From c2607b821850e0dc909b4a4f923eabb6d9aa5b30 Mon Sep 17 00:00:00 2001 From: Thomas Dehaeze Date: Tue, 10 Sep 2019 18:16:16 +0200 Subject: [PATCH] Add CSS/JS. Add plant identification scripts --- css/htmlize.css | 145 ++++ css/readtheorg.css | 1095 +++++++++++++++++++++++++++ figs/frequency_response_matrix.png | Bin 0 -> 184757 bytes figs/identification_coherence.png | Bin 0 -> 119076 bytes figs/identification_matrix_fit.png | Bin 0 -> 190657 bytes figs/identification_ux.png | Bin 0 -> 56274 bytes figs/identification_uy.png | Bin 0 -> 59550 bytes figs/weights.png | Bin 0 -> 41738 bytes index.html | 588 ++++++++++++++ index.org | 403 +++++++++- js/bootstrap.min.js | 7 + js/jquery.min.js | 4 + js/jquery.stickytableheaders.min.js | 1 + js/readtheorg.js | 85 +++ mat/plant.mat | Bin 0 -> 1417 bytes 15 files changed, 2302 insertions(+), 26 deletions(-) create mode 100644 css/htmlize.css create mode 100644 css/readtheorg.css create mode 100644 figs/frequency_response_matrix.png create mode 100644 figs/identification_coherence.png create mode 100644 figs/identification_matrix_fit.png create mode 100644 figs/identification_ux.png create mode 100644 figs/identification_uy.png create mode 100644 figs/weights.png create mode 100644 index.html create mode 100644 js/bootstrap.min.js create mode 100644 js/jquery.min.js create mode 100644 js/jquery.stickytableheaders.min.js create mode 100644 js/readtheorg.js create mode 100644 mat/plant.mat diff --git a/css/htmlize.css b/css/htmlize.css new file mode 100644 index 0000000..0b32a03 --- /dev/null +++ b/css/htmlize.css @@ -0,0 +1,145 @@ +.org-bold { /* bold */ font-weight: bold; } +.org-bold-italic { /* bold-italic */ font-weight: bold; font-style: italic; } +.org-buffer-menu-buffer { /* buffer-menu-buffer */ font-weight: bold; } +.org-builtin { /* font-lock-builtin-face */ color: #7a378b; } +.org-button { /* button */ text-decoration: underline; } +.org-calendar-today { /* calendar-today */ text-decoration: underline; } +.org-change-log-acknowledgement { /* change-log-acknowledgement */ color: #b22222; } +.org-change-log-conditionals { /* change-log-conditionals */ color: #a0522d; } +.org-change-log-date { /* change-log-date */ color: #8b2252; } +.org-change-log-email { /* change-log-email */ color: #a0522d; } +.org-change-log-file { /* change-log-file */ color: #0000ff; } +.org-change-log-function { /* change-log-function */ color: #a0522d; } +.org-change-log-list { /* change-log-list */ color: #a020f0; } +.org-change-log-name { /* change-log-name */ color: #008b8b; } +.org-comint-highlight-input { /* comint-highlight-input */ font-weight: bold; } +.org-comint-highlight-prompt { /* comint-highlight-prompt */ color: #00008b; } +.org-comment { /* font-lock-comment-face */ color: #999988; font-style: italic; } +.org-comment-delimiter { /* font-lock-comment-delimiter-face */ color: #999988; font-style: italic; } +.org-completions-annotations { /* completions-annotations */ font-style: italic; } +.org-completions-common-part { /* completions-common-part */ color: #000000; background-color: #ffffff; } +.org-completions-first-difference { /* completions-first-difference */ font-weight: bold; } +.org-constant { /* font-lock-constant-face */ color: #008b8b; } +.org-diary { /* diary */ color: #ff0000; } +.org-diff-context { /* diff-context */ color: #7f7f7f; } +.org-diff-file-header { /* diff-file-header */ background-color: #b3b3b3; font-weight: bold; } +.org-diff-function { /* diff-function */ background-color: #cccccc; } +.org-diff-header { /* diff-header */ background-color: #cccccc; } +.org-diff-hunk-header { /* diff-hunk-header */ background-color: #cccccc; } +.org-diff-index { /* diff-index */ background-color: #b3b3b3; font-weight: bold; } +.org-diff-nonexistent { /* diff-nonexistent */ background-color: #b3b3b3; font-weight: bold; } +.org-diff-refine-change { /* diff-refine-change */ background-color: #d9d9d9; } +.org-dired-directory { /* dired-directory */ color: #0000ff; } +.org-dired-flagged { /* dired-flagged */ color: #ff0000; font-weight: bold; } +.org-dired-header { /* dired-header */ color: #228b22; } +.org-dired-ignored { /* dired-ignored */ color: #7f7f7f; } +.org-dired-mark { /* dired-mark */ color: #008b8b; } +.org-dired-marked { /* dired-marked */ color: #ff0000; font-weight: bold; } +.org-dired-perm-write { /* dired-perm-write */ color: #b22222; } +.org-dired-symlink { /* dired-symlink */ color: #a020f0; } +.org-dired-warning { /* dired-warning */ color: #ff0000; font-weight: bold; } +.org-doc { /* font-lock-doc-face */ color: #8b2252; } +.org-escape-glyph { /* escape-glyph */ color: #a52a2a; } +.org-file-name-shadow { /* file-name-shadow */ color: #7f7f7f; } +.org-flyspell-duplicate { /* flyspell-duplicate */ color: #cdad00; font-weight: bold; text-decoration: underline; } +.org-flyspell-incorrect { /* flyspell-incorrect */ color: #ff4500; font-weight: bold; text-decoration: underline; } +.org-fringe { /* fringe */ background-color: #f2f2f2; } +.org-function-name { /* font-lock-function-name-face */ color: teal; } +.org-header-line { /* header-line */ color: #333333; background-color: #e5e5e5; } +.org-help-argument-name { /* help-argument-name */ font-style: italic; } +.org-highlight { /* highlight */ background-color: #b4eeb4; } +.org-holiday { /* holiday */ background-color: #ffc0cb; } +.org-isearch { /* isearch */ color: #b0e2ff; background-color: #cd00cd; } +.org-isearch-fail { /* isearch-fail */ background-color: #ffc1c1; } +.org-italic { /* italic */ font-style: italic; } +.org-keyword { /* font-lock-keyword-face */ color: #0086b3; } +.org-lazy-highlight { /* lazy-highlight */ background-color: #afeeee; } +.org-link { /* link */ color: #0000ff; text-decoration: underline; } +.org-link-visited { /* link-visited */ color: #8b008b; text-decoration: underline; } +.org-log-edit-header { /* log-edit-header */ color: #a020f0; } +.org-log-edit-summary { /* log-edit-summary */ color: #0000ff; } +.org-log-edit-unknown-header { /* log-edit-unknown-header */ color: #b22222; } +.org-match { /* match */ background-color: #ffff00; } +.org-next-error { /* next-error */ background-color: #eedc82; } +.org-nobreak-space { /* nobreak-space */ color: #a52a2a; text-decoration: underline; } +.org-org-archived { /* org-archived */ color: #7f7f7f; } +.org-org-block { /* org-block */ color: #7f7f7f; } +.org-org-block-begin-line { /* org-block-begin-line */ color: #b22222; } +.org-org-block-end-line { /* org-block-end-line */ color: #b22222; } +.org-org-checkbox { /* org-checkbox */ font-weight: bold; } +.org-org-checkbox-statistics-done { /* org-checkbox-statistics-done */ color: #228b22; font-weight: bold; } +.org-org-checkbox-statistics-todo { /* org-checkbox-statistics-todo */ color: #ff0000; font-weight: bold; } +.org-org-clock-overlay { /* org-clock-overlay */ background-color: #ffff00; } +.org-org-code { /* org-code */ color: #7f7f7f; } +.org-org-column { /* org-column */ background-color: #e5e5e5; } +.org-org-column-title { /* org-column-title */ background-color: #e5e5e5; font-weight: bold; text-decoration: underline; } +.org-org-date { /* org-date */ color: #a020f0; text-decoration: underline; } +.org-org-document-info { /* org-document-info */ color: #191970; } +.org-org-document-info-keyword { /* org-document-info-keyword */ color: #7f7f7f; } +.org-org-document-title { /* org-document-title */ color: #191970; font-size: 144%; font-weight: bold; } +.org-org-done { /* org-done */ color: #228b22; font-weight: bold; } +.org-org-drawer { /* org-drawer */ color: #0000ff; } +.org-org-ellipsis { /* org-ellipsis */ color: #b8860b; text-decoration: underline; } +.org-org-footnote { /* org-footnote */ color: #a020f0; text-decoration: underline; } +.org-org-formula { /* org-formula */ color: #b22222; } +.org-org-headline-done { /* org-headline-done */ color: #bc8f8f; } +.org-org-hide { /* org-hide */ color: #ffffff; } +.org-org-latex-and-export-specials { /* org-latex-and-export-specials */ color: #8b4513; } +.org-org-level-1 { /* org-level-1 */ color: #0000ff; } +.org-org-level-2 { /* org-level-2 */ color: #a0522d; } +.org-org-level-3 { /* org-level-3 */ color: #a020f0; } +.org-org-level-4 { /* org-level-4 */ color: #b22222; } +.org-org-level-5 { /* org-level-5 */ color: #228b22; } +.org-org-level-6 { /* org-level-6 */ color: #008b8b; } +.org-org-level-7 { /* org-level-7 */ color: #7a378b; } +.org-org-level-8 { /* org-level-8 */ color: #8b2252; } +.org-org-link { /* org-link */ color: #0000ff; text-decoration: underline; } +.org-org-meta-line { /* org-meta-line */ color: #b22222; } +.org-org-mode-line-clock { /* org-mode-line-clock */ color: #000000; background-color: #bfbfbf; } +.org-org-mode-line-clock-overrun { /* org-mode-line-clock-overrun */ color: #000000; background-color: #ff0000; } +.org-org-quote { /* org-quote */ color: #7f7f7f; } +.org-org-scheduled { /* org-scheduled */ color: #006400; } +.org-org-scheduled-previously { /* org-scheduled-previously */ color: #b22222; } +.org-org-scheduled-today { /* org-scheduled-today */ color: #006400; } +.org-org-sexp-date { /* org-sexp-date */ color: #a020f0; } +.org-org-special-keyword { /* org-special-keyword */ color: #a020f0; } +.org-org-table { /* org-table */ color: #0000ff; } +.org-org-tag { /* org-tag */ font-weight: bold; } +.org-org-target { /* org-target */ text-decoration: underline; } +.org-org-time-grid { /* org-time-grid */ color: #b8860b; } +.org-org-todo { /* org-todo */ color: #ff0000; font-weight: bold; } +.org-org-upcoming-deadline { /* org-upcoming-deadline */ color: #b22222; } +.org-org-verbatim { /* org-verbatim */ color: #7f7f7f; } +.org-org-verse { /* org-verse */ color: #7f7f7f; } +.org-org-warning { /* org-warning */ color: #ff0000; font-weight: bold; } +.org-outline-1 { /* outline-1 */ color: #0000ff; } +.org-outline-2 { /* outline-2 */ color: #a0522d; } +.org-outline-3 { /* outline-3 */ color: #a020f0; } +.org-outline-4 { /* outline-4 */ color: #b22222; } +.org-outline-5 { /* outline-5 */ color: #228b22; } +.org-outline-6 { /* outline-6 */ color: #008b8b; } +.org-outline-7 { /* outline-7 */ color: #7a378b; } +.org-outline-8 { /* outline-8 */ color: #8b2252; } +.org-preprocessor { /* font-lock-preprocessor-face */ color: #7a378b; } +.org-query-replace { /* query-replace */ color: #b0e2ff; background-color: #cd00cd; } +.org-regexp-grouping-backslash { /* font-lock-regexp-grouping-backslash */ font-weight: bold; } +.org-regexp-grouping-construct { /* font-lock-regexp-grouping-construct */ font-weight: bold; } +.org-region { /* region */ background-color: #eedc82; } +.org-secondary-selection { /* secondary-selection */ background-color: #ffff00; } +.org-shadow { /* shadow */ color: #7f7f7f; } +.org-show-paren-match { /* show-paren-match */ background-color: #40e0d0; } +.org-show-paren-mismatch { /* show-paren-mismatch */ color: #ffffff; background-color: #a020f0; } +.org-string { /* font-lock-string-face */ color: #dd1144; } +.org-tool-bar { /* tool-bar */ color: #000000; background-color: #bfbfbf; } +.org-tooltip { /* tooltip */ color: #000000; background-color: #ffffe0; } +.org-trailing-whitespace { /* trailing-whitespace */ background-color: #ff0000; } +.org-type { /* font-lock-type-face */ color: #228b22; } +.org-underline { /* underline */ text-decoration: underline; } +.org-variable-name { /* font-lock-variable-name-face */ color: teal; } +.org-warning { /* font-lock-warning-face */ color: #ff0000; font-weight: bold; } +.org-widget-button { /* widget-button */ font-weight: bold; } +.org-widget-button-pressed { /* widget-button-pressed */ color: #ff0000; } +.org-widget-documentation { /* widget-documentation */ color: #006400; } +.org-widget-field { /* widget-field */ background-color: #d9d9d9; } +.org-widget-inactive { /* widget-inactive */ color: #7f7f7f; } +.org-widget-single-line-field { /* widget-single-line-field */ background-color: #d9d9d9; } diff --git a/css/readtheorg.css b/css/readtheorg.css new file mode 100644 index 0000000..1690bd0 --- /dev/null +++ b/css/readtheorg.css @@ -0,0 +1,1095 @@ +@import url(https://fonts.googleapis.com/css?family=Lato:400,700,400italic,700italic|Roboto+Slab:400,700|Inconsolata:400,700); +@import url(https://maxcdn.bootstrapcdn.com/font-awesome/4.3.0/css/font-awesome.min.css); + +h1,h2,h3,h4,h5,h6,legend{ + font-family:"Roboto Slab","ff-tisa-web-pro","Georgia",Arial,sans-serif; + font-weight:700; + margin-top:0; +} + +h1{ + font-size:175%; +} + +.subtitle{ + font-size:95%; /* of h1 */ +} + +h2{ + font-size:150%; +} + +h3{ + font-size:125%; +} + +h4{ + font-size:115%; +} + +h5{ + font-size:110%; +} + +h6{ + font-size:100%; +} + +h4,h5,h6{ + color:#2980B9; + font-weight:300; +} + +html{ + -ms-text-size-adjust:100%; + -webkit-text-size-adjust:100%; + font-size:100%; + height:100%; + overflow-x:hidden; +} + +body{ + background:#edf0f2; + color:#404040; + font-family:"Lato","proxima-nova","Helvetica Neue",Arial,sans-serif; + font-weight:normal; + margin:0; + min-height:100%; + overflow-x:hidden; +} + +#content{ + background:#fcfcfc; + height:100%; + margin-left:300px; + /* margin:auto; */ + max-width:1200px; + min-height:100%; + padding:1.618em 3.236em; +} + +p{ + font-size:16px; + line-height:24px; + margin:0px 0px 24px 0px; +} + +b,strong{ + font-weight:bold} + +blockquote{ + background-color: #F0F0F0; + border-left:5px solid #CCCCCC; + font-style:italic; + line-height:24px; + margin:0px 0px 24px 0px; + /* margin-left:24px; */ + padding: 6px 20px; +} + +ul,ol,dl{ + line-height:24px; + list-style-image:none; + /* list-style:none; */ + margin:0px 0px 24px 0px; + padding:0; +} + +li{ + margin-left: 24px; +} + +dd{ + margin:0; +} + +#content .section ul,#content .toctree-wrapper ul,article ul{ + list-style:disc; + line-height:24px; + margin-bottom:24px} + +#content .section ul li,#content .toctree-wrapper ul li,article ul li{ + list-style:disc; + margin-left:24px} + +#content .section ul li p:last-child,#content .toctree-wrapper ul li p:last-child,article ul li p:last-child{ + margin-bottom:0} + +#content .section ul li ul,#content .toctree-wrapper ul li ul,article ul li ul{ + margin-bottom:0} + +#content .section ul li li,#content .toctree-wrapper ul li li,article ul li li{ + list-style:circle} + +#content .section ul li li li,#content .toctree-wrapper ul li li li,article ul li li li{ + list-style:square} + +#content .section ul li ol li,#content .toctree-wrapper ul li ol li,article ul li ol li{ + list-style:decimal} + +#content .section ol,#content ol,article ol{ + list-style:decimal; + line-height:24px; + margin-bottom:24px} + +#content .section ol li,#content ol li,article ol li{ + list-style:decimal; + margin-left:24px} + +#content .section ol li p:last-child,#content ol li p:last-child,article ol li p:last-child{ + margin-bottom:0} + +#content .section ol li ul,#content ol li ul,article ol li ul{ + margin-bottom:0} + +#content .section ol li ul li,#content ol li ul li,article ol li ul li{ + list-style:disc} + +dl dt{ + font-weight:bold; +} + +dl p,dl table,dl ul,dl ol{ + margin-bottom:12px !important; +} + +dl dd{ + margin:0 0 12px 24px; +} + +@media print{ + .codeblock,pre.src{ + white-space:pre.src-wrap} +} + +@media print{ + html,body,section{ + background:none !important} + + *{ + box-shadow:none !important; + text-shadow:none !important; + filter:none !important; + -ms-filter:none !important} + + a,a:visited{ + text-decoration:underline} + + pre.src,blockquote{ + page-break-inside:avoid} + + thead{ + display:table-header-group} + + tr,img{ + page-break-inside:avoid} + + img{ + max-width:100% !important} + + @page{ + margin:0.5cm} + + p,h2,h3{ + orphans:3; + widows:3} + + h2,h3{ + page-break-after:avoid} +} + +@media print{ + #postamble{ + display:none} + + #content{ + margin-left:0} +} + +@media print{ + #table-of-contents{ + display:none} + + @page{ + size: auto; + margin: 25mm 25mm 25mm 25mm;} + + body { + margin: 0px;} +} + +@media screen and (max-width: 768px){ +} + +@media only screen and (max-width: 480px){ +} + +@media screen and (max-width: 768px){ + .tablet-hide{ + display:none} +} + +@media screen and (max-width: 480px){ + .mobile-hide{ + display:none} +} + +@media screen and (max-width: 480px){ +} + +@media screen and (max-width: 768px){ + #content{ + margin-left:0} + + #content #content{ + padding:1.618em} + + #content.shift{ + position:fixed; + min-width:100%; + left:85%; + top:0; + height:100%; + overflow:hidden} +} + +@media screen and (min-width: 1400px){ + #content{ + background:rgba(0,0,0,0.05)} + + #content{ + background:#fcfcfc} +} + +@media screen and (max-width: 768px){ + #copyright{ + width:85%; + display:none} + + #copyright.shift{ + display:block} + + img{ + width:100%; + height:auto} +} + +@media screen and (max-width: 480px){ + #content .sidebar{ + width:100%} +} + +code{ + background:#fff; + border:solid 1px #e1e4e5; + /* color:#000; for clickable code */ + font-family:Consolas,"Andale Mono WT","Andale Mono","Lucida Console","Lucida Sans Typewriter","DejaVu Sans Mono","Bitstream Vera Sans Mono","Liberation Mono","Nimbus Mono L",Monaco,"Courier New",Courier,monospace; + font-size:75%; + max-width:100%; + overflow-x:auto; + padding:0 5px; + white-space:nowrap; +} + +.codeblock-example{ + border:1px solid #e1e4e5; + border-bottom:none; + padding:24px; + padding-top:48px; + font-weight:500; + background:#fff; + position:relative} + +.codeblock-example:after{ + content:"Example"; + position:absolute; + top:0px; + left:0px; + background:#9B59B6; + color:#fff; + padding:6px 12px} + +.codeblock-example.prettyprint-example-only{ + border:1px solid #e1e4e5; + margin-bottom:24px} + +.codeblock,pre.src,#content .literal-block{ + border:1px solid #e1e4e5; + padding:12px; + overflow-x:auto; + background:#fff; + margin:1px 0 24px 0} + +pre.src{ + /* color:#404040; */ + display:block; + font-family:Consolas,"Andale Mono WT","Andale Mono","Lucida Console","Lucida Sans Typewriter","DejaVu Sans Mono","Bitstream Vera Sans Mono","Liberation Mono","Nimbus Mono L",Monaco,"Courier New",Courier,monospace; + font-size:12px; + line-height:1.5; + margin:1px 0px 24px 0px; + overflow:auto; + padding:12px; + white-space:pre; +} + +.example{ + background:#f3f6f6; + border:1px solid #e1e4e5; + color:#404040; + font-size: 12px; + line-height: 1.5; + margin-bottom:24px; + padding:12px; +} + +table{ + border-collapse:collapse; + border-spacing:0; + empty-cells:show; + margin-bottom:24px; + border-bottom:1px solid #e1e4e5; +} + +td{ + vertical-align:top} + +table td,table th{ + font-size:90%; + margin:0; + overflow:visible; + padding:8px 16px; + background-color:white; + border:1px solid #e1e4e5; +} + +table thead th{ + font-weight:bold; + border-top:3px solid #e1e4e5; + border-bottom:1px solid #e1e4e5; +} + +table caption{ + color:#000; + font:italic 85%/1 arial,sans-serif; + padding:1em 0; +} + +table tr:nth-child(2n-1) td{ + background-color:#f3f6f6; +} + +table tr:nth-child(2n) td{ + background-color:white; +} + +.figure p{ + color:#000; + font:italic 85%/1 arial,sans-serif; + padding:1em 0; +} + +.rotate-90{ + -webkit-transform:rotate(90deg); + -moz-transform:rotate(90deg); + -ms-transform:rotate(90deg); + -o-transform:rotate(90deg); + transform:rotate(90deg); +} + +.rotate-270{ + -webkit-transform:rotate(270deg); + -moz-transform:rotate(270deg); + -ms-transform:rotate(270deg); + -o-transform:rotate(270deg); + transform:rotate(270deg); +} + +#toggle-sidebar, +#table-of-contents .close-sidebar { + display: none; +} + +@media screen and (max-width: 768px) { + #table-of-contents { + display: none; + width: 60%; + } + + #table-of-contents h2 a { + display: block; + } + + #table-of-contents:target { + display: block; + } + + #copyright, #postamble { + display: none; + } + + #toggle-sidebar { + background-color: #2980B9; + display: block; + margin-bottom: 1.6em; + padding: 0.6em; + text-align: center; + } + + #toggle-sidebar h2 { + background-color:#2980B9; + width:100%; + height:50px; + left:0; + top:0; + color: white; + font-size: 100%; + line-height: 50px; + position:fixed; + margin: 0; + padding: 0; + opacity:0.7; + } + + #table-of-contents .close-sidebar { + color: rgba(255, 255, 255, 0.3); + display: inline-block; + margin: 0px 10px 0px 45px; + padding: 10px; + } +} + +*{ + -webkit-box-sizing:border-box; + -moz-box-sizing:border-box; + box-sizing:border-box; +} + +figcaption,figure,footer,header,hgroup,nav{ + display:block} + +ins{ + background:#ff9; + color:#000; + text-decoration:none} + +mark{ + background:#ff0; + color:#000; + font-style:italic; + font-weight:bold} + +small{ + font-size:85%} + +sub,sup{ + font-size:75%; + line-height:0; + position:relative; + vertical-align:baseline} + +sup{ + top:-0.5em} + +sub{ + bottom:-0.25em} + +img{ + -ms-interpolation-mode:bicubic; + vertical-align:middle; + max-width:100%} + +svg:not(:root){ + overflow:hidden} + +figure{ + margin:0} + +label{ + cursor:pointer} + +legend{ + border:0; + margin-left:-7px; + padding:0; + white-space:normal} + +.fa:before,#content .admonition-title:before,#content h1 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z=>W%p)WStFmW|fz5;N+eoUsCbHWT4|l~``5cn_8--#SX(PEogkHuYd${9DK-7n9}$9`Nv?kF6FpJ+b^-6CoVPORvkQUhncMw)PcT^?X~bAl zBaQS6?FfhvEu|CnR;hP!KOc)aZg-7Zd;6TZcx6LRvu?~AAGL#=Z9?gF}_kEaL(hP{l$?FTAU;wMOyFk*pH zQ=dv4qnB``bM}YboV>R%4FSfNgTh0aIj4pS0BlSX=EttIo7efC!nN(Xo!k9#vWZB#IX{P_CzPRX?5U*REp;9kg zwrmL;nUX8 zUmsoWnsNgNAvE$Cl-wCoD}a0Xuf2C@|9X4M5uuwb3j43>*;wv+@_9+D&*pF6@1|17Z0CKl=%N|#!~~s>zp?yt~w!H zsCg+}erwa((~EtzzdY^o{`5ll&5Mvd2k&QWpa0NLY8P;==n*xCzZ?txOnbbd_OxA4 zbH*EB;@mjV-KLb$F(<4?FyiYBDcJ%k#wCXvEIn#nLBmo<(wrHmoM$@qeod)m|Jt>g zTNkOG(tm5}sCy-pp&_@-gTe~DWM4fBbYW^ literal 0 HcmV?d00001 diff --git a/index.html b/index.html new file mode 100644 index 0000000..e15c45d --- /dev/null +++ b/index.html @@ -0,0 +1,588 @@ + + + + + + + +Cercalo Test Bench + + + + + + + + + + + + + + + +
    +

    Cercalo Test Bench

    +
    + + +
    +

    1 Identification

    +
    +
    +
    +

    1.1 Excitation Data

    +
    +
    +
    fs = 1e4;
    +Ts = 1/fs;
    +
    +
    + +

    +We generate white noise with the "random number" simulink block, and we filter that noise. +

    + +
    +
    Gi = (1)/(1+s/2/pi/100);
    +
    +
    + +
    +
    c2d(Gi, Ts, 'tustin')
    +
    +
    + +
    +c2d(Gi, Ts, 'tustin')
    +
    +ans =
    +
    +  0.030459 (z+1)
    +  --------------
    +    (z-0.9391)
    +
    +Sample time: 0.0001 seconds
    +Discrete-time zero/pole/gain model.
    +
    +
    +
    + +
    +

    1.2 Input / Output data

    +
    +

    +The identification data is loaded +

    +
    +
    ux = load('mat/data_001.mat', 't', 'ux', 'yx', 'yy');
    +uy = load('mat/data_002.mat', 't', 'uy', 'yx', 'yy');
    +
    +
    + +

    +We remove the first seconds where the Cercalo is turned on. +

    +
    +
    i0x = 20*fs;
    +
    +i0y = 10*fs;
    +
    +ux.t  = ux.t( i0x:end) - ux.t(i0x);
    +ux.ux = ux.ux(i0x:end);
    +ux.yx = ux.yx(i0x:end);
    +ux.yy = ux.yy(i0x:end);
    +
    +uy.t  = uy.t( i0y:end) - uy.t(i0x);
    +uy.uy = uy.uy(i0y:end);
    +uy.yx = uy.yx(i0y:end);
    +uy.yy = uy.yy(i0y:end);
    +
    +
    + +
    +
    ux.ux = ux.ux-mean(ux.ux);
    +ux.yx = ux.yx-mean(ux.yx);
    +ux.yy = ux.yy-mean(ux.yy);
    +
    +uy.ux = uy.ux-mean(uy.ux);
    +uy.yx = uy.yx-mean(uy.yx);
    +uy.yy = uy.yy-mean(uy.yy);
    +
    +
    + + +
    +

    identification_ux.png +

    +

    Figure 1: Identification signals when exciting the \(x\) axis (png, pdf)

    +
    + + + +
    +

    identification_uy.png +

    +

    Figure 2: Identification signals when exciting the \(y\) axis (png, pdf)

    +
    +
    +
    + +
    +

    1.3 Estimation of the Frequency Response Function Matrix

    +
    +

    +We compute an estimate of the transfer functions. +

    +
    +
    [tf_ux_yx, f] = tfestimate(ux.ux, ux.yx, hanning(ceil(1*fs)), [], [], fs);
    +[tf_ux_yy, ~] = tfestimate(ux.ux, ux.yy, hanning(ceil(1*fs)), [], [], fs);
    +[tf_uy_yx, ~] = tfestimate(uy.uy, uy.yx, hanning(ceil(1*fs)), [], [], fs);
    +[tf_uy_yy, ~] = tfestimate(uy.uy, uy.yy, hanning(ceil(1*fs)), [], [], fs);
    +
    +
    + + +
    +

    frequency_response_matrix.png +

    +

    Figure 3: Frequency Response Matrix (png, pdf)

    +
    +
    +
    + +
    +

    1.4 Coherence

    +
    +
    +
    [coh_ux_yx, f] = mscohere(ux.ux, ux.yx, hanning(ceil(1*fs)), [], [], fs);
    +[coh_ux_yy, ~] = mscohere(ux.ux, ux.yy, hanning(ceil(1*fs)), [], [], fs);
    +[coh_uy_yx, ~] = mscohere(uy.uy, uy.yx, hanning(ceil(1*fs)), [], [], fs);
    +[coh_uy_yy, ~] = mscohere(uy.uy, uy.yy, hanning(ceil(1*fs)), [], [], fs);
    +
    +
    + + +
    +

    identification_coherence.png +

    +

    Figure 4: Coherence (png, pdf)

    +
    +
    +
    + + +
    +

    1.5 Extraction of a transfer function matrix

    +
    +

    +First we define the initial guess for the resonance frequencies and the weights associated. +

    +
    +
    freqs_res = [410, 250]; % [Hz]
    +freqs_res_weights = [10, 10]; % [Hz]
    +
    +
    + +

    +From the number of resonance frequency we want to fit, we define the order N of the system we want to obtain. +

    +
    +
    N = 2*length(freqs_res);
    +
    +
    + +

    +We then make an initial guess on the complex values of the poles. +

    +
    +
    xi = 0.001; % Approximate modal damping
    +poles = [2*pi*freqs_res*(xi + 1i), 2*pi*freqs_res*(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. +

    +
    +
    weight = ones(1, length(f));
    +% weight = G_coh';
    +
    +% alpha = 0.1;
    +
    +% for freq_i = 1:length(freqs_res)
    +%   weight(f>(1-alpha)*freqs_res(freq_i) & omega<(1 + alpha)*2*pi*freqs_res(freq_i)) = freqs_res_weights(freq_i);
    +% end
    +
    +
    + +

    +Ignore data above some frequency. +

    +
    +
    weight(f>1000) = 0;
    +
    +
    + + +
    +

    weights.png +

    +

    Figure 5: Weights amplitude (png, pdf)

    +
    + +

    +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_ux_yx, poles, ~, fit_ux_yx] = vectfit3(tf_ux_yx.', 1i*2*pi*f, poles, weight, opts);
    +end
    +for iter = 1:Niter
    +  [SER_uy_yx, poles, ~, fit_uy_yx] = vectfit3(tf_uy_yx.', 1i*2*pi*f, poles, weight, opts);
    +end
    +for iter = 1:Niter
    +  [SER_ux_yy, poles, ~, fit_ux_yy] = vectfit3(tf_ux_yy.', 1i*2*pi*f, poles, weight, opts);
    +end
    +for iter = 1:Niter
    +  [SER_uy_yy, poles, ~, fit_uy_yy] = vectfit3(tf_uy_yy.', 1i*2*pi*f, poles, weight, opts);
    +end
    +
    +
    + + +
    +

    identification_matrix_fit.png +

    +

    Figure 6: Transfer Function Extraction of the FRF matrix (png, pdf)

    +
    + +

    +And finally, we create the identified state space model: +

    +
    +
    G_ux_yx = minreal(ss(full(SER_ux_yx.A),SER_ux_yx.B,SER_ux_yx.C,SER_ux_yx.D));
    +G_uy_yx = minreal(ss(full(SER_uy_yx.A),SER_uy_yx.B,SER_uy_yx.C,SER_uy_yx.D));
    +G_ux_yy = minreal(ss(full(SER_ux_yy.A),SER_ux_yy.B,SER_ux_yy.C,SER_ux_yy.D));
    +G_uy_yy = minreal(ss(full(SER_uy_yy.A),SER_uy_yy.B,SER_uy_yy.C,SER_uy_yy.D));
    +
    +G = [G_ux_yx, G_uy_yx;
    +     G_ux_yy, G_uy_yy];
    +
    +
    + +
    +
    save('mat/plant.mat', 'G');
    +
    +
    +
    +
    +
    +
    +

    2 Plant Analysis

    +
    +
    +

    3 Control

    +
    +
    +
    +

    Author: Dehaeze Thomas

    +

    Created: 2019-09-10 mar. 18:15

    +

    Validate

    +
    + + diff --git a/index.org b/index.org index ede9ad5..101a343 100644 --- a/index.org +++ b/index.org @@ -6,6 +6,14 @@ #+EMAIL: dehaeze.thomas@gmail.com #+AUTHOR: Dehaeze Thomas +#+HTML_HEAD: +#+HTML_HEAD: +#+HTML_HEAD: +#+HTML_HEAD: +#+HTML_HEAD: +#+HTML_HEAD: +#+HTML_HEAD: + #+PROPERTY: header-args:matlab :session *MATLAB* #+PROPERTY: header-args:matlab+ :comments org #+PROPERTY: header-args:matlab+ :results none @@ -17,7 +25,8 @@ :END: -* Matlab Init :noexport:ignore: +* Identification +** Matlab Init :noexport:ignore: #+begin_src matlab :tangle no :exports none :results silent :noweb yes :var current_dir=(file-name-directory buffer-file-name) <> #+end_src @@ -26,7 +35,7 @@ <> #+end_src -* Identification of the Plant +** Excitation Data #+begin_src matlab fs = 1e4; Ts = 1/fs; @@ -56,35 +65,377 @@ Sample time: 0.0001 seconds Discrete-time zero/pole/gain model. #+end_example +** Input / Output data +The identification data is loaded #+begin_src matlab - load('mat/data_001.mat', 't', 'ux') + ux = load('mat/data_001.mat', 't', 'ux', 'yx', 'yy'); + uy = load('mat/data_002.mat', 't', 'uy', 'yx', 'yy'); +#+end_src + +We remove the first seconds where the Cercalo is turned on. +#+begin_src matlab + i0x = 20*fs; + + i0y = 10*fs; + + ux.t = ux.t( i0x:end) - ux.t(i0x); + ux.ux = ux.ux(i0x:end); + ux.yx = ux.yx(i0x:end); + ux.yy = ux.yy(i0x:end); + + uy.t = uy.t( i0y:end) - uy.t(i0x); + uy.uy = uy.uy(i0y:end); + uy.yx = uy.yx(i0y:end); + uy.yy = uy.yy(i0y:end); #+end_src #+begin_src matlab - figure; plot(t, ux) + ux.ux = ux.ux-mean(ux.ux); + ux.yx = ux.yx-mean(ux.yx); + ux.yy = ux.yy-mean(ux.yy); + + uy.ux = uy.ux-mean(uy.ux); + uy.yx = uy.yx-mean(uy.yx); + uy.yy = uy.yy-mean(uy.yy); +#+end_src + +#+begin_src matlab :exports none + figure; + ax1 = subplot(1, 2, 1); + plot(ux.t, ux.ux); + xlabel('Time [s]'); + ylabel('Amplitude [V]'); + legend({'$u_x$'}); + + ax2 = subplot(1, 2, 2); + hold on; + plot(ux.t, ux.yx, 'DisplayName', '$y_x$'); + plot(ux.t, ux.yy, 'DisplayName', '$y_y$'); + hold off; + xlabel('Time [s]'); + ylabel('Amplitude [V]'); + legend() + + linkaxes([ax1,ax2],'x'); + xlim([ux.t(1), ux.t(end)]) +#+end_src + +#+HEADER: :tangle no :exports results :results none :noweb yes +#+begin_src matlab :var filepath="figs/identification_ux.pdf" :var figsize="full-normal" :post pdf2svg(file=*this*, ext="png") + <> +#+end_src + +#+NAME: fig:identification_ux +#+CAPTION: Identification signals when exciting the $x$ axis ([[./figs/identification_ux.png][png]], [[./figs/identification_ux.pdf][pdf]]) +[[file:figs/identification_ux.png]] + + +#+begin_src matlab :exports none + figure; + ax1 = subplot(1, 2, 1); + plot(uy.t, uy.uy); + xlabel('Time [s]'); + ylabel('Amplitude [V]'); + legend({'$u_y$'}); + + ax2 = subplot(1, 2, 2); + hold on; + plot(uy.t, uy.yy, 'DisplayName', '$y_y$'); + plot(uy.t, uy.yx, 'DisplayName', '$y_x$'); + hold off; + xlabel('Time [s]'); + ylabel('Amplitude [V]'); + legend() + + linkaxes([ax1,ax2],'x'); + xlim([uy.t(1), uy.t(end)]) +#+end_src + +#+HEADER: :tangle no :exports results :results none :noweb yes +#+begin_src matlab :var filepath="figs/identification_uy.pdf" :var figsize="full-normal" :post pdf2svg(file=*this*, ext="png") + <> +#+end_src + +#+NAME: fig:identification_uy +#+CAPTION: Identification signals when exciting the $y$ axis ([[./figs/identification_uy.png][png]], [[./figs/identification_uy.pdf][pdf]]) +[[file:figs/identification_uy.png]] + +** Estimation of the Frequency Response Function Matrix +We compute an estimate of the transfer functions. +#+begin_src matlab + [tf_ux_yx, f] = tfestimate(ux.ux, ux.yx, hanning(ceil(1*fs)), [], [], fs); + [tf_ux_yy, ~] = tfestimate(ux.ux, ux.yy, hanning(ceil(1*fs)), [], [], fs); + [tf_uy_yx, ~] = tfestimate(uy.uy, uy.yx, hanning(ceil(1*fs)), [], [], fs); + [tf_uy_yy, ~] = tfestimate(uy.uy, uy.yy, hanning(ceil(1*fs)), [], [], fs); +#+end_src + +#+begin_src matlab :exports none + figure; + ax11 = subplot(2, 2, 1); + hold on; + plot(f, abs(tf_ux_yx)) + title('Frequency Response Function $\frac{y_x}{u_x}$') + set(gca, 'Xscale', 'log'); set(gca, 'Yscale', 'log'); + ylabel('Amplitude') + hold off; + + ax12 = subplot(2, 2, 2); + hold on; + plot(f, abs(tf_ux_yy)) + title('Frequency Response Function $\frac{y_x}{u_y}$') + set(gca, 'Xscale', 'log'); set(gca, 'Yscale', 'log'); + hold off; + + ax21 = subplot(2, 2, 3); + hold on; + plot(f, abs(tf_uy_yx)) + title('Frequency Response Function $\frac{y_y}{u_x}$') + set(gca, 'Xscale', 'log'); set(gca, 'Yscale', 'log'); + ylabel('Amplitude') + xlabel('Frequency [Hz]') + hold off; + + ax22 = subplot(2, 2, 4); + hold on; + plot(f, abs(tf_uy_yy)) + title('Frequency Response Function $\frac{y_y}{u_y}$') + set(gca, 'Xscale', 'log'); set(gca, 'Yscale', 'log'); + xlabel('Frequency [Hz]') + hold off; + + linkaxes([ax11,ax12,ax21,ax22],'x'); + xlim([10, 1000]); + linkaxes([ax11,ax12,ax21,ax22],'y'); + ylim([1e-2, 1e3]) +#+end_src + +#+HEADER: :tangle no :exports results :results none :noweb yes +#+begin_src matlab :var filepath="figs/frequency_response_matrix.pdf" :var figsize="full-tall" :post pdf2svg(file=*this*, ext="png") + <> +#+end_src + +#+NAME: fig:frequency_response_matrix +#+CAPTION: Frequency Response Matrix ([[./figs/frequency_response_matrix.png][png]], [[./figs/frequency_response_matrix.pdf][pdf]]) +[[file:figs/frequency_response_matrix.png]] + +** Coherence +#+begin_src matlab + [coh_ux_yx, f] = mscohere(ux.ux, ux.yx, hanning(ceil(1*fs)), [], [], fs); + [coh_ux_yy, ~] = mscohere(ux.ux, ux.yy, hanning(ceil(1*fs)), [], [], fs); + [coh_uy_yx, ~] = mscohere(uy.uy, uy.yx, hanning(ceil(1*fs)), [], [], fs); + [coh_uy_yy, ~] = mscohere(uy.uy, uy.yy, hanning(ceil(1*fs)), [], [], fs); +#+end_src + +#+begin_src matlab :exports none + figure; + ax11 = subplot(2, 2, 1); + hold on; + plot(f, coh_ux_yx) + set(gca, 'Xscale', 'log'); + title('Coherence $\frac{y_x}{u_x}$') + ylabel('Coherence') + hold off; + + ax12 = subplot(2, 2, 2); + hold on; + plot(f, coh_ux_yy) + set(gca, 'Xscale', 'log'); + title('Coherence $\frac{y_x}{u_y}$') + hold off; + + ax21 = subplot(2, 2, 3); + hold on; + plot(f, coh_uy_yx) + set(gca, 'Xscale', 'log'); + title('Coherence $\frac{y_y}{u_x}$') + ylabel('Coherence') + xlabel('Frequency [Hz]') + hold off; + + ax22 = subplot(2, 2, 4); + hold on; + plot(f, coh_uy_yy) + set(gca, 'Xscale', 'log'); + title('Coherence $\frac{y_y}{u_y}$') + xlabel('Frequency [Hz]') + hold off; + + linkaxes([ax11,ax12,ax21,ax22],'x'); + xlim([10, 1000]); + linkaxes([ax11,ax12,ax21,ax22],'y'); + ylim([0, 1]) +#+end_src + +#+HEADER: :tangle no :exports results :results none :noweb yes +#+begin_src matlab :var filepath="figs/identification_coherence.pdf" :var figsize="full-tall" :post pdf2svg(file=*this*, ext="png") + <> +#+end_src + +#+NAME: fig:identification_coherence +#+CAPTION: Coherence ([[./figs/identification_coherence.png][png]], [[./figs/identification_coherence.pdf][pdf]]) +[[file:figs/identification_coherence.png]] + + +** Extraction of a transfer function matrix +First we define the initial guess for the resonance frequencies and the weights associated. +#+begin_src matlab + freqs_res = [410, 250]; % [Hz] + freqs_res_weights = [10, 10]; % [Hz] +#+end_src + +From the number of resonance frequency we want to fit, we define the order =N= of the system we want to obtain. +#+begin_src matlab + N = 2*length(freqs_res); +#+end_src + +We then make an initial guess on the complex values of the poles. +#+begin_src matlab + xi = 0.001; % Approximate modal damping + poles = [2*pi*freqs_res*(xi + 1i), 2*pi*freqs_res*(xi - 1i)]; +#+end_src + +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. +#+begin_src matlab + weight = ones(1, length(f)); + % weight = G_coh'; + + % alpha = 0.1; + + % for freq_i = 1:length(freqs_res) + % weight(f>(1-alpha)*freqs_res(freq_i) & omega<(1 + alpha)*2*pi*freqs_res(freq_i)) = freqs_res_weights(freq_i); + % end +#+end_src + +Ignore data above some frequency. +#+begin_src matlab + weight(f>1000) = 0; +#+end_src + +#+begin_src matlab :exports none + figure; + hold on; + plot(f, weight); + plot(freqs_res, ones(size(freqs_res)), 'rx'); + hold off; + xlabel('Frequency [Hz]'); + xlabel('Weight Amplitude'); + set(gca, 'xscale', 'log'); + xlim([f(1), f(end)]); +#+end_src + +#+HEADER: :tangle no :exports results :results none :noweb yes +#+begin_src matlab :var filepath="figs/weights.pdf" :var figsize="wide-normal" :post pdf2svg(file=*this*, ext="png") + <> +#+end_src + +#+NAME: fig:weights +#+CAPTION: Weights amplitude ([[./figs/weights.png][png]], [[./figs/weights.pdf][pdf]]) +[[file:figs/weights.png]] + +When we set some options for =vfit3=. +#+begin_src matlab + 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) +#+end_src + +We define the number of iteration. +#+begin_src matlab + Niter = 5; +#+end_src + +An we run the =vectfit3= algorithm. +#+begin_src matlab + for iter = 1:Niter + [SER_ux_yx, poles, ~, fit_ux_yx] = vectfit3(tf_ux_yx.', 1i*2*pi*f, poles, weight, opts); + end + for iter = 1:Niter + [SER_uy_yx, poles, ~, fit_uy_yx] = vectfit3(tf_uy_yx.', 1i*2*pi*f, poles, weight, opts); + end + for iter = 1:Niter + [SER_ux_yy, poles, ~, fit_ux_yy] = vectfit3(tf_ux_yy.', 1i*2*pi*f, poles, weight, opts); + end + for iter = 1:Niter + [SER_uy_yy, poles, ~, fit_uy_yy] = vectfit3(tf_uy_yy.', 1i*2*pi*f, poles, weight, opts); + end +#+end_src + +#+begin_src matlab :exports none + figure; + ax11 = subplot(2, 2, 1); + hold on; + plot(f, abs(tf_ux_yx)) + plot(f, abs(fit_ux_yx)) + title('Frequency Response Function $\frac{y_x}{u_x}$') + set(gca, 'Xscale', 'log'); set(gca, 'Yscale', 'log'); + ylabel('Amplitude') + hold off; + + ax12 = subplot(2, 2, 2); + hold on; + plot(f, abs(tf_ux_yy)) + plot(f, abs(fit_ux_yy)) + title('Frequency Response Function $\frac{y_x}{u_y}$') + set(gca, 'Xscale', 'log'); set(gca, 'Yscale', 'log'); + hold off; + + ax21 = subplot(2, 2, 3); + hold on; + plot(f, abs(tf_uy_yx)) + plot(f, abs(fit_uy_yx)) + title('Frequency Response Function $\frac{y_y}{u_x}$') + set(gca, 'Xscale', 'log'); set(gca, 'Yscale', 'log'); + ylabel('Amplitude') + xlabel('Frequency [Hz]') + hold off; + + ax22 = subplot(2, 2, 4); + hold on; + plot(f, abs(tf_uy_yy)) + plot(f, abs(fit_uy_yy)) + title('Frequency Response Function $\frac{y_y}{u_y}$') + set(gca, 'Xscale', 'log'); set(gca, 'Yscale', 'log'); + xlabel('Frequency [Hz]') + hold off; + + linkaxes([ax11,ax12,ax21,ax22],'x'); + xlim([10, 1000]); + linkaxes([ax11,ax12,ax21,ax22],'y'); + ylim([1e-2, 1e3]) +#+end_src + +#+HEADER: :tangle no :exports results :results none :noweb yes +#+begin_src matlab :var filepath="figs/identification_matrix_fit.pdf" :var figsize="full-tall" :post pdf2svg(file=*this*, ext="png") + <> +#+end_src + +#+NAME: fig:identification_matrix_fit +#+CAPTION: Transfer Function Extraction of the FRF matrix ([[./figs/identification_matrix_fit.png][png]], [[./figs/identification_matrix_fit.pdf][pdf]]) +[[file:figs/identification_matrix_fit.png]] + +And finally, we create the identified state space model: +#+begin_src matlab + G_ux_yx = minreal(ss(full(SER_ux_yx.A),SER_ux_yx.B,SER_ux_yx.C,SER_ux_yx.D)); + G_uy_yx = minreal(ss(full(SER_uy_yx.A),SER_uy_yx.B,SER_uy_yx.C,SER_uy_yx.D)); + G_ux_yy = minreal(ss(full(SER_ux_yy.A),SER_ux_yy.B,SER_ux_yy.C,SER_ux_yy.D)); + G_uy_yy = minreal(ss(full(SER_uy_yy.A),SER_uy_yy.B,SER_uy_yy.C,SER_uy_yy.D)); + + G = [G_ux_yx, G_uy_yx; + G_ux_yy, G_uy_yy]; #+end_src #+begin_src matlab - [tf_est, freqs] = tfestimate(ux, yx, hanning(ceil(length(ux)/10)), [], [], fs); - - % h = idfrd(tf_est, freqs*2*pi, Ts); -#+end_src - -#+begin_src matlab -figure; -ax1 = subplot(2, 1, 1); -hold on; -plot(freqs, abs(tf_est),'--') -set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log'); -ylabel('Amplitude [m/N]'); -hold off; - -ax2 = subplot(2, 1, 2); -hold on; -plot(freqs, mod(180+180/pi*phase(tf_est), 360)-180,'--') -set(gca,'xscale','log'); -ylim([-180, 180]); -yticks([-180, -90, 0, 90, 180]); -xlabel('Frequency [$Hz$]'); ylabel('Phase [deg]'); -hold off; + save('mat/plant.mat', 'G'); #+end_src +* Plant Analysis +* Control diff --git a/js/bootstrap.min.js b/js/bootstrap.min.js new file mode 100644 index 0000000..c8f82e5 --- /dev/null +++ b/js/bootstrap.min.js @@ -0,0 +1,7 @@ +/*! + * Bootstrap v3.3.4 (http://getbootstrap.com) + * Copyright 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