build based on 87374ad

Author: Documenter.jl

previews/PR69/.documenter-siteinfo.json

@@ -1 +1 @@
-{"documenter":{"julia_version":"1.9.4","generation_timestamp":"2024-05-07T22:21:15","documenter_version":"1.4.1"}}
+{"documenter":{"julia_version":"1.9.4","generation_timestamp":"2024-05-08T00:26:17","documenter_version":"1.4.1"}}

previews/PR69/func_index/index.html

@@ -129,4 +129,4 @@
 u, y, d = model.uop, model(), mpc_d.estim.model.dop
 initstate!(mpc_d, u, y, d)
 u_data, y_data, ry_data = test_mpc_d(mpc_d, model)
-plot_data(t_data, u_data, y_data, ry_data)</code></pre><p><img src="../plot4_LinMPC.svg" alt="plot4_LinMPC"/></p><p>Note that measured disturbances are assumed constant in the future by default but custom <span>$\mathbf{D̂}$</span> predictions are possible. The same applies for the setpoint predictions <span>$\mathbf{R̂_y}$</span>.</p><section class="footnotes is-size-7"><ul><li class="footnote" id="footnote-1"><a class="tag is-link" href="#citeref-1">1</a>As an alternative to state observer, we could have use an <a href="../../public/state_estim/#InternalModel"><code>InternalModel</code></a> structure with <code>mpc = LinMPC(InternalModel(model), Hp=15, Hc=2, Mwt=[1, 1], Nwt=[0.1, 0.1])</code>. It was tested on the example of this page and it gave similar results.</li></ul></section></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../installation/">« Installation</a><a class="docs-footer-nextpage" href="../nonlinmpc/">Nonlinear Design »</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></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.4.1 on <span class="colophon-date" title="Tuesday 7 May 2024 22:21">Tuesday 7 May 2024</span>. Using Julia version 1.9.4.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+plot_data(t_data, u_data, y_data, ry_data)</code></pre><p><img src="../plot4_LinMPC.svg" alt="plot4_LinMPC"/></p><p>Note that measured disturbances are assumed constant in the future by default but custom <span>$\mathbf{D̂}$</span> predictions are possible. The same applies for the setpoint predictions <span>$\mathbf{R̂_y}$</span>.</p><section class="footnotes is-size-7"><ul><li class="footnote" id="footnote-1"><a class="tag is-link" href="#citeref-1">1</a>As an alternative to state observer, we could have use an <a href="../../public/state_estim/#InternalModel"><code>InternalModel</code></a> structure with <code>mpc = LinMPC(InternalModel(model), Hp=15, Hc=2, Mwt=[1, 1], Nwt=[0.1, 0.1])</code>. It was tested on the example of this page and it gave similar results.</li></ul></section></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../installation/">« Installation</a><a class="docs-footer-nextpage" href="../nonlinmpc/">Nonlinear Design »</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></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.4.1 on <span class="colophon-date" title="Wednesday 8 May 2024 00:26">Wednesday 8 May 2024</span>. Using Julia version 1.9.4.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>

previews/PR69/index.html

@@ -143,4 +143,4 @@
     return res
 end</code></pre><p>The <a href="../../public/generic_func/#ModelPredictiveControl.setmodel!"><code>setmodel!</code></a> method must be called after solving the optimization problem with <a href="../../public/predictive_control/#ModelPredictiveControl.moveinput!"><code>moveinput!</code></a>, and before updating the state estimate with <a href="../../public/generic_func/#ModelPredictiveControl.updatestate!"><code>updatestate!</code></a>. The <a href="../../public/plot_sim/#ModelPredictiveControl.SimResult"><code>SimResult</code></a> object is for plotting purposes only. The adaptive <a href="../../public/predictive_control/#LinMPC"><code>LinMPC</code></a> performances are similar to the nonlinear MPC, both for the 180° setpoint:</p><pre><code class="language-julia hljs">res_slin = test_slmpc(model, mpc3, [180], plant, x_0=[0, 0])
 plot(res_slin)</code></pre><p><img src="../plot10_NonLinMPC.svg" alt="plot10_NonLinMPC"/></p><p>and the 10° step disturbance:</p><pre><code class="language-julia hljs">res_slin = test_slmpc(model, mpc3, [180], plant, x_0=[π, 0], y_step=[10])
-plot(res_slin)</code></pre><p><img src="../plot11_NonLinMPC.svg" alt="plot11_NonLinMPC"/></p><p>The computations of the successive linearization MPC are about 125 times faster than the nonlinear MPC (0.00012 s per time steps versus 0.015 s per time steps, on average), an impressive gain for similar closed-loop performances!</p><section class="footnotes is-size-7"><ul><li class="footnote" id="footnote-1"><a class="tag is-link" href="#citeref-1">1</a>Arnström, D., Bemporad, A., and Axehill, D. (2022). A dual active-set solver for embedded quadratic programming using recursive LDLᵀ updates. IEEE Trans. Autom. Contr., 67(8). <a href="https://doi.org/doi:10.1109/TAC.2022.3176430">https://doi.org/doi:10.1109/TAC.2022.3176430</a>.</li></ul></section></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../linmpc/">« Linear Design</a><a class="docs-footer-nextpage" href="../../public/sim_model/">Plant Models »</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></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.4.1 on <span class="colophon-date" title="Tuesday 7 May 2024 22:21">Tuesday 7 May 2024</span>. Using Julia version 1.9.4.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+plot(res_slin)</code></pre><p><img src="../plot11_NonLinMPC.svg" alt="plot11_NonLinMPC"/></p><p>The computations of the successive linearization MPC are about 125 times faster than the nonlinear MPC (0.00012 s per time steps versus 0.015 s per time steps, on average), an impressive gain for similar closed-loop performances!</p><section class="footnotes is-size-7"><ul><li class="footnote" id="footnote-1"><a class="tag is-link" href="#citeref-1">1</a>Arnström, D., Bemporad, A., and Axehill, D. (2022). A dual active-set solver for embedded quadratic programming using recursive LDLᵀ updates. IEEE Trans. Autom. Contr., 67(8). <a href="https://doi.org/doi:10.1109/TAC.2022.3176430">https://doi.org/doi:10.1109/TAC.2022.3176430</a>.</li></ul></section></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../linmpc/">« Linear Design</a><a class="docs-footer-nextpage" href="../../public/sim_model/">Plant Models »</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></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.4.1 on <span class="colophon-date" title="Wednesday 8 May 2024 00:26">Wednesday 8 May 2024</span>. Using Julia version 1.9.4.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>