thesis/tesi_st.m

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3.6 KiB
Matlab
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2024-10-06 16:03:19 +02:00
clc
clear all
close all
% options
ROBOT = 'unicycle'
%TESTS = ["straightline/chill", "straightline/chill_errortheta_pisixths", "straightline/toofast", "straightline/chill_errory", "circle/start_center", "figure8/chill", "figure8/toofast", "square"]
TESTS = ["figure8/chill"]
CONTROLLER = 3
% main
s_ = size(TESTS);
for i = 1:length(TESTS)
clearvars -except i s_ TESTS ROBOT CONTROLLER
close all
% load simulation parameters common to all robots and all tests
sim_data = load(["tests/robot_common.mat"]);
TEST = convertStringsToChars(TESTS(i))
% load test data (trajectory, etc)
test_data = load(['tests/' TEST '/common.mat']);
for fn = fieldnames(test_data)'
sim_data.(fn{1}) = test_data.(fn{1});
end
% set trajectory and starting conditions
sim_data.q0 = set_initial_conditions(sim_data.INITIAL_CONDITIONS);
[ref dref] = set_trajectory(sim_data.TRAJECTORY, sim_data);
sim_data.ref = ref;
sim_data.dref = dref;
% spawn a new worker for each contzroller
% 1: track only
% 2: track + 1step
% 3: track + multistep
% load controller-specific options
data = load(['tests/' num2str(CONTROLLER) '.mat']);
for fn = fieldnames(data)'
sim_data.(fn{1}) = data.(fn{1});
end
% load robot-specific options
% put here to overwrite any parameter value left over in the tests
% .mat files, just in case
data = load(['tests/' ROBOT '.mat']);
for fn = fieldnames(data)'
sim_data.(fn{1}) = data.(fn{1});
end
% initialize prediction horizon
sim_data.U_corr_history = zeros(2,1,sim_data.PREDICTION_HORIZON);
sim_data
% simulate robot
tic;
[t, q, y, ref_t, U, U_track, Q_pred, Prob] = simulate_discr(sim_data);
toc;
disp('Done')
% save simulation data
f1 = [ TEST '/' char(datetime, 'dd-MM-yyyy-HH-mm-ss')]; % windows compatible name
f = ['results-' ROBOT '-costfun2-st/' f1];
mkdir(f)
% save workspace
dsave([f '/workspace_composite.mat']);
% save test file
copyfile(['tests/' TEST], f);
% save figures + plot results
% plot results
h = figure('Name', [TEST ' ' num2str(CONTROLLER)] );
plot_results(t, q, ref_t, U, U_track, U_track);
% save figures
savefig(h, [f '/figure.fig']);
end
%% FUNCTION DECLARATIONS
% Discrete-time simulation
function [t, q, y, ref_t, U, U_track, Q_pred, Prob] = simulate_discr(sim_data)
tc = sim_data.tc;
steps = sim_data.tfin/tc
q = sim_data.q0';
t = 0;
Q_pred = zeros(sim_data.PREDICTION_HORIZON,3, steps + 1);
Prob = cell(steps+1, 1);
[u_discr, u_track, q_pred, prob] = control_act(t(end), q(end, :), sim_data);
U = u_discr';
U_track = u_track';
Q_pred(:, :, 1) = q_pred;
Prob{1} = prob;
if eq(sim_data.robot, 0)
fun = @(t, q, u_discr, sim_data) unicycle(t, q, u_discr, sim_data);
elseif eq(sim_data.robot, 1)
fun = @(t, q, u_discr, sim_data) diffdrive(t, q, u_discr, sim_data);
end
for n = 1:steps
tspan = [(n-1)*tc n*tc];
z0 = q(end, :);
opt = odeset('MaxStep', 0.005);
[v, z] = ode45(@(v, z) fun(v, z, u_discr, sim_data), tspan, z0, opt);
q = [q; z];
t = [t; v];
[u_discr, u_track, q_pred, prob] = control_act(t(end), q(end, :), sim_data);
Prob{1+n} = prob;
U = [U; ones(length(v), 1)*u_discr'];
U_track = [U_track; ones(length(v), 1)*u_track'];
Q_pred(:, :, 1+n) = q_pred;
end
y1 = q(:, 1) + sim_data.b * cos(q(:,3));
y2 = q(:, 2) + sim_data.b * sin(q(:,3));
y = [y1, y2];
ref_t = double(subs(sim_data.ref, t'))';
end
%%