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Control Signal Editor with Simulink Real Time

/ 2024-08-20

Hello MathWorks community,

I’m currently working on my Speedgoat RealTime target, and I would like to perform Data Replay, using the block Signal Editor.
My scenario is well configured, and the simulation works pretty well, but currently the data are generated by the Signal Editor as soon the simulation Start. However I need before to wake-up the DUT and let him correctly initialize …
We can imagine to use a Switch bloc with 2 differents bus with the data comming from the Signal Editor, and from the plant model to allow the DUT to correctly init. Unfortunalty, because the Signal Editor is already sending the data, I cannot perform the replay testing as expected.

Is there an option for controlling the Signal Editor (such as start/stop/loop) with Simulink RealTime during the Simulation ?

Thank you for your advice !
RegardsHello MathWorks community,

Is there an option for controlling the Signal Editor (such as start/stop/loop) with Simulink RealTime during the Simulation ?

Thank you for your advice !
Regards Hello MathWorks community,

Is there an option for controlling the Signal Editor (such as start/stop/loop) with Simulink RealTime during the Simulation ?

Thank you for your advice !
Regards signal editor, simulink realtime, speedgoat MATLAB Answers — New Questions

Matlab

Find the combination that maximize the objective function values from the data set.

/ 2024-08-20

Hello,
I have a written a small piece of code to find the unique combinations from the excel file which has data from my objective optimzation problem.
The file has six columns other hidden columns are not important. The first column in the green shows the objective value, the second and third column with blue indicates the constraint outputs, and fourth, fifth and sixth column with the yellow represents the combinations that are maximizing my objective function.
I have to choose the maximum (green) value from first column each time in the iteration, which will then select the constraint values (blue) from second and third column, whatever they may be. Additionally, each time, it should select a unique combination (yellow) from fourth fifth and sixth column different from the previous combination that maximized the value. No combination value in column should be repeated like if in first iteration it selected [1 8 1] then in next it should [2 6 2] or [3 7 1] or [4 6 5] but every time based on the maximum value it should have unique combination which is maximizing the objective.
My code snippet is here as I am not able to figure out how to choose unique value in every column.
filename = ‘Objective Optimization MATLAB.xlsx’;
data = readtable(filename);

% Initialize variables
previous_combination = [0 0 0];

% Number of iterations (adjust as needed)
num_iterations = 10;

for i = 1:num_iterations
% Find the maximum objective value
[max_value, max_index] = max(data{:, 1});

% Get the corresponding constraint values and combination
constraint_value1 = data{max_index, 2};
constraint_value2 = data{max_index, 3};
combination = data{max_index, 4:6};

% Check if the combination is unique in each column
while ~isempty(previous_combination) && any(combination == previous_combination)
% Remove the current maximum value to find the next one
data{max_index, 1} = -inf;
[max_value, max_index] = max(data{:, 1});
constraint_value1 = data{max_index, 2};
constraint_value2 = data{max_index, 3};
combination = data{max_index, 4:6};
end

% Ensure no two column values are identical
while length(unique(combination)) < length(combination)
% Remove the current maximum value to find the next one
data{max_index, 1} = -inf;
[max_value, max_index] = max(data{:, 1});
constraint_value1 = data{max_index, 2};
constraint_value2 = data{max_index, 3};
combination = data{max_index, 4:6};
end

% Store the combination to avoid repetition
previous_combination = combination;

% Display the results
fprintf(‘Iteration %d:n’, i);
fprintf(‘Max Objective Value: %fn’, max_value);
fprintf(‘Constraint Value 1: %fn’, constraint_value1);
fprintf(‘Constraint Value 2: %fn’, constraint_value2);
fprintf(‘Combination: %snn’, mat2str(combination));
uniquecombinations(i,:) = combination;
% Remove the current maximum value to find the next one in the next iteration
data{max_index, 1} = -inf;
end
% Getting these combinations

1 8 2
3 7 1
1 3 2
3 6 1
1 2 3
3 5 1
7 8 1
3 2 4
3 1 2
4 8 1
% Since in first column 3 and 1 are repeated
% In 2nd column 8 is repeated
% In 3rd column 2 and 1 are repeated.Hello,
I have a written a small piece of code to find the unique combinations from the excel file which has data from my objective optimzation problem.
The file has six columns other hidden columns are not important. The first column in the green shows the objective value, the second and third column with blue indicates the constraint outputs, and fourth, fifth and sixth column with the yellow represents the combinations that are maximizing my objective function.
I have to choose the maximum (green) value from first column each time in the iteration, which will then select the constraint values (blue) from second and third column, whatever they may be. Additionally, each time, it should select a unique combination (yellow) from fourth fifth and sixth column different from the previous combination that maximized the value. No combination value in column should be repeated like if in first iteration it selected [1 8 1] then in next it should [2 6 2] or [3 7 1] or [4 6 5] but every time based on the maximum value it should have unique combination which is maximizing the objective.
My code snippet is here as I am not able to figure out how to choose unique value in every column.
filename = ‘Objective Optimization MATLAB.xlsx’;
data = readtable(filename);

% Initialize variables
previous_combination = [0 0 0];

% Number of iterations (adjust as needed)
num_iterations = 10;

for i = 1:num_iterations
% Find the maximum objective value
[max_value, max_index] = max(data{:, 1});

% Get the corresponding constraint values and combination
constraint_value1 = data{max_index, 2};
constraint_value2 = data{max_index, 3};
combination = data{max_index, 4:6};

% Store the combination to avoid repetition
previous_combination = combination;

1 8 2
3 7 1
1 3 2
3 6 1
1 2 3
3 5 1
7 8 1
3 2 4
3 1 2
4 8 1
% Since in first column 3 and 1 are repeated
% In 2nd column 8 is repeated
% In 3rd column 2 and 1 are repeated. Hello,
I have a written a small piece of code to find the unique combinations from the excel file which has data from my objective optimzation problem.
The file has six columns other hidden columns are not important. The first column in the green shows the objective value, the second and third column with blue indicates the constraint outputs, and fourth, fifth and sixth column with the yellow represents the combinations that are maximizing my objective function.
I have to choose the maximum (green) value from first column each time in the iteration, which will then select the constraint values (blue) from second and third column, whatever they may be. Additionally, each time, it should select a unique combination (yellow) from fourth fifth and sixth column different from the previous combination that maximized the value. No combination value in column should be repeated like if in first iteration it selected [1 8 1] then in next it should [2 6 2] or [3 7 1] or [4 6 5] but every time based on the maximum value it should have unique combination which is maximizing the objective.
My code snippet is here as I am not able to figure out how to choose unique value in every column.
filename = ‘Objective Optimization MATLAB.xlsx’;
data = readtable(filename);

% Initialize variables
previous_combination = [0 0 0];

% Number of iterations (adjust as needed)
num_iterations = 10;

for i = 1:num_iterations
% Find the maximum objective value
[max_value, max_index] = max(data{:, 1});

% Get the corresponding constraint values and combination
constraint_value1 = data{max_index, 2};
constraint_value2 = data{max_index, 3};
combination = data{max_index, 4:6};

% Store the combination to avoid repetition
previous_combination = combination;

1 8 2
3 7 1
1 3 2
3 6 1
1 2 3
3 5 1
7 8 1
3 2 4
3 1 2
4 8 1
% Since in first column 3 and 1 are repeated
% In 2nd column 8 is repeated
% In 3rd column 2 and 1 are repeated. combination, maximizing the objective MATLAB Answers — New Questions

Matlab

What the phase C values are not coming Rpeak = 90% and Rss = 58% while other phase values are correct. Kindly explain is their any logical error in the code

/ 2024-08-20

What the phase C values are not coming Rpeak = 90% and Rss = 58% while other phase values are correct. Kindly explain is their any logical error in the code. Thanks
% Define parameters
Kf_Max = 100; % maximum forward rate
RLC = [0.01, 1, 10,0.01]; % RLC values
TauKf_ON = -0.01; % TauKf_ON
TauKf_OFF = -0.01; % TauKf_OFF
Kb_Max = 80; % maximum backward rate
Kb_Min = 10; % minimum backward rate
TauKb_ON = -0.01; % TauKb_ON
TauKb_OFF = -0.01; % TauKb_OFF
PhaseTimes = [0, 10, 500, 1000, 2000];% phase times (each row defines a phase)
timespan = [0, 2000]; % timespan for the simulation

% Example initial conditions for non-active and active receptor concentrations
Non_Active_Receptor_concentration = 100; % Initial non-active receptor concentration (as a vector)
Active_Receptor_concentration =0; % Initial active receptor concentration (as a vector)

[t, Non_Active_Receptor_concentration, Active_Receptor_concentration, PhaseResults, Kf_LMax, Kb] =TestCode1(Kf_Max, RLC, TauKf_ON, …
Kb_Max, Kb_Min, TauKb_ON, PhaseTimes, timespan, Non_Active_Receptor_concentration, Active_Receptor_concentration);

function [t, Non_Active_Receptor_concentration, Active_Receptor_concentration, PhaseResults, Kf_LMax, Kb] =TestCode1(Kf_Max, RLC, TauKf_ON, …
Kb_Max, Kb_Min, TauKb_ON, PhaseTimes, timespan, Non_Active_Receptor_concentration, Active_Receptor_concentration)

% Define functions for forward and backward reaction rates
Kf_L = @(t) calculate_kf(t, PhaseTimes, Kf_Max, RLC, TauKf_ON);
Kb = @(t) calculate_kb(t, PhaseTimes, Kb_Max, Kb_Min, TauKb_ON, RLC);

% Ensure that Non_Active_Receptor_concentration and Active_Receptor_concentration are column vectors
Non_Active_Receptor_concentration = Non_Active_Receptor_concentration(:);
Active_Receptor_concentration = Active_Receptor_concentration(:);

% Set initial conditions
initial_conditions = [Non_Active_Receptor_concentration(1); Active_Receptor_concentration(1)];

% Set ODE options for step size
options = odeset(‘MaxStep’, 0.05, ‘RelTol’, 1e-6, ‘AbsTol’, 1e-8);

% Solve the ODE system
[t, y] = ode45(@(t, y) ode_LR(t, y, Kf_L, Kb), timespan, initial_conditions, options);

% Extract the concentrations
Non_Active_Receptor_concentration = y(:, 1);
Active_Receptor_concentration = y(:, 2);

% Calculate forward and backward reaction rates over time
Kf_values = arrayfun(Kf_L, t);
Kb_values = arrayfun(Kb, t);

% Plot active and non-active receptor concentrations
figure;
plot(t, Non_Active_Receptor_concentration, ‘r’, ‘DisplayName’, ‘Non-Active Receptor Concentration’);
hold on;
plot(t, Active_Receptor_concentration, ‘b’, ‘DisplayName’, ‘Active Receptor Concentration’);
legend;
xlabel(‘Time’);
ylabel(‘Concentration’);
title(‘Receptor Concentrations’);
hold off;

% Plot forward and backward reaction rates
figure;
plot(t, Kf_values, ‘k’, ‘DisplayName’, ‘Forward Reaction Rate’);
hold on;
plot(t, Kb_values, ‘c’, ‘DisplayName’, ‘Backward Reaction Rate’);
legend;
xlabel(‘Time’);
ylabel(‘Reaction Rate’);
title(‘Reaction Rates’);
hold off;

% Calculate phase results
PhaseResults = arrayfun(@(i) …
calculate_phase(t, Active_Receptor_concentration, PhaseTimes(:,i:i+1), RLC(i)) …
, 1:(size(PhaseTimes, 2)-1), ‘UniformOutput’, false);
PhaseResults = vertcat(PhaseResults{:}); % Concatenate results

% Calculate maximum forward reaction rate
Kf_LMax = Kf_Max * (RLC ./ (RLC + 1));

% Write Phase results to CSV
for i = 1:size(PhaseResults, 1)
PhaseTable = struct2table(PhaseResults(i))
writetable(PhaseTable, [‘Phase’, num2str(i), ‘.csv’]);
end
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function Kf_L = calculate_kf(t, PhaseTimes, Kf_Max, RLC, TauKf_ON)
Kf_LMax = Kf_Max * (RLC ./ (RLC + 1));
Kf_L = Kf_LMax(1); % Default to the first phase value

num_phases = numel(RLC);
for j = 1:num_phases
T_start = PhaseTimes(j);
T_end = PhaseTimes(j + 1);
if t >= T_start && t < T_end
if j == 1
Kf_L = Kf_LMax(j);
else
prev_end = PhaseTimes(j);
if j < num_phases
if RLC(j-1) < RLC(j) && RLC(j) > RLC(j+1)
Kf_L = Kf_LMax(j);
elseif RLC(j-1) < RLC(j) && RLC(j) <= RLC(j+1)
Kf_end = Kf_LMax(j) – (Kf_LMax(j) – Kf_LMax(j-1)) * exp(TauKf_ON * (T_end – T_start));
Kf_L = Kf_LMax(j) + (Kf_end – Kf_LMax(j-1)) * exp(TauKf_ON * (t – prev_end));
elseif RLC(j-1) > RLC(j) && RLC(j) < RLC(j+1)
Kf_end = Kf_LMax(j) – (Kf_LMax(j) – Kf_LMax(j-1)) * exp(TauKf_ON * (T_end – T_start));
Kf_L = Kf_LMax(j) + (Kf_end – Kf_LMax(j-1)) * exp(TauKf_ON * (t – prev_end));
elseif RLC(j-1) > RLC(j) && RLC(j) >= RLC(j+1)
Kf_end = Kf_LMax(j) – (Kf_LMax(j) – Kf_LMax(j-1)) * exp(TauKf_ON * (T_end – T_start));
Kf_L = Kf_LMax(j) + (Kf_end – Kf_LMax(j-1)) * exp(TauKf_ON * (t – prev_end));
end
end
end
return;
end
end
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function Kb = calculate_kb(t, PhaseTimes, Kb_Max, Kb_Min, ~, RLC)
Kb = Kb_Max; % Default to the minimum value
if all(RLC == RLC(1))
Kb = Kb_Min; % Set Kb to Kb_Min if all RLC values are equal
return;
end
for j = 1:numel(RLC)
T_start = PhaseTimes(j);
T_end = PhaseTimes(j + 1);
if t >= T_start && t < T_end
if j == 1
Kb = Kb_Max;
else
% prev_end = PhaseTimes(j);
if j < numel(RLC)
if RLC(j-1) < RLC(j) && RLC(j) > RLC(j+1)
Kb = Kb_Max ;
elseif RLC(j-1) < RLC(j) && RLC(j) <= RLC(j+1)
Kb = Kb_Max ;
elseif RLC(j-1) > RLC(j) && RLC(j) < RLC(j+1)
Kb = Kb_Max;
elseif RLC(j-1) > RLC(j) && RLC(j) >= RLC(j+1)
Kb = Kb_Max ;
end
else
if RLC(j-1) < RLC(j)
Kb = Kb_Max;

elseif RLC(j-1) > RLC(j)
Kb = Kb_Max;
end
end
end
return;
end
end
end

dNonActiveReceptor_dt = -Kf_L(t) * Non_Active_Receptor_concentration + Kb(t) * Active_Receptor_concentration;
dActiveReceptor_dt = Kf_L(t) * Non_Active_Receptor_concentration – Kb(t) * Active_Receptor_concentration;

dydt = [dNonActiveReceptor_dt; dActiveReceptor_dt];
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function result = calculate_phase(t, Active_Receptor_concentration, PhaseTimes, RLC)
T_start = PhaseTimes(:,1);
T_end = PhaseTimes(:,2);
Phase_indices = (t >= T_start & t <= T_end);
Phase_concentration = Active_Receptor_concentration(Phase_indices);
Phase_time = t(Phase_indices);

% Calculate peak and steady-state values
[Rpeak, Tpeak, peak_type] = findpeak(Phase_time, Phase_concentration);
Rss = mean(Active_Receptor_concentration(t >= (T_end – 10) & t <= T_end));

% Calculate the T50 (time to reach half of peak value)
half_peak_value = (Rss + Rpeak) / 2;
[~, idx_50_percent] = min(abs(Phase_concentration – half_peak_value));
T50 = Phase_time(idx_50_percent) – Tpeak;

% Calculate other metrics
ratio_Rpeak_Rss = Rpeak / Rss;
Delta = Rpeak – Rss;
L = RLC;

% Package results in a struct
result.Rpeak = Rpeak;
result.Rss = Rss;
result.Tpeak = Tpeak;
result.T50 = T50;
result.ratio_Rpeak_Rss = ratio_Rpeak_Rss;
result.Delta = Delta;
result.L = L;
result.peak_type = peak_type;
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [Rpeak, Tpeak, peak_type] = findpeak(time, concentration)
% Compute the derivative
dt = diff(time);
dy = diff(concentration);
derivative = dy ./ dt;

% Find zero-crossings of the derivative
zero_crossings = find(diff(sign(derivative)) ~= 0);

% Initialize output variables
Rpeak = NaN;
Tpeak = NaN;
peak_type = ‘None’;

% Check if there are zero crossings
if ~isempty(zero_crossings)
% Identify peaks and troughs by examining the sign changes
max_indices = zero_crossings(derivative(zero_crossings) > 0 & derivative(zero_crossings + 1) < 0);
min_indices = zero_crossings(derivative(zero_crossings) < 0 & derivative(zero_crossings + 1) > 0);

% Check if there are maxima or minima
if ~isempty(max_indices) || ~isempty(min_indices)
if ~isempty(max_indices) && ~isempty(min_indices)
% Find the highest maximum
[Rpeak_max, maxIdx] = max(concentration(max_indices));
% Find the lowest minimum
[Rpeak_min, minIdx] = min(concentration(min_indices));

% Determine whether the highest maximum is greater or the lowest minimum is smaller
if Rpeak_max >= abs(Rpeak_min)
Rpeak = Rpeak_max;
Tpeak = time(max_indices(maxIdx));
peak_type = ‘High’;
else
Rpeak = Rpeak_min;
Tpeak = time(min_indices(minIdx));
peak_type = ‘Low’;
end
% If only maxima exist
elseif ~isempty(max_indices)
[Rpeak, maxIdx] = max(concentration(max_indices));
Tpeak = time(max_indices(maxIdx));
peak_type = ‘High’;
% If only minima exist
elseif ~isempty(min_indices)
[Rpeak, minIdx] = min(concentration(min_indices));
Tpeak = time(min_indices(minIdx));
peak_type = ‘Low’;
end
end
end
endWhat the phase C values are not coming Rpeak = 90% and Rss = 58% while other phase values are correct. Kindly explain is their any logical error in the code. Thanks
% Define parameters
Kf_Max = 100; % maximum forward rate
RLC = [0.01, 1, 10,0.01]; % RLC values
TauKf_ON = -0.01; % TauKf_ON
TauKf_OFF = -0.01; % TauKf_OFF
Kb_Max = 80; % maximum backward rate
Kb_Min = 10; % minimum backward rate
TauKb_ON = -0.01; % TauKb_ON
TauKb_OFF = -0.01; % TauKb_OFF
PhaseTimes = [0, 10, 500, 1000, 2000];% phase times (each row defines a phase)
timespan = [0, 2000]; % timespan for the simulation