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MATLAB/Simulink code update

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Radu C. Martin 2021-06-02 10:43:38 +02:00
parent d2179071db
commit d6b69acb17
28 changed files with 956 additions and 266 deletions
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Simulink/gp_mpc_system.m
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@ -133,14 +133,17 @@ classdef gp_mpc_system < matlab.System & matlab.system.mixin.Propagates
real_p = vertcat(vec(DM(w)), vec(DM([obj.u_lags obj.y_lags])));
disp("Starting optimization")
tic
%res = obj.casadi_solver('p', real_p, 'ubx', obj.ubx, 'lbx', obj.lbx);
res = obj.casadi_solver('p', real_p, 'ubx', obj.ubx, 'lbx', obj.lbx);
t = toc;
disp(t)
u = obj.Pel * full(res.x(1));
u = 15000 * (20 - x);
% Update the u lags
obj.u_lags = [u, obj.u_lags(2:end-1)];
end
function resetImpl(obj)

98
Simulink/model_identification.m
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@ -3,74 +3,58 @@ close all
clc
%%%%%%%%%%%%%%%%%%%%%%%
%% Load the experimental data
exp_id = "Exp1";
exp_path = strcat("../Data/Luca_experimental_data/", exp_id,".mat");
wdb_path = strcat("../Data/Experimental_data_WDB/", exp_id, "_WDB.mat");
Exp_data = load(exp_path);
load(wdb_path);
% Save the current WDB to the Simulink model import (since Carnot's input file is hardcoded)
save('../Data/input_WDB.mat', 'Exp_WDB');
tin = Exp_WDB(:,1);
% The power trick: when the setpoint is larger than the actual temperature
% the HVAC system is heating the room, otherwise it is cooling the room
Setpoint = Exp_data.(exp_id).Setpoint.values;
InsideTemp = mean([Exp_data.(exp_id).InsideTemp.values, Exp_data.(exp_id).LakeTemp.values], 2);
OutsideTemp = Exp_data.(exp_id).OutsideTemp.values;
HVAC_COP = 3;
Heating_coeff = sign(Setpoint - InsideTemp);
Heating_coeff(Heating_coeff == -1) = -1 * HVAC_COP;
%% Set the run parameters
air_exchange_rate = tin;
air_exchange_rate(:,2) = 1.0;
% Set the initial temperature to be the measured initial temperature
t0 = Exp_data.(exp_id).InsideTemp.values(1);
t0 = 23;
power = Exp_data.(exp_id).Power.values - 1.67 * 1000;
runtime1 = 161400;
runtime2 = 136200;
runtime3 = 208200;
runtime4 = 208200;
runtime5 = 208200;
runtime6 = 208200;
runtime7 = 553800;
power = [tin Heating_coeff .* power];
runtime = 24 * 3600;
set_param('polydome', 'StopTime', int2str(runtime))
Tsample = 900;
steps = runtime/Tsample;
tin = Tsample *(0:steps)';
prbs_sig = 2*prbs(8, steps+1)' - 1;
COP = 5.0;
Pel = 6300;
% Turn down the air exchange rate when the HVAC is not running
night_air_exchange_rate = 0.5;
air_exchange_rate(abs(power(:, 2)) < 100, 2) = night_air_exchange_rate;
power = [tin COP*Pel*prbs_sig(1:steps+1)];
%% Run the simulation
% Note: The simlulink model loads the data separately, includes the
% calculated solar position and radiations from pvlib
load_system("polydome");
set_param('polydome', 'StopTime', int2str(tin(end)));
simout = sim("polydome");
%% Simulate the model
out = sim('polydome');
SimulatedTemp = simout.SimulatedTemp;
%% Compare the simulation results with the measured values
figure; hold on; grid minor;
plot(tin, InsideTemp);
plot(tin, OutsideTemp);
plot(SimulatedTemp, 'LineWidth', 2);
legend('InsideTemp', 'OutsideTemp', 'SimulatedTemp');
%% For manual simulation running
WeatherMeasurement = struct;
WeatherMeasurement.data = squeeze(out.WeatherMeasurement.data)';
WeatherMeasurement.time = out.WeatherMeasurement.time;
input = [power(:, 2:end) WeatherMeasurement.data];
x0=500;
y0=300;
width=1500;
height=500;
set(gcf,'position',[x0,y0,width,height]);
title(exp_id);
%title(sprintf('Night Air exchange rate %f', night_air_exchange_rate));
Exp7_data = iddata(out.SimulatedTemp.data, input);
hold off;
Exp7_table = array2table([input out.SimulatedTemp.data], 'VariableNames', {'Power', 'SolRad', 'OutsideTemp', 'SimulatedTemp'});
saveas(gcf, strcat(exp_id, '_simulation'), 'svg')
writetable(Exp7_table, 'Exp7_table.csv')
%% Export simulated temperature to a .mat file for further use
carnot_output_dir = strcat("../Data/CARNOT_output/",exp_id,"_carnot_temp.mat");
save(carnot_output_dir, 'SimulatedTemp');
%%
save('Exp_CARNOT.mat', ...
'Exp1_data', 'Exp1_table', ...
'Exp2_data', 'Exp2_table', ...
'Exp3_data', 'Exp3_table', ...
'Exp4_data', 'Exp4_table', ...
'Exp5_data', 'Exp5_table', ...
'Exp6_data', 'Exp6_table', ...
'Exp7_data', 'Exp7_table' ...
)
data_train = merge(Exp1_data, Exp3_data, Exp5_data);
data_test = merge(Exp2_data, Exp4_data, Exp6_data, Exp7_data);

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Simulink/mpc_simulink/casadi_block.m
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@ -1,177 +0,0 @@
classdef casadi_block < matlab.System & matlab.system.mixin.Propagates
% untitled Add summary here
%
% This template includes the minimum set of functions required
% to define a System object with discrete state.
properties
% Public, tunable properties.
end
properties (DiscreteState)
end
properties (Access = private)
% Pre-computed constants.
casadi_solver
x0
lbx
ubx
lbg
ubg
end
methods (Access = protected)
function num = getNumInputsImpl(~)
num = 2;
end
function num = getNumOutputsImpl(~)
num = 1;
end
function dt1 = getOutputDataTypeImpl(~)
dt1 = 'double';
end
function dt1 = getInputDataTypeImpl(~)
dt1 = 'double';
end
function sz1 = getOutputSizeImpl(~)
sz1 = [1,1];
end
function sz1 = getInputSizeImpl(~)
sz1 = [1,1];
end
function cp1 = isInputComplexImpl(~)
cp1 = false;
end
function cp1 = isOutputComplexImpl(~)
cp1 = false;
end
function fz1 = isInputFixedSizeImpl(~)
fz1 = true;
end
function fz1 = isOutputFixedSizeImpl(~)
fz1 = true;
end
function setupImpl(obj,~,~)
% Implement tasks that need to be performed only once,
% such as pre-computed constants.
import casadi.*
T = 10; % Time horizon
N = 20; % number of control intervals
% Declare model variables
x1 = SX.sym('x1');
x2 = SX.sym('x2');
x = [x1; x2];
u = SX.sym('u');
% Model equations
xdot = [(1-x2^2)*x1 - x2 + u; x1];
% Objective term
L = x1^2 + x2^2 + u^2;
% Continuous time dynamics
f = casadi.Function('f', {x, u}, {xdot, L});
% Formulate discrete time dynamics
% Fixed step Runge-Kutta 4 integrator
M = 4; % RK4 steps per interval
DT = T/N/M;
f = Function('f', {x, u}, {xdot, L});
X0 = MX.sym('X0', 2);
U = MX.sym('U');
X = X0;
Q = 0;
for j=1:M
[k1, k1_q] = f(X, U);
[k2, k2_q] = f(X + DT/2 * k1, U);
[k3, k3_q] = f(X + DT/2 * k2, U);
[k4, k4_q] = f(X + DT * k3, U);
X=X+DT/6*(k1 +2*k2 +2*k3 +k4);
Q = Q + DT/6*(k1_q + 2*k2_q + 2*k3_q + k4_q);
end
F = Function('F', {X0, U}, {X, Q}, {'x0','p'}, {'xf', 'qf'});
% Start with an empty NLP
w={};
w0 = [];
lbw = [];
ubw = [];
J = 0;
g={};
lbg = [];
ubg = [];
% "Lift" initial conditions
X0 = MX.sym('X0', 2);
w = {w{:}, X0};
lbw = [lbw; 0; 1];
ubw = [ubw; 0; 1];
w0 = [w0; 0; 1];
% Formulate the NLP
Xk = X0;
for k=0:N-1
% New NLP variable for the control
Uk = MX.sym(['U_' num2str(k)]);
w = {w{:}, Uk};
lbw = [lbw; -1];
ubw = [ubw; 1];
w0 = [w0; 0];
% Integrate till the end of the interval
Fk = F('x0', Xk, 'p', Uk);
Xk_end = Fk.xf;
J=J+Fk.qf;
% New NLP variable for state at end of interval
Xk = MX.sym(['X_' num2str(k+1)], 2);
w = {w{:}, Xk};
lbw = [lbw; -0.25; -inf];
ubw = [ubw; inf; inf];
w0 = [w0; 0; 0];
% Add equality constraint
g = {g{:}, Xk_end-Xk};
lbg = [lbg; 0; 0];
ubg = [ubg; 0; 0];
end
% Create an NLP solver
prob = struct('f', J, 'x', vertcat(w{:}), 'g', vertcat(g{:}));
options = struct('ipopt',struct('print_level',0),'print_time',false);
solver = nlpsol('solver', 'ipopt', prob, options);
obj.casadi_solver = solver;
obj.x0 = w0;
obj.lbx = lbw;
obj.ubx = ubw;
obj.lbg = lbg;
obj.ubg = ubg;
end
function u = stepImpl(obj,x,t)
disp(t)
tic
w0 = obj.x0;
lbw = obj.lbx;
ubw = obj.ubx;
solver = obj.casadi_solver;
lbw(1:2) = x;
ubw(1:2) = x;
sol = solver('x0', w0, 'lbx', lbw, 'ubx', ubw,...
'lbg', obj.lbg, 'ubg', obj.ubg);
u = full(sol.x(3));
toc
end
function resetImpl(obj)
% Initialize discrete-state properties.
end
end
end

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Simulink/test_casadi_mpc.m
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@ -1,10 +0,0 @@
clear all
close all
clc
%%%%%%%%%%%%%%%%
%% Load the existing GP
addpath("../../Gaussiandome/Identification/Computation results/")
load("Identification_Validation.mat")
load("Gaussian_Process_models.mat")

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Simulink/weather_predictor.m
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@ -29,17 +29,20 @@ classdef weather_predictor < matlab.System
num = 2;
end
function num = getNumOutputsImpl(~)
num = 1;
num = 2;
end
function dt1 = getOutputDataTypeImpl(~)
function [dt1, dt2] = getOutputDataTypeImpl(~)
dt1 = 'double';
dt2 = 'double';
end
function [dt1, dt2] = getInputDataTypeImpl(~)
dt1 = 'double';
dt2 = 'double';
end
function sz1 = getOutputSizeImpl(obj)
sz1 = [obj.N 2];
function [sz1, sz2] = getOutputSizeImpl(obj)
sz1 = [1 2];
sz2 = [obj.N 2];
end
function sz1 = getInputSizeImpl(~)
sz1 = 1;
@ -47,14 +50,16 @@ classdef weather_predictor < matlab.System
function cp1 = isInputComplexImpl(~)
cp1 = false;
end
function cp1 = isOutputComplexImpl(~)
function [cp1, cp2] = isOutputComplexImpl(~)
cp1 = false;
cp2 = false;
end
function fz1 = isInputFixedSizeImpl(~)
fz1 = true;
fz1 = true;
end
function fz1 = isOutputFixedSizeImpl(~)
function [fz1, fz2] = isOutputFixedSizeImpl(~)
fz1 = true;
fz2 = true;
end
@ -63,13 +68,29 @@ classdef weather_predictor < matlab.System
% Perform one-time calculations, such as computing constants
end
function w = stepImpl(obj,wdb_mat,timestamp)
function [w, w_hat] = stepImpl(obj,wdb_mat,timestamp)
disp(timestamp)
% Implement algorithm. Calculate y as a function of input u and
% discrete states.
curr_idx = find(wdb_mat(:, 1) == timestamp);
N_idx = (1:obj.N) + curr_idx;
w = [wdb_mat(N_idx, 18) + wdb_mat(N_idx, 19), wdb_mat(N_idx, 7)];
forecast_start = timestamp + obj.TimeStep;
forecast_end = timestamp + obj.N * obj.TimeStep;
xq = forecast_start:obj.TimeStep:forecast_end;
weather_start_idx = find(wdb_mat(:, 1) <= timestamp, 1);
weather_end_idx = find(wdb_mat(:, 1) >= forecast_end, 1);
weather_idx = weather_start_idx:weather_end_idx;
solar_direct = interp1(wdb_mat(weather_idx, 1), wdb_mat(weather_idx, 18), timestamp);
solar_diffuse = interp1(wdb_mat(weather_idx, 1), wdb_mat(weather_idx, 19), timestamp);
outside_temp = interp1(wdb_mat(weather_idx, 1), wdb_mat(weather_idx, 7), timestamp);
w = [solar_direct + solar_diffuse, outside_temp];
solar_direct = interp1(wdb_mat(weather_idx, 1), wdb_mat(weather_idx, 18), xq)';
solar_diffuse = interp1(wdb_mat(weather_idx, 1), wdb_mat(weather_idx, 19), xq)';
outside_temp = interp1(wdb_mat(weather_idx, 1), wdb_mat(weather_idx, 7), xq)';
w_hat = [solar_direct + solar_diffuse, outside_temp];
end
function resetImpl(obj)

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Simulink/weather_predictor2.m
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@ -1,8 +0,0 @@
function w = weather_predictor2(wdb_mat,timestamp, N)
%WEATHER_PREDICTOR2 Summary of this function goes here
% Detailed explanation goes here
curr_idx = find(wdb_mat(:, 1) == timestamp);
N_idx = (1:N) + curr_idx;
w = [wdb_mat(N_idx, 18) + wdb_mat(N_idx, 19), wdb_mat(N_idx, 7)];
end