href="../../blocks/#ModelingToolkitSampledData.ZeroOrderHold"><code>ZeroOrderHold</code></a> blocks. Apart from the three aforementioned components, the model is the same as before.</p><pre><code class="language-julia hljs">using ModelingToolkitSampledData
 using JuliaSimCompiler
 
 z = ShiftIndex()
@@ -125,7 +125,7 @@
  label = &quot;Measurement (cont. controller)&quot;, title = &quot;DC Motor with Speed Controller&quot;)
 Plots.plot!(disc_sol.t, disc_sol[inertia.w], ylabel = &quot;Angular Vel. in rad/s&quot;,
  label = &quot;Measurement (disc. controller)&quot;, title = &quot;DC Motor with Discrete-time Speed Controller&quot;, legend=:bottomleft, dpi=600)
-lens!([1.2, 1.6], [0.75, 1.02], inset=(1, bbox(.6, .5, .3, .4))) # 1 is subplot index</code></pre><img src="c1fc6a4d.svg" alt="Example block output"/><p>In the plot above, we compare the result of the discrete-time control system to the continuous-time result from before. We see that with the chosen sample-interval of <code>dt=0.005</code> (provided to the <code>Sampler</code> block), we have a slight degradation in the control performance as a consequence of the discretization.</p><h3 id="Adding-a-slower-outer-position-loop"><a class="docs-heading-anchor" href="#Adding-a-slower-outer-position-loop">Adding a slower outer position loop</a><a id="Adding-a-slower-outer-position-loop-1"></a><a class="docs-heading-anchor-permalink" href="#Adding-a-slower-outer-position-loop" title="Permalink"></a></h3><p>Below, we add an outer control loop that controls the position of the motor by manipulating the reference to the inner speed controller. This is a common control strategy, often referred to as <em>cascade control</em>.</p><p><img src="https://help.juliahub.com/juliasimcontrol/dev/figs/cascade_pid.png" alt="block diagram of a cascade control loop"/></p><pre><code class="language-julia hljs">@mtkmodel Cascade begin
+lens!([1.2, 1.6], [0.75, 1.02], inset=(1, bbox(.6, .5, .3, .4))) # 1 is subplot index</code></pre><img src="a46e4bbc.svg" alt="Example block output"/><p>In the plot above, we compare the result of the discrete-time control system to the continuous-time result from before. We see that with the chosen sample-interval of <code>dt=0.005</code> (provided to the <code>Sampler</code> block), we have a slight degradation in the control performance as a consequence of the discretization.</p><h3 id="Adding-a-slower-outer-position-loop"><a class="docs-heading-anchor" href="#Adding-a-slower-outer-position-loop">Adding a slower outer position loop</a><a id="Adding-a-slower-outer-position-loop-1"></a><a class="docs-heading-anchor-permalink" href="#Adding-a-slower-outer-position-loop" title="Permalink"></a></h3><p>Below, we add an outer control loop that controls the position of the motor by manipulating the reference to the inner speed controller. This is a common control strategy, often referred to as <em>cascade control</em>.</p><p><img src="https://help.juliahub.com/juliasimcontrol/dev/figs/cascade_pid.png" alt="block diagram of a cascade control loop"/></p><pre><code class="language-julia hljs">@mtkmodel Cascade begin
  @components begin
  inner = DiscreteClosedLoop(use_ref = false)
  sampler = Sampler(clock = Clock(0.005))
@@ -170,4 +170,4 @@
  cascade.cc.u(z-1) =&gt; 0.0;
  ], (0, 2))
 cascade_sol = solve(cascade_prob, Tsit5())
-Plots.plot(cascade_sol, idxs = [i.inertia.phi, i.inertia.w, i.zoh.u])</code></pre><img src="c2d4a030.svg" alt="Example block output"/></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../../tutorials/noise/">« Noise and measurement corruption</a><a class="docs-footer-nextpage" href="../onoffcontroller/">On-off controller »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Saturday 7 September 2024 10:44">Saturday 7 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+Plots.plot(cascade_sol, idxs = [i.inertia.phi, i.inertia.w, i.zoh.u])</code></pre><img src="9fd477a3.svg" alt="Example block output"/></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../../tutorials/noise/">« Noise and measurement corruption</a><a class="docs-footer-nextpage" href="../onoffcontroller/">On-off controller »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Saturday 14 September 2024 09:13">Saturday 14 September 2024</span>. Using Julia version 1.10.5.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>