measure one mass with solidification

.gitignore

@@ -2,7 +2,17 @@ sim_data/
 
 auto/
 *.tex
+*.blg
+*-blx.bib
 *.bbl
+*.aux
+*.bcf
+*.fdb_latexmk
+*.log
+*.out
+*.pyg
+*.toc
+*.fls
 *.synctex.gz
 .auctex-auto/
 _minted*

matlab/identif_S_T.m

@@ -0,0 +1,24 @@
+load('mat/T_S_meas_Dz_3m_hac_svd_iff.mat', 't', 'de', 'Vs', 'u', 'Va', 'Rx', 'Kiff', 'Khac_iff_struts');
+
+Ts = (t(end) - t(1))/(length(t)-1);
+Fs = 1/Ts;
+
+win = hanning(ceil(10*Fs)); % Hannning Windows
+
+load('mat/jacobian.mat', 'J');
+
+y = (inv(J)*de')';
+
+[T, f] = tfestimate(Rx(:,3), y(:,3), win, [], [], 1/Ts);
+[S, ~] = tfestimate(Rx(:,3), Rx(:,3)-y(:,3), win, [], [], 1/Ts);
+
+figure;
+hold on;
+plot(f, abs(T));
+plot(f, abs(S));
+hold off;
+set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
+xlabel('Frequency [Hz]'); ylabel('Amplitude');
+grid;
+xlim([1, 2e3]);
+

matlab/identif_analyze.m

@@ -10,8 +10,8 @@ addpath('./src/');
 
 %% Load measurement data for APA number 1
 strut_number = 1;
-% load(sprintf('mat/frf_data_exc_strut_%i_noise_lf.mat', strut_number), 't', 'Va', 'Vs', 'de');
-load(sprintf('mat/iff_strut_%i_noise.mat', strut_number), 't', 'Va', 'Vs', 'de');
+load(sprintf('mat/frf_data_exc_strut_%i_spindle_0m.mat', strut_number), 't', 'Va', 'Vs', 'de');
+% load(sprintf('mat/iff_strut_%i_noise.mat', strut_number), 't', 'Va', 'Vs', 'de');
 
 % Compute transfer functions:
 
@@ -21,7 +21,7 @@ Fs = 1/Ts;
 win = hanning(ceil(1*Fs)); % Hannning Windows
 
 %% DVF
-[G_dvf, f] = tfestimate(Vexc, de, win, [], [], 1/Ts);
+[G_dvf, f] = tfestimate(Va, de, win, [], [], 1/Ts);
 
 figure;
 tiledlayout(3, 1, 'TileSpacing', 'None', 'Padding', 'None');
@@ -29,12 +29,14 @@ tiledlayout(3, 1, 'TileSpacing', 'None', 'Padding', 'None');
 ax1 = nexttile([2,1]);
 hold on;
 for i =1:6
-    plot(f, abs(G_dvf(:,i)));
+    plot(f, abs(G_dvf(:,i)), 'DisplayName', sprintf('Leg %i', i));
 end
 hold off;
 set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
 ylabel('Amplitude $V_{out}/V_{in}$ [V/V]'); set(gca, 'XTickLabel',[]);
 hold off;
+legend;
+grid;
 
 ax2 = nexttile;
 hold on;
@@ -46,6 +48,7 @@ set(gca, 'XScale', 'log'); set(gca, 'YScale', 'lin');
 xlabel('Frequency [Hz]'); ylabel('Phase [deg]');
 hold off;
 yticks(-360:90:360);
+grid;
 
 linkaxes([ax1,ax2],'x');
 xlim([5, 5e3]);

matlab/identif_analyze_all.m

@@ -9,42 +9,34 @@ addpath('./src/');
 % Test with one APA
 
 %% Load measurement data for APA number 1
-meas_data_lf = {};
+meas_data = {};
 for i = 1:6
-    meas_data_lf(i) = {load(sprintf('mat/frf_data_exc_strut_%i_noise_lf.mat', i), 't', 'Va', 'Vs', 'de')};
-    meas_data_hf(i) = {load(sprintf('mat/frf_data_exc_strut_%i_noise_hf.mat', i), 't', 'Va', 'Vs', 'de')};
+    meas_data(i) = {load(sprintf('mat/frf_data_exc_strut_%i_spindle_0m.mat', i), 't', 'Va', 'Vs', 'de')};
 end
 
 %%
-Ts = (meas_data_lf{1}.t(end) - (meas_data_lf{1}.t(1)))/(length(meas_data_lf{1}.t)-1);
+Ts = (meas_data{1}.t(end) - (meas_data{1}.t(1)))/(length(meas_data{1}.t)-1);
 Fs = 1/Ts;
 
 win = hanning(ceil(1*Fs)); % Hannning Windows
 
 %% DVF
-[~, f] = tfestimate(meas_data_lf{1}.Va, meas_data_lf{1}.de, win, [], [], 1/Ts);
+[~, f] = tfestimate(meas_data{1}.Va, meas_data{1}.de, win, [], [], 1/Ts);
 
-G_dvf_lf = zeros(length(f), 6, 6);
-G_dvf_hf = zeros(length(f), 6, 6);
+G_dvf = zeros(length(f), 6, 6);
 for i = 1:6
-    G_dvf_lf(:, :, i) = tfestimate(meas_data_lf{i}.Va, meas_data_lf{i}.de, win, [], [], 1/Ts);
-    G_dvf_hf(:, :, i) = tfestimate(meas_data_hf{i}.Va, meas_data_hf{i}.de, win, [], [], 1/Ts);
+    G_dvf(:, :, i) = tfestimate(meas_data{i}.Va, meas_data{i}.de, win, [], [], 1/Ts);
 end
 
 %% IFF
-[~, f] = tfestimate(meas_data_lf{1}.Va, meas_data_lf{1}.de, win, [], [], 1/Ts);
+[~, f] = tfestimate(meas_data{1}.Va, meas_data{1}.de, win, [], [], 1/Ts);
 
-G_iff_lf = zeros(length(f), 6, 6);
-G_iff_hf = zeros(length(f), 6, 6);
+G_iff = zeros(length(f), 6, 6);
 for i = 1:6
-    G_iff_lf(:, :, i) = tfestimate(meas_data_lf{i}.Va, meas_data_lf{i}.Vs, win, [], [], 1/Ts);
-    G_iff_hf(:, :, i) = tfestimate(meas_data_hf{i}.Va, meas_data_hf{i}.Vs, win, [], [], 1/Ts);
+    G_iff(:, :, i) = tfestimate(meas_data{i}.Va, meas_data{i}.Vs, win, [], [], 1/Ts);
 end
 
 %%
-i_lf = f < 250;
-i_hf = f > 250;
-
 figure;
 tiledlayout(3, 1, 'TileSpacing', 'None', 'Padding', 'None');
 
@@ -52,9 +44,7 @@ ax1 = nexttile([2,1]);
 hold on;
 for i =1:6
     set(gca,'ColorOrderIndex',i)
-    plot(f(i_lf), abs(G_dvf_lf(i_lf,i, i)));
-    set(gca,'ColorOrderIndex',i)
-    plot(f(i_hf), abs(G_dvf_hf(i_hf,i, i)));
+    plot(f, abs(G_dvf(:,i, i)));
 end
 hold off;
 set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
@@ -65,9 +55,7 @@ ax2 = nexttile;
 hold on;
 for i =1:6
     set(gca,'ColorOrderIndex',i)
-    plot(f(i_lf), 180/pi*angle(G_dvf_lf(i_lf,i, i)));
-    set(gca,'ColorOrderIndex',i)
-    plot(f(i_hf), 180/pi*angle(G_dvf_hf(i_hf,i, i)));
+    plot(f, 180/pi*angle(G_dvf(:,i, i)));
 end
 hold off;
 set(gca, 'XScale', 'log'); set(gca, 'YScale', 'lin');
@@ -76,7 +64,7 @@ hold off;
 yticks(-360:90:360);
 
 linkaxes([ax1,ax2],'x');
-xlim([20, 2e3]);
+xlim([10, 1e3]);
 
 %%
 figure;
@@ -86,9 +74,7 @@ ax1 = nexttile([2,1]);
 hold on;
 for i =1:6
     set(gca,'ColorOrderIndex',i)
-    plot(f(i_lf), abs(G_iff_lf(i_lf,i, i)));
-    set(gca,'ColorOrderIndex',i)
-    plot(f(i_hf), abs(G_iff_hf(i_hf,i, i)));
+    plot(f, abs(G_iff(:,i, i)));
 end
 hold off;
 set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
@@ -99,9 +85,7 @@ ax2 = nexttile;
 hold on;
 for i =1:6
     set(gca,'ColorOrderIndex',i)
-    plot(f(i_lf), 180/pi*angle(G_iff_lf(i_lf,i, i)));
-    set(gca,'ColorOrderIndex',i)
-    plot(f(i_hf), 180/pi*angle(G_iff_hf(i_hf,i, i)));
+    plot(f, 180/pi*angle(G_iff(:,i, i)));
 end
 hold off;
 set(gca, 'XScale', 'log'); set(gca, 'YScale', 'lin');

matlab/identif_save.m

@@ -1,30 +1,35 @@
+%%
 tg = slrt;
 
 f = SimulinkRealTime.openFTP(tg);
 mget(f, 'data/data.dat');
 close(f);
 
-% And we load the data on the Workspace:
-
+%% And we load the data on the Workspace:
 data = SimulinkRealTime.utils.getFileScopeData('data/data.dat').data;
 
 de = data(:, 1:6);   % Measurment displacement (encoder) [m]
 Vs = data(:, 7:12);  % Force Sensor [V]
 u  = data(:, 13:18); % Control Output [V]
 Va = data(:, 19); % Excitation Signal [V]
+Rx = data(:, 20:25); % Excitation Signal [V]
 t  = data(:, end);   % Time [s]
 
-% strut_num = 6;
-% save(sprintf('mat/frf_exc_iff_strut_%i_enc_plates_noise.mat', strut_num), 't', 'de', 'Vs', 'u', 'Va');
-
-save('mat/hac_iff_more_lead_nass_scan', 't', 'de', 'Vs', 'u', 'Va');
+%%
+load('sim_data/Khac_iff_struts.mat', 'Khac_iff_struts');
+load('sim_data/Kiff.mat', 'Kiff');
 
 %%
-load('mat/jacobian.mat', 'J');
+save('mat/T_S_meas_Rz_3m_hac_svd_iff.mat', 't', 'de', 'Vs', 'u', 'Va', 'Rx', 'Kiff', 'Khac_iff_struts');
 
-X = inv(J)*de';
-X = X';
+% save('mat/hac_iff_more_lead_nass_scan', 't', 'de', 'Vs', 'u', 'Va');
 
-%%
-figure;
-plot3(X(:,1), X(:,2), X(:,3))
+% %%
+% load('mat/jacobian.mat', 'J');
+% 
+% X = inv(J)*de';
+% X = X';
+% 
+% %%
+% figure;
+% plot3(X(:,1), X(:,2), X(:,3))

matlab/init_hac_iff.m

@@ -1,51 +1,25 @@
 %%
-clear;
-
-%% Jacobian
-load('mat/jacobian.mat', 'J');
-
-Jinv = pinv(J);
-
-save('sim_data/J.mat', 'J', 'Jinv');
+clear; close all; clc;
 
 %% Saved HAC
 % load('mat/Khac_iff_struts.mat', 'Khac_iff_struts');
-
-%% Developed HAC
-s = zpk('s');
-
-a  = 5;  % Amount of phase lead / width of the phase lead / high frequency gain
-wc = 2*pi*110; % Frequency with the maximum phase lead [rad/s]
-
-H_lead = (1 + s/(wc/sqrt(a)))/(1 + s/(wc*sqrt(a)));
-H_lpf = 1/(1 + s/2/pi/300);
-
-gm = 0.02;
-xi = 0.5;
-wn = 2*pi*700;
-
-H_notch = (s^2 + 2*gm*xi*wn*s + wn^2)/(s^2 + 2*xi*wn*s + wn^2);
-
-Khac_iff_struts = -2.2e4 * ... % Gain
-                  H_lead * ...       % Lead
-                  H_lpf * ...       % Lead
-                  H_notch * ...      % Notch
-                  (2*pi*100/s) * ... % Integrator
-                  eye(6);            % 6x6 Diagonal
+% load('mat/Khac_iff_struts_rob.mat', 'Kd');
+load('mat/Khac_iff_struts_svd.mat', 'Kd');
+% load('mat/Khac_iff_struts_jacobian_cok.mat', 'Kd');
 
 %% Save Controller
 load('sim_data/data_sim.mat', 'Ts')
-Khac_iff_struts = c2d(Khac_iff_struts, Ts, 'Tustin');
+Khac_iff_struts = c2d(Kd, Ts, 'Tustin');
 
 save('sim_data/Khac_iff_struts.mat', 'Khac_iff_struts');
 
 %% Loop Gain
-load('mat/damped_plant_enc_plates.mat', 'f', 'G_enc_iff_opt')
+load('mat/frf_iff_vib_table_m.mat', 'f', 'G_dL');
 
-L_frf = zeros(size(G_enc_iff_opt));
+L_frf = zeros(size(G_dL{3}));
 
-for i = 1:size(G_enc_iff_opt, 1)
-    L_frf(i, :, :) = squeeze(G_enc_iff_opt(i,:,:))*freqresp(Khac_iff_struts, f(i), 'Hz');
+for i = 1:length(f)
+    L_frf(i, :, :) = squeeze(G_dL{3}(i,:,:))*freqresp(Khac_iff_struts, f(i), 'Hz');
 end
 
 colors = colororder;

matlab/init_iff.m

@@ -1,8 +1,14 @@
 %%
-clear;
+clear; close all; clc;
 
 %%
-load('mat/Kiff.mat', 'Kiff');
+% load('mat/Kiff.mat', 'Kiff');
+s = zpk( 's');
+Kiff = -2e2*...
+       (1/(s + 2*pi*20))*...    % LPF: provides integral action above x[Hz]
+       (s/(s + 2*pi*20))*...    % HPF: limit low frequency gain
+       (1/(1 + s/2/pi/400))*... % LPF: more robust to high frequency resonances
+       eye(6);                  % Diagonal controller
 
 %%
 load('sim_data/data_sim.mat', 'Ts')

matlab/init_ref_dist.m

@@ -0,0 +1,38 @@
+%%
+clear; close all; clc;
+
+%%
+load('sim_data/data_sim.mat', 'Trec_start', 'Trec_dur', 'Ts');
+
+%% White Noise => Identification of the sensibility transfer function
+s = zpk('s');
+
+Rx_noise = generateShapedNoise('Ts', Ts, ...
+    'V_mean', 0, ...
+    't_start', Trec_start, ...
+    'exc_duration', Trec_dur, ...
+    'V_exc', 2e-6/(2*pi*0.2 + s));
+
+R_dist = timeseries(Rx_noise(2, :), Rx_noise(1, :));
+
+figure;
+tiledlayout(1, 2, 'TileSpacing', 'Normal', 'Padding', 'None');
+
+ax1 = nexttile;
+hold on;
+plot(R_dist.Time, squeeze(R_dist.Data(1, 1, :)));
+xlabel('Time [s]'); ylabel('Amplitude [m,rad]');
+
+ax2 = nexttile;
+hold on;
+win = hanning(floor(length(squeeze(R_dist.Data(1, 1, :)))/8));
+[pxx, f] = pwelch(squeeze(R_dist.Data(1, 1, :)), win, 0, [], 1/Ts);
+plot(f, sqrt(pxx))
+hold off;
+xlabel('Frequency [Hz]'); ylabel('Amplitude Spectral Density [$m/\sqrt{Hz}$]');
+set(gca, 'xscale', 'log'); set(gca, 'yscale', 'log');
+xlim([1, 1e3]); ylim([1e-10, 1e0]);
+
+%%
+save('sim_data/ref_dist.mat', 'R_dist');
+

matlab/init_simulation.m

@@ -7,18 +7,24 @@ s = zpk('s');
 addpath('./src/');
 
 %% Simulation configuration
-Fs = 10e3; % Sampling Frequency [Hz]
+Fs = 1e4; % Sampling Frequency [Hz]
 Ts = 1/Fs; % Sampling Time [s]
 
 %% Data record configuration
 Trec_start = 5;  % Start time for Recording [s]
-Trec_dur   = 60; % Recording Duration [s]
+Trec_dur   = 100; % Recording Duration [s]
 
 Tsim = 2*Trec_start + Trec_dur;  % Simulation Time [s]
 
 %% Security
-u_min = -1;
-u_max = 7.5;
+% u_min = -1;
+% u_max = 7.5;
+
+u_min = 1.25;
+u_max = 5.25;
+
+d_min = -50e-6;
+d_max =  50e-6;
 
 %% Shaped Noise
 V_noise = generateShapedNoise('Ts', 1/Fs, ...
@@ -46,12 +52,12 @@ V_sweep = generateSweepExc('Ts',      Ts, ...
     'V_exc', G_sweep*1/(1 + s/2/pi/500));
 
 V_sweep_lf = generateSweepExc('Ts',      Ts, ...
-    'f_start', 0.1, ...
-    'f_end',   10, ...
+    'f_start', 20, ...
+    'f_end',   40, ...
     'V_mean',  0, ...
     't_start', Trec_start, ...
     'exc_duration', Trec_dur, ...
-    'sweep_type',   'log', ...
+    'sweep_type',   'lin', ...
     'V_exc', 0.2);
 
 %% High Frequency Shaped Noise
@@ -66,8 +72,8 @@ V_noise_hf = generateShapedNoise('Ts', Ts, ...
     'V_exc', wL);
 
 %% Low Frequency Shaped Noise
-[b,a] = cheby1(10, 2, 2*pi*[10 260], 'bandpass', 's');
-wL = 0.005*tf(b, a);
+[b,a] = cheby1(10, 2, 2*pi*[20 40], 'bandpass', 's');
+wL = 0.01*tf(b, a);
 
 V_noise_lf = generateShapedNoise('Ts', 1/Fs, ...
     'V_mean', 0, ...
@@ -76,6 +82,17 @@ V_noise_lf = generateShapedNoise('Ts', 1/Fs, ...
     'smooth_ends', true, ...
     'V_exc', wL);
 
+%% Band Pass Shaped Noise
+[b,a] = cheby1(10, 2, 2*pi*[9 1.1e3], 'bandpass', 's');
+wL = 0.005*tf(b, a);
+
+V_noise_bf = generateShapedNoise('Ts', 1/Fs, ...
+    'V_mean', 0, ...
+    't_start', Trec_start, ...
+    'exc_duration', Trec_dur, ...
+    'smooth_ends', true, ...
+    'V_exc', wL);
+
 %% Sinus excitation with increasing amplitude
 V_sin = generateSinIncreasingAmpl('Ts', 1/Fs, ...
     'V_mean', 0, ...
@@ -102,7 +119,7 @@ V_zero_off = generateShapedNoise('Ts', 1/Fs, ...
     'V_exc', tf(0));
 
 %% Select the excitation signal and offset voltage
-V_exc = timeseries(V_zero(2,:), V_zero(1,:));
+V_exc = timeseries(V_noise_bf(2,:), V_noise_bf(1,:));
 V_off = timeseries(V_zero_off(2,:), V_zero_off(1,:));
 
 %% Plot
@@ -110,7 +127,11 @@ figure;
 tiledlayout(1, 2, 'TileSpacing', 'Normal', 'Padding', 'None');
 
 ax1 = nexttile;
+hold on;
 plot(V_exc.Time, squeeze(V_exc.Data+V_off.Data));
+plot([V_exc.Time(1), V_exc.Time(end)], [u_min, u_min], 'k--');
+plot([V_exc.Time(1), V_exc.Time(end)], [u_max, u_max], 'k--');
+hold off;
 xlabel('Time [s]'); ylabel('Amplitude [V]');
 
 ax2 = nexttile;
@@ -125,4 +146,5 @@ xlim([1, Fs/2]); ylim([1e-10, 1e0]);
 save('sim_data/data_sim.mat', ...
     'Fs', 'Ts', 'Tsim', 'Trec_start', 'Trec_dur', ...
     'V_exc', 'V_off', ...
-    'u_min', 'u_max');
+    'u_min', 'u_max', ...
+    'd_min', 'd_max');

matlab/run_batch_id_S_T.m

@@ -0,0 +1,54 @@
+%% First make sure the model is open, and we are connected to the
+% Speedgoat Machine.
+
+labels = {'Dx', 'Dy', 'Dz', 'Rx', 'Ry', 'Rz'};
+
+%% Run Multiple Simulations
+my_model = 'iff_measure';
+tg = slrt;
+
+%% For each strut
+for i_dir = 1:6
+    %% Get excitation strut
+    p = Simulink.Mask.get([my_model, '/Subsystem5']);
+    p.Parameters.Value = sprintf('%i', i_dir);
+    
+    %% Connect
+    set_param(my_model,'SimulationCommand','connect')
+
+    %% Run the simulation
+    sprintf('Start measurement in %s', labels{i_dir})
+    set_param(my_model,'SimulationCommand','start')
+    
+    %% Wait for the simulation to finish
+    pause(1)
+    while strcmp(get_param(my_model,'SimulationStatus'), 'external')
+        pause(1)
+    end
+    sprintf('Finished excitation for %s', labels{i_dir})
+
+    %% Disconnect
+    set_param(my_model,'SimulationCommand','disconnect')
+
+    %% Save the data
+    f = SimulinkRealTime.openFTP(tg);
+    mget(f, 'data/data.dat');
+    close(f);
+    data = SimulinkRealTime.utils.getFileScopeData('data/data.dat').data;
+
+    de = data(:, 1:6);   % Measurment displacement (encoder) [m]
+    Vs = data(:, 7:12);  % Force Sensor [V]
+    u  = data(:, 13:18); % Control Output [V]
+    Va = data(:, 19);    % Excitation Signal [V]
+    Rx = data(:, 20:25); % Reference Signal [m/rad]
+    t  = data(:, end);   % Time [s]
+    
+    load('sim_data/data_sim.mat', 'Ts'); % To save Sampling Period
+    load('sim_data/Kiff.mat', 'Kiff'); % To save Controller
+    load('sim_data/Khac_iff_struts.mat', 'Khac_iff_struts'); % To save Controller
+    
+    save(sprintf('mat/T_S_meas_%s_2m_hac_svd_iff.mat', labels{i_dir}), ...
+        't', 'Ts', 'de', 'Vs', 'u', 'Va', 'Kiff', 'Khac_iff_struts');
+
+    sprintf('Saved Data for strut %s', labels{i_dir})
+end

matlab/run_batch_identification.m

@@ -10,9 +10,11 @@ for i_leg = 1:6
     %% Get excitation strut
     p = Simulink.Mask.get([my_model, '/Subsystem7']);
     p.Parameters.Value = sprintf('%i', i_leg);
+    pause(0.1);
     
     %% Connect
-    set_param(my_model,'SimulationCommand','connect')
+    set_param(my_model,'SimulationCommand','connect');
+    pause(0.1);
 
     %% Run the simulation
     sprintf('Start excitation for strut %i', i_leg)
@@ -23,10 +25,11 @@ for i_leg = 1:6
     while strcmp(get_param(my_model,'SimulationStatus'), 'external')
         pause(1)
     end
-    sprintf('Finished excitation for strut %i', i_leg)
+    sprintf('Finished excitation for strut %i', i_leg);
 
     %% Disconnect
-    set_param(my_model,'SimulationCommand','disconnect')
+    set_param(my_model,'SimulationCommand','disconnect');
+    pause(0.1);
 
     %% Save the data
     f = SimulinkRealTime.openFTP(tg);
@@ -44,8 +47,9 @@ for i_leg = 1:6
     load('sim_data/data_sim.mat', 'Ts'); % To save Sampling Period
     save('sim_data/Kiff.mat', 'Kiff'); % To save Controller
     
-    save(sprintf('mat/frf_data_exc_strut_%i_iff_vib_table_0m.mat', i_leg), ...
+    save(sprintf('mat/frf_data_exc_strut_%i_spindle_3m_iff_60rpm.mat', i_leg), ...
         't', 'Ts', 'de', 'Vs', 'u', 'Va', 'Kiff');
 
     sprintf('Saved Data for strut %i', i_leg)
+    pause(0.1);
 end

ref.bib

@@ -19,29 +19,31 @@
   school          = {University of Twente},
   title           = {An Exploration of Active Hard Mount Vibration Isolation for
                   Precision Equipment},
-  url             = {https://doi.org/10.3990/1.9789036530163},
   year            = 2010,
 }
 
 @book{indri20_mechat_robot,
-  author = {Indri, Marina and Oboe, Roberto},
-  title = {Mechatronics and Robotics: New Trends and Challenges},
-  year = {2020},
-  publisher = {CRC Press},
+  author          = {Indri, Marina and Oboe, Roberto},
+  title           = {Mechatronics and Robotics: New Trends and Challenges},
+  year            = 2020,
+  publisher       = {CRC Press},
 }
 
 @book{skogestad07_multiv_feedb_contr,
-  author = {Skogestad, Sigurd and Postlethwaite, Ian},
-  title = {Multivariable Feedback Control: Analysis and Design - Second Edition},
-  year = {2007},
-  publisher = {John Wiley},
-  isbn = {978-0470011683},
-  keywords = {favorite},
+  author          = {Skogestad, Sigurd and Postlethwaite, Ian},
+  title           = {Multivariable Feedback Control: Analysis and Design -
+                  Second Edition},
+  year            = 2007,
+  publisher       = {John Wiley},
+  isbn            = {978-0470011683},
+  keywords        = {favorite},
 }
 
 @article{oomen15_ident_robus_contr_compl_system,
-  author = {Oomen, Tom and Steinbuch, Maarten},
-  title = {Identification for Robust Control of Complex Systems: Algorithm and Motion Application},
-  journal = {Control-oriented modelling and identification: theory and applications, IET},
-  year = {2015},
+  author          = {Oomen, Tom and Steinbuch, Maarten},
+  title           = {Identification for Robust Control of Complex Systems:
+                  Algorithm and Motion Application},
+  journal         = {Control-oriented modelling and identification: theory and
+                  applications, IET},
+  year            = 2015,
 }