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    Table of Contents

    -
    -

    1 Identification

    +
    +

    1 Identification

    - +

    @@ -306,8 +306,8 @@ All the files (data and Matlab scripts) are accessible -

    1.1 Excitation Data

    +
    +

    1.1 Excitation Data

    fs = 1e4;
    @@ -344,8 +344,8 @@ Discrete-time zero/pole/gain model.
     
    -
    -

    1.2 Input / Output data

    +
    +

    1.2 Input / Output data

    The identification data is loaded @@ -388,7 +388,7 @@ uy.yy = uy.yy-mean +

    identification_ux.png

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

    @@ -396,7 +396,7 @@ uy.yy = uy.yy-mean +

    identification_uy.png

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

    @@ -404,8 +404,8 @@ uy.yy = uy.yy-mean -

    1.3 Estimation of the Frequency Response Function Matrix

    +
    +

    1.3 Estimation of the Frequency Response Function Matrix

    We compute an estimate of the transfer functions. @@ -419,7 +419,7 @@ We compute an estimate of the transfer functions.

    -
    +

    frequency_response_matrix.png

    Figure 3: Frequency Response Matrix (png, pdf)

    @@ -427,8 +427,8 @@ We compute an estimate of the transfer functions.
    -
    -

    1.4 Coherence

    +
    +

    1.4 Coherence

    [coh_ux_yx, f] = mscohere(ux.ux, ux.yx, hanning(ceil(1*fs)), [], [], fs);
    @@ -439,7 +439,7 @@ We compute an estimate of the transfer functions.
     
    -
    +

    identification_coherence.png

    Figure 4: Coherence (png, pdf)

    @@ -448,8 +448,8 @@ We compute an estimate of the transfer functions.
    -
    -

    1.5 Extraction of a transfer function matrix

    +
    +

    1.5 Extraction of a transfer function matrix

    First we define the initial guess for the resonance frequencies and the weights associated. @@ -503,7 +503,7 @@ Ignore data above some frequency.

    -
    +

    weights.png

    Figure 5: Weights amplitude (png, pdf)

    @@ -555,7 +555,7 @@ An we run the vectfit3 algorithm.
    -
    +

    identification_matrix_fit.png

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

    @@ -582,16 +582,17 @@ G = [G_ux_yx, G_uy_yx;
    -
    -

    2 Plant Analysis

    + +
    +

    2 Plant Analysis

    -
    -

    3 Control

    +
    +

    3 Control

    Author: Dehaeze Thomas

    -

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

    +

    Created: 2019-09-11 mer. 09:10

    Validate

    diff --git a/index.org b/index.org index ece7dcd..e0322f3 100644 --- a/index.org +++ b/index.org @@ -203,14 +203,14 @@ We compute an estimate of the transfer functions. ax12 = subplot(2, 2, 2); hold on; - plot(f, abs(tf_ux_yy)) + plot(f, abs(tf_uy_yx)) 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(tf_ux_yy)) title('Frequency Response Function $\frac{y_y}{u_x}$') set(gca, 'Xscale', 'log'); set(gca, 'Yscale', 'log'); ylabel('Amplitude') @@ -260,14 +260,14 @@ We compute an estimate of the transfer functions. ax12 = subplot(2, 2, 2); hold on; - plot(f, coh_ux_yy) + plot(f, coh_uy_yx) 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) + plot(f, coh_ux_yy) set(gca, 'Xscale', 'log'); title('Coherence $\frac{y_y}{u_x}$') ylabel('Coherence') @@ -405,16 +405,16 @@ An we run the =vectfit3= algorithm. ax12 = subplot(2, 2, 2); hold on; - plot(f, abs(tf_ux_yy)) - plot(f, abs(fit_ux_yy)) + plot(f, abs(tf_uy_yx)) + plot(f, abs(fit_uy_yx)) 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)) + plot(f, abs(tf_ux_yy)) + plot(f, abs(fit_ux_yy)) title('Frequency Response Function $\frac{y_y}{u_x}$') set(gca, 'Xscale', 'log'); set(gca, 'Yscale', 'log'); ylabel('Amplitude') @@ -459,5 +459,6 @@ And finally, we create the identified state space model: #+begin_src matlab save('mat/plant.mat', 'G'); #+end_src + * Plant Analysis * Control diff --git a/mat/plant.mat b/mat/plant.mat index 6c39a2569a931f8b76db7689e4cd9d3961fe53a4..837729ae85fe36253d458f6b00f6e2fb249963c4 100644 GIT binary patch delta 1258 zcmVDCk91<@R%ez3Pvjg{=rHfm}0j%yB)J9*N5GCq%9@AxK=YY7&fN#Ejhz~R3SZ>F?Q)H zuwfN67>5a%1Rd7uS#1V}OHe9JGK;Kf6ts!wATyz5^-0K>nyGKnrgXQ*ut3YrK*=l` zxo07xO%<|sXP{_Isx}0qbA@S>>8oy+J7?X2Vt%@y!r-*&sknVt$L({aO?gu<+04<} zsuxQ}KBqzzv9(xC#%?Bma#yvPlh-Aj-$dj^M2-`=lgJ4oQ%>U%DNJOh9|M2gktD7w z5nbdG(M2wfqc53^%bZ8Zc!cvP8IN+_M#kGXZzmY^K1Mk##4htbTg#=q-@!2>f_{|u zWj=@d8222T$NaH*Tw6Ae>xz4=l@u5Fz2NU_%oWCng!zcIz)op@V2Ak??Giqmg52@n zULPj`{(bKGw9b?|hpgd&uyJtP z(ZJ*3eie)(X#Z8zw)2O$?LxK7TA_T}i34C;x%x zDfg>j96|f1WGzZ>AL(CVEiZp?spqrc?+@-*!8n5UUqxnqS9Fi2sJ)8(DR3IxK%jcT}NJsnzg^t_;u|U${WjB)p^T*77op?IoSD-i8yfn%F_47 zyWaKWp^x?%tM~U>C|~F`f7*Q9|KIC<=S_3j>|LJz{M&tH#W|;LhQ@iL_gjhvUxS)Cf2Ts% zSAW3wlce38_hL5TzWh2Pre4fxSs!0n;Ce-Ta=x#)zE|IlW%o@yWfawf&FIg&;MH9{>QD}uUO+k zT*MUXjP>?wDpJ2LNpa{tt@S*E3LLr0Ui@FXmggbXXl}O~FFLYbjk;s?K8Q(;@g?4C z)qcbpT&wV80k7G-_k^!6?;Tv81`w;ZAl7Z6qH=4B zc%1E7U1%It6h6BwTwCq@d6YIDQ1z;(>XOc4zZ+~(NYl8Rb4x%PH1+IVS$>RhJv0? zXOBZnoycX(&i;HlrI-*fl+8`)Okc6P+&Sy+&reR}6d0b;9TmH8>)3s+bZ}DF3MR9( z#-2_3;G*Tqf>U#yH&eUq)wpPXDz0m=S*>@#@`O z?&pL1l`#&t{Zm?hv-OWpBJIZXaQ{n*FVDFi5BDo$9B%tBqpuc!UH+xnm}~P7{NCyQ z{@{LPjKgjJl#ITAxz6oMxAAt*-R1Kq-QOSFuZ(fH?Vr-@$4_s$^=#BwNsOPl@(;W} zxL+CLaN9p6W2I&L$evc?`pxcC_vh~K5AIjSINbJ6X?AGr-Tp40vFqjMGoRgdKOfw$ zjB!}^22bsK1HFF;ms^?h*(&l{RImMm#;R^C|7%I;f#rVxDX?0CnHrw|7=clQ2p zYId7}^2nX%5`E!&Btd%E+T42AP{rO+Z+yA)b=M`&Qh>MtF zow3?^jryzCB`FR)r0*lx|CEw-t-GHX*7-w36GXJYwy#QFsBk z_jRE@d8O;phbqY&Y27R51^Y_BL%k^2d$}zB R4khNHp5LJu{SESkZ)iT6p349L diff --git a/matlab/plant_identification.m b/matlab/plant_identification.m index 743cc52..5bfb182 100644 --- a/matlab/plant_identification.m +++ b/matlab/plant_identification.m @@ -115,14 +115,14 @@ hold off; ax12 = subplot(2, 2, 2); hold on; -plot(f, abs(tf_ux_yy)) +plot(f, abs(tf_uy_yx)) 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(tf_ux_yy)) title('Frequency Response Function $\frac{y_y}{u_x}$') set(gca, 'Xscale', 'log'); set(gca, 'Yscale', 'log'); ylabel('Amplitude') @@ -160,14 +160,14 @@ hold off; ax12 = subplot(2, 2, 2); hold on; -plot(f, coh_ux_yy) +plot(f, coh_uy_yx) 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) +plot(f, coh_ux_yy) set(gca, 'Xscale', 'log'); title('Coherence $\frac{y_y}{u_x}$') ylabel('Coherence') @@ -292,16 +292,16 @@ hold off; ax12 = subplot(2, 2, 2); hold on; -plot(f, abs(tf_ux_yy)) -plot(f, abs(fit_ux_yy)) +plot(f, abs(tf_uy_yx)) +plot(f, abs(fit_uy_yx)) 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)) +plot(f, abs(tf_ux_yy)) +plot(f, abs(fit_ux_yy)) title('Frequency Response Function $\frac{y_y}{u_x}$') set(gca, 'Xscale', 'log'); set(gca, 'Yscale', 'log'); ylabel('Amplitude')

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