From 35ada2f33ee7230dca82f969f318436f94d093fb Mon Sep 17 00:00:00 2001 From: Thomas Dehaeze Date: Thu, 9 Apr 2020 17:36:07 +0200 Subject: [PATCH] Removed unused figures/sections/simulinks --- figs/G_sigma.svg | Bin 415341 -> 0 bytes figs/G_ws_pz.svg | Bin 354571 -> 0 bytes figs/G_ws_vc.svg | Bin 335362 -> 0 bytes figs/Gc_ws_pz.svg | Bin 284576 -> 0 bytes figs/Gc_ws_vc.svg | Bin 325044 -> 0 bytes figs/Gtvc_loop_gain.svg | Bin 218443 -> 0 bytes figs/Guu_ws.svg | Bin 432414 -> 0 bytes figs/Guu_ws_pz.svg | Bin 392099 -> 0 bytes figs/Guv_ws.svg | Bin 420983 -> 0 bytes figs/Gvc_loop_gain.svg | Bin 233326 -> 0 bytes figs/Gvc_speed.svg | Bin 394988 -> 0 bytes figs/coupling_heavy.svg | Bin 166832 -> 0 bytes figs/coupling_light.svg | Bin 217116 -> 0 bytes figs/coupling_ratio_light_heavy.svg | Bin 329308 -> 0 bytes figs/coupling_simscape_heavy.svg | Bin 221149 -> 0 bytes figs/coupling_simscape_light.svg | Bin 250502 -> 0 bytes figs/coupling_small_m.svg | Bin 224353 -> 0 bytes figs/perfconp.svg | 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figure [[fig:control_measure_rotating_2 The loop gain is $L = G K$. * Multi Body Model - Simscape - :PROPERTIES: - :HEADER-ARGS:matlab+: :tangle matlab/simscape_analysis.m - :END: - <> +:PROPERTIES: +:HEADER-ARGS:matlab+: :tangle matlab/simscape_analysis.m +:END: +<> ** Matlab Init :noexport:ignore: #+begin_src matlab :tangle no :exports none :noweb yes :var current_dir=(file-name-directory buffer-file-name) @@ -1998,47 +1998,3 @@ First, we create the closed loop systems. Then, we plot the transfer function fr The close-loop performance does not vary a lot with the rotating speed (figure [[fig:perfcomp]]) even tough the open loop system is varying quite a lot (figure [[fig:Guu_uv_ws]]). #+end_important -** BKMK Plant Control - MIMO approach -*** Analysis - SVD -The singular value decomposition of a MIMO system $G$ is defined as follow: -\[ G = U \Sigma V^H \] - -With: -- $\Sigma$ is an $2 \times 2$ matrix with 2 non-negative *singular values* $\sigma_i$, arranged in descending order along its main diagonal -- $U$ is an $2 \times 2$ unitary matrix. The columns vectors of $U$, denoted $u_i$, represent the *output directions* of the plant. They are orthonomal -- $V$ is an $2 \times 2$ unitary matrix. The columns vectors of $V$, denoted $v_i$, represent the *input directions* of the plant. They are orthonomal - -We first look at the evolution of the singular values as a function of frequency (figure [[fig:G_sigma]]). - -#+begin_src matlab :exports none - freqs = logspace(-2, 1, 1000); - - figure; - hold on; - for i = 1:length(ws) - sv = sigma(Gs_vc{i}, 2*pi*freqs); - set(gca,'ColorOrderIndex',i) - plot(freqs, sv(1, :), 'DisplayName', sprintf('w = %.0f rpm', ws(i)*60/2/pi)); - set(gca,'ColorOrderIndex',i) - plot(freqs, sv(2, :), '--', 'HandleVisibility', 'off'); - end - hold off; - set(gca,'xscale','log'); set(gca,'yscale','log'); - legend('location', 'southwest'); -#+end_src - -#+HEADER: :tangle no :exports results :results none :noweb yes -#+begin_src matlab :var filepath="figs/G_sigma.pdf" :var figsize="full-tall" :post pdf2svg(file=*this*, ext="png") - <> -#+end_src - -#+LABEL: fig:G_sigma -#+CAPTION: Evolution of the singular values with frequency -[[file:figs/G_sigma.png]] - -* Control Implementation - <> - -* Bibliography :ignore: -# bibliographystyle:unsrt -# 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