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%% Clear Workspace and Close figures
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clear; close all; clc;
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%% Intialize Laplace variable
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s = zpk('s');
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%% Path for functions, data and scripts
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addpath('./src/'); % Path for scripts
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addpath('./mat/'); % Path for data
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addpath('./STEPS/'); % Path for Simscape Model
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addpath('./subsystems/'); % Path for Subsystems Simulink files
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%% Linearization options
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opts = linearizeOptions;
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opts.SampleTime = 0;
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%% Open Simscape Model
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mdl = 'detail_instrumentation_nass'; % Name of the Simulink File
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open(mdl); % Open Simscape Model
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%% Colors for the figures
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colors = colororder;
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freqs = logspace(1,4,1000); % Frequency vector [Hz]
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%% Identify the transfer functions from disturbance sources to vertical position error
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% Let's initialize all the stages with default parameters.
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initializeGround();
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initializeGranite();
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initializeTy();
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initializeRy();
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initializeRz();
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initializeMicroHexapod();
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initializeSample('m', 1);
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initializeSimplifiedNanoHexapod();
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initializeSimscapeConfiguration();
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initializeDisturbances('enable', false);
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initializeLoggingConfiguration('log', 'none');
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initializeController('type', 'hac-iff');
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initializeReferences();
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% Decentralized IFF controller
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wz = 2*pi*2;
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xiz = 0.7;
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Ghpf = (s^2/wz^2)/(s^2/wz^2 + 2*xiz*s/wz + 1);
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Kiff = -200 * ... % Gain
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1/(0.01*2*pi + s) * ... % LPF: provides integral action
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Ghpf * ... % 2nd order HPF (limit low frequency gain)
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eye(6); % Diagonal 6x6 controller (i.e. decentralized)
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% Centralized HAC
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wc = 2*pi*10; % Wanted crossover [rad/s]
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H_int = wc/s; % Integrator
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a = 2; % Amount of phase lead / width of the phase lead / high frequency gain
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H_lead = 1/sqrt(a)*(1 + s/(wc/sqrt(a)))/(1 + s/(wc*sqrt(a))); % Lead to increase phase margin
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H_lpf = 1/(1 + s/2/pi/80); % Low Pass filter to increase robustness
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Khac = -5e4 * ... % Gain
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H_int * ... % Integrator
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H_lead * ... % Low Pass filter
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H_lpf * ... % Low Pass filter
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eye(6); % 6x6 Diagonal
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% Input/Output definition
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clear io; io_i = 1;
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io(io_i) = linio([mdl, '/dac_noise'], 1, 'input'); io_i = io_i + 1; % DAC noise [V]
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io(io_i) = linio([mdl, '/amp_noise'], 1, 'input'); io_i = io_i + 1; % Voltage Amplifier noise [V]
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io(io_i) = linio([mdl, '/NASS/adc_noise'], 1, 'input'); io_i = io_i + 1; % ADC noise [V]
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io(io_i) = linio([mdl, '/NASS/enc_noise'], 1, 'input'); io_i = io_i + 1; % Encoder noise [m]
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io(io_i) = linio([mdl, '/NASS'], 2, 'output', [], 'z'); io_i = io_i + 1; % Vertical error [m]
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io(io_i) = linio([mdl, '/NASS'], 2, 'output', [], 'y'); io_i = io_i + 1; % Lateral error [m]
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Gd = linearize(mdl, io);
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Gd.InputName = {...
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'nda1', 'nda2', 'nda3', 'nda4', 'nda5', 'nda6', ... % DAC and Voltage amplifier noise
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'namp1', 'namp2', 'namp3', 'namp4', 'namp5', 'namp6', ... % DAC and Voltage amplifier noise
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'nad1', 'nad2', 'nad3', 'nad4', 'nad5', 'nad6', ... % ADC noise
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'ddL1', 'ddL2', 'ddL3', 'ddL4', 'ddL5', 'ddL6' ... % Encoder noise
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};
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Gd.OutputName = {'y', 'z'}; % Vertical error of the sample
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%% Save Requirements
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save('./mat/instrumentation_sensitivity.mat', 'Gd');
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%% Transfer function from noise sources to vertical motion errors
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freqs = logspace(0, 3, 1000);
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figure;
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tiledlayout(1, 1, 'TileSpacing', 'compact', 'Padding', 'None');
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nexttile();
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hold on;
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plot(freqs, abs(squeeze(freqresp(Gd('z', 'nda1' ), freqs, 'Hz'))), 'DisplayName', '$\epsilon_z/n_{da}$');
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plot(freqs, abs(squeeze(freqresp(Gd('z', 'namp1'), freqs, 'Hz'))), 'DisplayName', '$\epsilon_z/n_{amp}$');
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plot(freqs, abs(squeeze(freqresp(Gd('z', 'nad1' ), freqs, 'Hz'))), 'DisplayName', '$\epsilon_z/n_{ad}$');
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hold off;
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set(gca, 'XScale', 'log'); set(gca, 'YScale', 'log');
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xlabel('Frequency [Hz]'); ylabel('Sensitivity [m/V]');
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ylim([1e-9, 1e-4]);
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leg = legend('location', 'southwest', 'FontSize', 8, 'NumColumns', 1);
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leg.ItemTokenSize(1) = 15;
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xlim([1, 1e3]);
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% Maximum wanted effect of each noise source on the vertical error
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% Specifications: 15nm RMS
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% divide by sqrt(6) because 6 struts
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% divide by sqrt(3) because 3 considered noise sources
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max_asd_z = 15e-9 / sqrt(6) / sqrt(3); % [m/sqrt(Hz)]
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% Suppose unitary flat noise ASD => compute the effect on vertical noise
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unit_asd = ones(1, length(freqs));
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rms_unit_asd_dac = sqrt(sum((unit_asd.*abs(squeeze(freqresp(Gd('z', 'nda1' ), freqs, 'Hz'))).').^2));
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rms_unit_asd_amp = sqrt(sum((unit_asd.*abs(squeeze(freqresp(Gd('z', 'namp1'), freqs, 'Hz'))).').^2));
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rms_unit_asd_adc = sqrt(sum((unit_asd.*abs(squeeze(freqresp(Gd('z', 'nad1' ), freqs, 'Hz'))).').^2));
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% Obtained maximum ASD for different instruments
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max_dac_asd = max_asd_z./rms_unit_asd_dac; % [V/sqrt(Hz)]
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max_amp_asd = max_asd_z./rms_unit_asd_amp; % [V/sqrt(Hz)]
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max_adc_asd = max_asd_z./rms_unit_asd_adc; % [V/sqrt(Hz)]
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% Estimation of the equivalent RMS noise
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max_dac_rms = 1e3*max_dac_asd*sqrt(5e3) % [mV RMS]
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max_amp_rms = 1e3*max_amp_asd*sqrt(5e3) % [mV RMS]
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max_adc_rms = 1e3*max_adc_asd*sqrt(5e3) % [mV RMS]
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%% Save Requirements
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save('./mat/instrumentation_requirements.mat', ...
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'max_dac_asd', 'max_amp_asd', 'max_adc_asd', ...
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'max_dac_rms', 'max_amp_rms', 'max_adc_rms');
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