general.m

clear; close all; clc;

%% Define parameters
params = struct();
params.tf = 85;
params.Ts = 0.2;
params.N = params.tf / params.Ts;
params.u0_nonlinear = -0.897;
params.u0_linear = -1.7863;

bias = -1;
span = 0.4;

%% Define input
levels = bias * ones(1, 10) + span * [0, 0.6, 1.8, -0.3, -1.5, -0.6, 0, 2.4, -2.1, 0];
spans = 1 * [5, 5, 7, 7, 7, 7, 6, 20, 12, 8];

span_steps = round(spans ./ params.Ts);
ref_signal = [];
for i = 1:length(levels)
    ref_signal = [ref_signal; repmat(levels(i), span_steps(i), 1)];
end
params.Ref = [ref_signal; repmat(levels(end), max(0, params.N - length(ref_signal)), 1)];

%% Define parameters
params.P = 11;
params.M = 8;
params.alpha = 0.5;
params.Q = 1 * ones(params.P, 1);
params.R = 0.3107 * ones(params.M, 1);
params.N_model = 55;
params.disturbance_amp = 0.2;
params.noise_power = -40;
params.disturbance_start_time = 55;
params.x0 = [-1; 0];
params.is_programmed = true;
params.is_open_loop = false;

%% Run simulations
[results_lin, sr_lin] = simulate_linear_system(params);
[results_nonlin, ~] = simulate_nonlinear_system(params);

Hankel = results_lin.F;
Toeplitz = results_lin.G;
gainMatrix = results_lin.gainMatrix;
sr_all = results_lin.sr_all;

%% Plot results
plot_simulation_results(results_lin, results_nonlin, params, true);
% plot_step_response(params, sr_all);
% plot_static_gain(-1);