I have multiple graphs and i wand to sum all these graphs together to have a simple one. (adding them like 1+1=2)I have multiple graphs and i wand to sum all these graphs together to have a simple one. (adding them like 1+1=2) I have multiple graphs and i wand to sum all these graphs together to have a simple one. (adding them like 1+1=2) sum, graph MATLAB Answers — New Questions

​

Hi, im working on a ball on a plate system, i dont have a system model and i want to auto tune my PID controller on the basis of the data received from sensors. I want to know how to auto tune PID in matlab or if theres a way to connect sensor data with simulink in real time for auto tuning purpose. Moreover, i find it difficult to perfrom image processing to gather data in simulink. My code is shared below for further assistance.
%% Initialize Environment
clear all;
clc;
close all;
%% Initialize Data Logging
logFile = fopen(‘data_log.csv’, ‘w’); % Open a CSV file for writing
fprintf(logFile, ‘Time,Disturbance1,Disturbance2,Disturbance3,RollAngle,PitchAngle,CentroidX,CentroidY,Servo1,Servo2,Servo3n’); % Header row
stateLogFile = fopen(‘state_log.csv’, ‘w’); % Open another CSV file for writing
fprintf(stateLogFile, ‘Time,Phi,Theta,X_Offset,Y_Offset,Zp,Gamman’); % Header row

%% Initialize Image Processing
% Load the camera parameters
load(‘test.mat’); % Ensure the file ‘cameraParams.mat’ is in your working directory
% Create a webcam object
cap = webcam(‘Adesso CyberTrack H3’);
dist_1 = 0;
dist_2=dist_1
dist_3=dist_2
% Adjust camera settings if supported
try
% Set exposure time within valid range
exposureTime = -6; % Adjust as needed within the valid range
cap.ExposureMode = ‘manual’;
cap.Exposure = exposureTime;
catch ME
disp(‘Warning: Unable to set exposure time for the camera.’);
disp(ME.message);
end

%% Anomaly Variables
% Parameters for angle adjustments
p_a = 0;
p_r = 0;

%% Blob Object
% Blob analysis object for ball detection
% Blob analysis object for ball detection using vision.BlobAnalysis
blobAnalyzer = vision.BlobAnalysis(…
‘MinimumBlobArea’, 200, … % Adjust to filter small noise
‘MaximumBlobArea’, 50000, … % Adjust to ensure the ball is detected
‘BoundingBoxOutputPort’, true, …
‘CentroidOutputPort’, true, …
‘AreaOutputPort’, true);

%% Create Figure and Axes
hFig = figure(‘DoubleBuffer’, ‘on’);
hAxes = axes(‘Parent’, hFig);
s = 1;
%% Main Loop

%% Measure Sampling Time
%% Servo Motor Control Initial Position
% Port at which your Arduino is connected
port = ‘COM5’;
% Model of your Arduino board
board = ‘Uno’;
% Creating Arduino object with servo library
arduino_board = arduino(port, board, ‘Libraries’, ‘Servo’);
% Creating servo motor object
servo_motor = servo(arduino_board, ‘D9′,’MinPulseDuration’, 500e-6, ‘MaxPulseDuration’, 2500e-6);
writePosition(servo_motor, 0.333); % 45 degrees in as per calculations and in radians = 0.333
servo_motor1 = servo(arduino_board, ‘D10′,’MinPulseDuration’, 500e-6, ‘MaxPulseDuration’, 2500e-6);
writePosition(servo_motor1, 0.333);
servo_motor2 = servo(arduino_board, ‘D11′,’MinPulseDuration’, 500e-6, ‘MaxPulseDuration’, 2500e-6);
writePosition(servo_motor2, 0.333);

A= 45;
B= 45;
C = 45;
disp(‘done here1’);
h = 1;
for i = 1:2
%% Image Processing Started
start = tic;
[Zp,alpha] = forwardkinfunc(A,B,C);
% Read frame from webcam
frame = snapshot(cap);

% Undistort the frame using the camera parameters
undistortedFrame = undistortImage(frame, cameraParams2);

% Resize the undistorted frame to a lower resolution (e.g., half the original size)
undistortedFrame = imresize(undistortedFrame, 0.2); % Reduce resolution to 50%
%% Calculate the center of the image
[frameHeight, frameWidth, ~] = size(undistortedFrame);
centerX = frameWidth / 2;
centerY = frameHeight / 2;

%% Circle (Dot) Detection
% Convert to grayscale and adjust contrast
I_gray = rgb2gray(undistortedFrame);
I_contrast = imadjust(I_gray);

% Detect circles in the image (dots)
[Centers, Radii] = imfindcircles(I_contrast, [1 5], ‘ObjectPolarity’, ‘dark’, ‘Sensitivity’, 0.9);

% Check if circles are detected
if isempty(Centers)
disp(‘No circles detected’);
else
disp([‘Detected ‘, num2str(size(Centers, 1)), ‘ circles’]);
end

% Display the undistorted frame and detected circles
imshow(undistortedFrame, ‘Parent’, hAxes);
hold(hAxes, ‘on’);
viscircles(hAxes, Centers, Radii, ‘EdgeColor’, ‘r’);
% Plot a magenta dot at the center of the image
plot(hAxes, centerX, centerY, ‘m.’, ‘MarkerSize’, 20); % Magenta dot

% Check if at least 2 circles are detected for line fitting
if size(Centers, 1) >= 2
% Fit lines to X and Y coordinates separately
x = Centers(:, 1);
yof = Centers(:, 2);
pX = polyfit(yof, x, 1); % Line fit for X as a function of Y
pY = polyfit(x, yof, 1); % Line fit for Y as a function of X

% Display fitted line coefficients
disp([‘Line fit for X (Y): slope = ‘, num2str(pX(1))]);
disp([‘Line fit for Y (X): slope = ‘, num2str(pY(1))]);

% Calculate the slopes
slopeX = pX(1);
slopeY = pY(1);

% Calculate roll and pitch angles from the slopes
Theta = -atan2(slopeX, 1); % Tilt along X-axis (roll)
Phi = atan2(slopeY, 1); % Tilt along Y-axis (pitch)

% Display calculated angles
disp([‘Roll Angle: ‘, num2str(Theta)]);
disp([‘Pitch Angle: ‘, num2str(Phi)]);

% Draw the X and Y axis lines
plot(hAxes, [0, size(I_contrast, 2)], [mean(yof), mean(yof)], ‘g–‘, ‘LineWidth’, 2); % X-axis line
plot(hAxes, [mean(x), mean(x)], [0, size(I_contrast, 1)], ‘y–‘, ‘LineWidth’, 2); % Y-axis line

% Draw the slope lines
yFitLineX = polyval(pX, [0, size(I_contrast, 1)]);
plot(hAxes, yFitLineX, [0, size(I_contrast, 1)], ‘b-‘, ‘LineWidth’, 2); % Slope line for X

xFitLineY = polyval(pY, [0, size(I_contrast, 2)]);
plot(hAxes, [0, size(I_contrast, 2)], xFitLineY, ‘r-‘, ‘LineWidth’, 2); % Slope line for Y

% Display the calculated angles
title(hAxes, sprintf(‘Roll: %.2f°, Pitch: %.2f°’, Theta, Phi));
else
title(hAxes, ‘Not enough circles detected’);
end

%% Ball Detection Using vision.BlobAnalysis
% Check if any blobs are detected
%% Ball Detection Using vision.BlobAnalysis
hsvFrame = rgb2hsv(undistortedFrame);

% Define thresholds for channel 1 based on histogram settings
channel1Min = 0.845;
channel1Max = 0.081;

% Define thresholds for channel 2 based on histogram settings
channel2Min = 0.133;
channel2Max = 1.000;

% Define thresholds for channel 3 based on histogram settings
channel3Min = 0.519;
channel3Max = 1.000;

% Create a binary mask based on chosen histogram thresholds
sliderBW = ((hsvFrame(:,:,1) >= channel1Min) | (hsvFrame(:,:,1) <= channel1Max)) & …
(hsvFrame(:,:,2) >= channel2Min) & (hsvFrame(:,:,2) <= channel2Max) & …
(hsvFrame(:,:,3) >= channel3Min) & (hsvFrame(:,:,3) <= channel3Max);
BW = sliderBW;
BW = imfill(BW, ‘holes’); % Fill any small holes
BW = bwareaopen(BW, 200); % Remove small noise (adjust 200 as needed)
% Apply morphological operations to clean up the mask
BW = imopen(BW, strel(‘disk’, 5));

BW = imclose(BW, strel(‘disk’, 20));

% Perform blob analysis
[area,centroid,bbox] = step(blobAnalyzer,BW);
if ~isempty(centroid)
x_p = centroid(1);
y_p = centroid(2);

% Draw the bounding box on the undistorted frame
rectangle(‘Position’, bbox, ‘EdgeColor’, ‘green’, ‘LineWidth’, 2);
plot(hAxes, x_p, y_p, ‘ro’, ‘MarkerSize’, 10);
pix_cm = 32/frameWidth;
xc = x_p*pix_cm;
yc = y_p*pix_cm
[xof1, yof1] = camera_base(xc,yc)

end
elapsed_time = toc(start); % Compute elapsed time dynamically
Ts = getElapsedTime();% Use elapsed time for PID calculations
tic;
Kp_x = 10; Ki_x = 0; Kd_x = 0
Kp_y = 10; Ki_y = 0; Kd_y = 0;
Kp_phi = 80; Ki_phi = 0; Kd_phi = 0.5;
Kp_theta =80; Ki_theta =0; Kd_theta = 0.5;

% Setpoints (target values)
setpoint_x = -0.1;
setpoint_y = -5.5;
setpoint_phi = 0.00;
setpoint_theta = 0.00;

previous_errorx = 0;
integral_errorx=0
derivative_errorx = 0

previous_errory = 0;
integral_errory=0
derivative_errory = 0

previous_errorphi = 0;
integral_errorphi=0
derivative_errorphi = 0

previous_errortheta = 0;
integral_errortheta=0
derivative_errortheta = 0
prev_filtest = 0;
filte_est =0;
prev_filtesty = 0;
filte_esty =0;
prev_filtestp = 0;
filte_estp =0;
prev_filtestth = 0;
filte_estth =0;
o = 0.05;
K_xp = 4/100;
K_yp= 4.8/100;
K_xr = 0.63/100;
K_yr = 3.05/100;

error_x= setpoint_x – xof1;
integral_errorx = integral_errorx + error_x * Ts;
derivative_errorx = (error_x – previous_errorx);
filte_est = (o*prev_filtest)+(1-o)*derivative_errorx
derivative = filte_est/Ts;
prev_filtest = filte_est;
control_signalx = Kp_x * error_x + Ki_x * integral_errorx + Kd_x * derivative;
previous_errorx = error_x;

% Cross-coupling effect on phi due to x_error
theta_coupling_effect = K_xr * error_x;

error_y= setpoint_y – yof1;
integral_errory = integral_errory + error_y * Ts;
derivative_errory = (error_y – previous_errory)
filte_esty = (o*prev_filtesty)+(1-o)*derivative_errory
derivativey = filte_esty/Ts;
prev_filtesty = filte_esty;
control_signaly = Kp_y * error_y + Ki_y * integral_errory + Kd_y * derivativey;
previous_errory = error_y;

% Cross-coupling effect on theta due to y_error
phi_coupling_effect = K_yp * error_y;

% Step 5: Compute final tilt angles
theta_desired =(K_yr * error_y) + phi_coupling_effect; % Roll (tilt about X-axis)
phi_desired = (K_xp * error_x) + theta_coupling_effect; % Pitch (tilt about Y-axis)

error_phi= phi_desired – Phi;
integral_errorphi = integral_errorphi + error_phi * Ts;
derivative_errorphi = (error_phi – previous_errorphi)
filte_estp = (o*prev_filtestp)+(1-o)*derivative_errorphi
derivativep = filte_estp/Ts;
prev_filtestp = filte_estp;
control_signalphi = Kp_phi * error_phi + Ki_phi * integral_errorphi + Kd_phi * derivativep;
previous_errorphi = error_phi;
Phi1 = (Phi + control_signalphi * Ts); % Update tilt
Phi1 = max(-0.6, min(Phi1, 0.6));
pidTuner(Phi1)

error_theta= theta_desired – Theta;
integral_errortheta = integral_errortheta + error_theta * Ts;
derivative_errortheta = (error_theta – previous_errortheta)
filte_estth = (o*prev_filtestth)+(1-o)*derivative_errortheta;
derivativeth = filte_estth/Ts;
prev_filtestth = filte_estth;
control_signaltheta = Kp_theta * error_theta + Ki_theta * integral_errortheta + Kd_theta * derivativeth;
previous_errortheta = error_theta;
Theta1 = (Theta + control_signaltheta * Ts); % Update tilt
Theta1 = max(-0.6, min(Theta1, 0.6));
xof1 = xof1+(control_signalx*Ts);
yof1 = yof1+(control_signaly*Ts)

disp(‘done here 3’)
xo= 82*sin(Phi);
yo =-82*sin(Theta);

[a11f,a22f,a33f] = inversekin(xof1,yof1,Phi1,Theta1,Zp,alpha)
% [a11f,a22f,a33f] = inversekin(xof1,yof1,phi_control,theta_control,Zp,gamma)
a11 = a11f;
a22 = a22f;
a33 = a33f;
disp(‘done here 4’)
% Clamp the angles to the range 40° to 50°
a11 = max(42, min(a11, 48));
a22 = max(42, min(a22, 48));
a33 = max(42, min(a33, 48));
currentTime = datestr(now, ‘HH:MM:SS.FFF’); % Get current time
fprintf(logFile, ‘%s,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.4f,%.4f,%.4fn’, …
currentTime, dist_1, dist_2, dist_3, Phi, Theta, xof1, yof1,a11, a22, a33);
% fprintf(stateLogFile, ‘%.3f,%.3f,%.3f,%.3f,%.3f,%.3f,%.3fn’, Ts, Phi, Theta, xof1, yof1, Zp, gamma);

%% Servo Motor Code
% Clamp the angles to the range 40° to 50°
a = normalize(a11);
b = normalize(a22);
c = normalize(a33);

writePosition(servo_motor,a)
writePosition(servo_motor1,b)
writePosition(servo_motor2,c)
stop_it = toc(start);
A = a11;
B = a22;
C= a33;
stop = toc(start)
disp(‘done here 5’);

if (xof1 <=0.5 && xof1>=-0.5)&&(yof1 <= 1.5 &&yof1>=-1.5)
% if (xof1==0 && yof1==0)
disp(‘im done’)
writePosition(servo_motor,0.3300)
writePosition(servo_motor1,0.3300)
writePosition(servo_motor2,0.3300)
break;
else
disp(‘continued’)
h = h+1
end

end

% Clean up
fclose(logFile); % Close the log file
clear arduino_board servo_motor servo_motor1 servo_motor2 s
clear cap;
release(blobAnalyzer);
close(hFig); % Close the figure when done
disp(‘System is Stable’);

function norm_position = normalize(angle_deg)
angle_deg = angle_deg/1.5;
angle_deg = 90 – angle_deg;
angle_deg = angle_deg – (angle_deg/100);
norm_position = angle_deg/180;
endHi, im working on a ball on a plate system, i dont have a system model and i want to auto tune my PID controller on the basis of the data received from sensors. I want to know how to auto tune PID in matlab or if theres a way to connect sensor data with simulink in real time for auto tuning purpose. Moreover, i find it difficult to perfrom image processing to gather data in simulink. My code is shared below for further assistance.
%% Initialize Environment
clear all;
clc;
close all;
%% Initialize Data Logging
logFile = fopen(‘data_log.csv’, ‘w’); % Open a CSV file for writing
fprintf(logFile, ‘Time,Disturbance1,Disturbance2,Disturbance3,RollAngle,PitchAngle,CentroidX,CentroidY,Servo1,Servo2,Servo3n’); % Header row
stateLogFile = fopen(‘state_log.csv’, ‘w’); % Open another CSV file for writing
fprintf(stateLogFile, ‘Time,Phi,Theta,X_Offset,Y_Offset,Zp,Gamman’); % Header row

%% Initialize Image Processing
% Load the camera parameters
load(‘test.mat’); % Ensure the file ‘cameraParams.mat’ is in your working directory
% Create a webcam object
cap = webcam(‘Adesso CyberTrack H3’);
dist_1 = 0;
dist_2=dist_1
dist_3=dist_2
% Adjust camera settings if supported
try
% Set exposure time within valid range
exposureTime = -6; % Adjust as needed within the valid range
cap.ExposureMode = ‘manual’;
cap.Exposure = exposureTime;
catch ME
disp(‘Warning: Unable to set exposure time for the camera.’);
disp(ME.message);
end

%% Anomaly Variables
% Parameters for angle adjustments
p_a = 0;
p_r = 0;

%% Blob Object
% Blob analysis object for ball detection
% Blob analysis object for ball detection using vision.BlobAnalysis
blobAnalyzer = vision.BlobAnalysis(…
‘MinimumBlobArea’, 200, … % Adjust to filter small noise
‘MaximumBlobArea’, 50000, … % Adjust to ensure the ball is detected
‘BoundingBoxOutputPort’, true, …
‘CentroidOutputPort’, true, …
‘AreaOutputPort’, true);

%% Create Figure and Axes
hFig = figure(‘DoubleBuffer’, ‘on’);
hAxes = axes(‘Parent’, hFig);
s = 1;
%% Main Loop

%% Measure Sampling Time
%% Servo Motor Control Initial Position
% Port at which your Arduino is connected
port = ‘COM5’;
% Model of your Arduino board
board = ‘Uno’;
% Creating Arduino object with servo library
arduino_board = arduino(port, board, ‘Libraries’, ‘Servo’);
% Creating servo motor object
servo_motor = servo(arduino_board, ‘D9′,’MinPulseDuration’, 500e-6, ‘MaxPulseDuration’, 2500e-6);
writePosition(servo_motor, 0.333); % 45 degrees in as per calculations and in radians = 0.333
servo_motor1 = servo(arduino_board, ‘D10′,’MinPulseDuration’, 500e-6, ‘MaxPulseDuration’, 2500e-6);
writePosition(servo_motor1, 0.333);
servo_motor2 = servo(arduino_board, ‘D11′,’MinPulseDuration’, 500e-6, ‘MaxPulseDuration’, 2500e-6);
writePosition(servo_motor2, 0.333);

A= 45;
B= 45;
C = 45;
disp(‘done here1’);
h = 1;
for i = 1:2
%% Image Processing Started
start = tic;
[Zp,alpha] = forwardkinfunc(A,B,C);
% Read frame from webcam
frame = snapshot(cap);

% Undistort the frame using the camera parameters
undistortedFrame = undistortImage(frame, cameraParams2);

% Resize the undistorted frame to a lower resolution (e.g., half the original size)
undistortedFrame = imresize(undistortedFrame, 0.2); % Reduce resolution to 50%
%% Calculate the center of the image
[frameHeight, frameWidth, ~] = size(undistortedFrame);
centerX = frameWidth / 2;
centerY = frameHeight / 2;

%% Circle (Dot) Detection
% Convert to grayscale and adjust contrast
I_gray = rgb2gray(undistortedFrame);
I_contrast = imadjust(I_gray);

% Detect circles in the image (dots)
[Centers, Radii] = imfindcircles(I_contrast, [1 5], ‘ObjectPolarity’, ‘dark’, ‘Sensitivity’, 0.9);

% Check if circles are detected
if isempty(Centers)
disp(‘No circles detected’);
else
disp([‘Detected ‘, num2str(size(Centers, 1)), ‘ circles’]);
end

% Display the undistorted frame and detected circles
imshow(undistortedFrame, ‘Parent’, hAxes);
hold(hAxes, ‘on’);
viscircles(hAxes, Centers, Radii, ‘EdgeColor’, ‘r’);
% Plot a magenta dot at the center of the image
plot(hAxes, centerX, centerY, ‘m.’, ‘MarkerSize’, 20); % Magenta dot

% Check if at least 2 circles are detected for line fitting
if size(Centers, 1) >= 2
% Fit lines to X and Y coordinates separately
x = Centers(:, 1);
yof = Centers(:, 2);
pX = polyfit(yof, x, 1); % Line fit for X as a function of Y
pY = polyfit(x, yof, 1); % Line fit for Y as a function of X

% Display fitted line coefficients
disp([‘Line fit for X (Y): slope = ‘, num2str(pX(1))]);
disp([‘Line fit for Y (X): slope = ‘, num2str(pY(1))]);

% Calculate the slopes
slopeX = pX(1);
slopeY = pY(1);

% Calculate roll and pitch angles from the slopes
Theta = -atan2(slopeX, 1); % Tilt along X-axis (roll)
Phi = atan2(slopeY, 1); % Tilt along Y-axis (pitch)

% Display calculated angles
disp([‘Roll Angle: ‘, num2str(Theta)]);
disp([‘Pitch Angle: ‘, num2str(Phi)]);

% Draw the X and Y axis lines
plot(hAxes, [0, size(I_contrast, 2)], [mean(yof), mean(yof)], ‘g–‘, ‘LineWidth’, 2); % X-axis line
plot(hAxes, [mean(x), mean(x)], [0, size(I_contrast, 1)], ‘y–‘, ‘LineWidth’, 2); % Y-axis line

% Draw the slope lines
yFitLineX = polyval(pX, [0, size(I_contrast, 1)]);
plot(hAxes, yFitLineX, [0, size(I_contrast, 1)], ‘b-‘, ‘LineWidth’, 2); % Slope line for X

xFitLineY = polyval(pY, [0, size(I_contrast, 2)]);
plot(hAxes, [0, size(I_contrast, 2)], xFitLineY, ‘r-‘, ‘LineWidth’, 2); % Slope line for Y

% Display the calculated angles
title(hAxes, sprintf(‘Roll: %.2f°, Pitch: %.2f°’, Theta, Phi));
else
title(hAxes, ‘Not enough circles detected’);
end

%% Ball Detection Using vision.BlobAnalysis
% Check if any blobs are detected
%% Ball Detection Using vision.BlobAnalysis
hsvFrame = rgb2hsv(undistortedFrame);

% Define thresholds for channel 1 based on histogram settings
channel1Min = 0.845;
channel1Max = 0.081;

% Define thresholds for channel 2 based on histogram settings
channel2Min = 0.133;
channel2Max = 1.000;

% Define thresholds for channel 3 based on histogram settings
channel3Min = 0.519;
channel3Max = 1.000;

% Create a binary mask based on chosen histogram thresholds
sliderBW = ((hsvFrame(:,:,1) >= channel1Min) | (hsvFrame(:,:,1) <= channel1Max)) & …
(hsvFrame(:,:,2) >= channel2Min) & (hsvFrame(:,:,2) <= channel2Max) & …
(hsvFrame(:,:,3) >= channel3Min) & (hsvFrame(:,:,3) <= channel3Max);
BW = sliderBW;
BW = imfill(BW, ‘holes’); % Fill any small holes
BW = bwareaopen(BW, 200); % Remove small noise (adjust 200 as needed)
% Apply morphological operations to clean up the mask
BW = imopen(BW, strel(‘disk’, 5));

BW = imclose(BW, strel(‘disk’, 20));

% Perform blob analysis
[area,centroid,bbox] = step(blobAnalyzer,BW);
if ~isempty(centroid)
x_p = centroid(1);
y_p = centroid(2);

% Draw the bounding box on the undistorted frame
rectangle(‘Position’, bbox, ‘EdgeColor’, ‘green’, ‘LineWidth’, 2);
plot(hAxes, x_p, y_p, ‘ro’, ‘MarkerSize’, 10);
pix_cm = 32/frameWidth;
xc = x_p*pix_cm;
yc = y_p*pix_cm
[xof1, yof1] = camera_base(xc,yc)

end
elapsed_time = toc(start); % Compute elapsed time dynamically
Ts = getElapsedTime();% Use elapsed time for PID calculations
tic;
Kp_x = 10; Ki_x = 0; Kd_x = 0
Kp_y = 10; Ki_y = 0; Kd_y = 0;
Kp_phi = 80; Ki_phi = 0; Kd_phi = 0.5;
Kp_theta =80; Ki_theta =0; Kd_theta = 0.5;

% Setpoints (target values)
setpoint_x = -0.1;
setpoint_y = -5.5;
setpoint_phi = 0.00;
setpoint_theta = 0.00;

previous_errorx = 0;
integral_errorx=0
derivative_errorx = 0

previous_errory = 0;
integral_errory=0
derivative_errory = 0

previous_errorphi = 0;
integral_errorphi=0
derivative_errorphi = 0

previous_errortheta = 0;
integral_errortheta=0
derivative_errortheta = 0
prev_filtest = 0;
filte_est =0;
prev_filtesty = 0;
filte_esty =0;
prev_filtestp = 0;
filte_estp =0;
prev_filtestth = 0;
filte_estth =0;
o = 0.05;
K_xp = 4/100;
K_yp= 4.8/100;
K_xr = 0.63/100;
K_yr = 3.05/100;

error_x= setpoint_x – xof1;
integral_errorx = integral_errorx + error_x * Ts;
derivative_errorx = (error_x – previous_errorx);
filte_est = (o*prev_filtest)+(1-o)*derivative_errorx
derivative = filte_est/Ts;
prev_filtest = filte_est;
control_signalx = Kp_x * error_x + Ki_x * integral_errorx + Kd_x * derivative;
previous_errorx = error_x;

% Cross-coupling effect on phi due to x_error
theta_coupling_effect = K_xr * error_x;

error_y= setpoint_y – yof1;
integral_errory = integral_errory + error_y * Ts;
derivative_errory = (error_y – previous_errory)
filte_esty = (o*prev_filtesty)+(1-o)*derivative_errory
derivativey = filte_esty/Ts;
prev_filtesty = filte_esty;
control_signaly = Kp_y * error_y + Ki_y * integral_errory + Kd_y * derivativey;
previous_errory = error_y;

% Cross-coupling effect on theta due to y_error
phi_coupling_effect = K_yp * error_y;

% Step 5: Compute final tilt angles
theta_desired =(K_yr * error_y) + phi_coupling_effect; % Roll (tilt about X-axis)
phi_desired = (K_xp * error_x) + theta_coupling_effect; % Pitch (tilt about Y-axis)

error_phi= phi_desired – Phi;
integral_errorphi = integral_errorphi + error_phi * Ts;
derivative_errorphi = (error_phi – previous_errorphi)
filte_estp = (o*prev_filtestp)+(1-o)*derivative_errorphi
derivativep = filte_estp/Ts;
prev_filtestp = filte_estp;
control_signalphi = Kp_phi * error_phi + Ki_phi * integral_errorphi + Kd_phi * derivativep;
previous_errorphi = error_phi;
Phi1 = (Phi + control_signalphi * Ts); % Update tilt
Phi1 = max(-0.6, min(Phi1, 0.6));
pidTuner(Phi1)

error_theta= theta_desired – Theta;
integral_errortheta = integral_errortheta + error_theta * Ts;
derivative_errortheta = (error_theta – previous_errortheta)
filte_estth = (o*prev_filtestth)+(1-o)*derivative_errortheta;
derivativeth = filte_estth/Ts;
prev_filtestth = filte_estth;
control_signaltheta = Kp_theta * error_theta + Ki_theta * integral_errortheta + Kd_theta * derivativeth;
previous_errortheta = error_theta;
Theta1 = (Theta + control_signaltheta * Ts); % Update tilt
Theta1 = max(-0.6, min(Theta1, 0.6));
xof1 = xof1+(control_signalx*Ts);
yof1 = yof1+(control_signaly*Ts)

disp(‘done here 3’)
xo= 82*sin(Phi);
yo =-82*sin(Theta);

[a11f,a22f,a33f] = inversekin(xof1,yof1,Phi1,Theta1,Zp,alpha)
% [a11f,a22f,a33f] = inversekin(xof1,yof1,phi_control,theta_control,Zp,gamma)
a11 = a11f;
a22 = a22f;
a33 = a33f;
disp(‘done here 4’)
% Clamp the angles to the range 40° to 50°
a11 = max(42, min(a11, 48));
a22 = max(42, min(a22, 48));
a33 = max(42, min(a33, 48));
currentTime = datestr(now, ‘HH:MM:SS.FFF’); % Get current time
fprintf(logFile, ‘%s,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.4f,%.4f,%.4fn’, …
currentTime, dist_1, dist_2, dist_3, Phi, Theta, xof1, yof1,a11, a22, a33);
% fprintf(stateLogFile, ‘%.3f,%.3f,%.3f,%.3f,%.3f,%.3f,%.3fn’, Ts, Phi, Theta, xof1, yof1, Zp, gamma);

%% Servo Motor Code
% Clamp the angles to the range 40° to 50°
a = normalize(a11);
b = normalize(a22);
c = normalize(a33);

writePosition(servo_motor,a)
writePosition(servo_motor1,b)
writePosition(servo_motor2,c)
stop_it = toc(start);
A = a11;
B = a22;
C= a33;
stop = toc(start)
disp(‘done here 5’);

if (xof1 <=0.5 && xof1>=-0.5)&&(yof1 <= 1.5 &&yof1>=-1.5)
% if (xof1==0 && yof1==0)
disp(‘im done’)
writePosition(servo_motor,0.3300)
writePosition(servo_motor1,0.3300)
writePosition(servo_motor2,0.3300)
break;
else
disp(‘continued’)
h = h+1
end

end

% Clean up
fclose(logFile); % Close the log file
clear arduino_board servo_motor servo_motor1 servo_motor2 s
clear cap;
release(blobAnalyzer);
close(hFig); % Close the figure when done
disp(‘System is Stable’);

function norm_position = normalize(angle_deg)
angle_deg = angle_deg/1.5;
angle_deg = 90 – angle_deg;
angle_deg = angle_deg – (angle_deg/100);
norm_position = angle_deg/180;
end Hi, im working on a ball on a plate system, i dont have a system model and i want to auto tune my PID controller on the basis of the data received from sensors. I want to know how to auto tune PID in matlab or if theres a way to connect sensor data with simulink in real time for auto tuning purpose. Moreover, i find it difficult to perfrom image processing to gather data in simulink. My code is shared below for further assistance.
%% Initialize Environment
clear all;
clc;
close all;
%% Initialize Data Logging
logFile = fopen(‘data_log.csv’, ‘w’); % Open a CSV file for writing
fprintf(logFile, ‘Time,Disturbance1,Disturbance2,Disturbance3,RollAngle,PitchAngle,CentroidX,CentroidY,Servo1,Servo2,Servo3n’); % Header row
stateLogFile = fopen(‘state_log.csv’, ‘w’); % Open another CSV file for writing
fprintf(stateLogFile, ‘Time,Phi,Theta,X_Offset,Y_Offset,Zp,Gamman’); % Header row

%% Initialize Image Processing
% Load the camera parameters
load(‘test.mat’); % Ensure the file ‘cameraParams.mat’ is in your working directory
% Create a webcam object
cap = webcam(‘Adesso CyberTrack H3’);
dist_1 = 0;
dist_2=dist_1
dist_3=dist_2
% Adjust camera settings if supported
try
% Set exposure time within valid range
exposureTime = -6; % Adjust as needed within the valid range
cap.ExposureMode = ‘manual’;
cap.Exposure = exposureTime;
catch ME
disp(‘Warning: Unable to set exposure time for the camera.’);
disp(ME.message);
end

%% Anomaly Variables
% Parameters for angle adjustments
p_a = 0;
p_r = 0;

%% Blob Object
% Blob analysis object for ball detection
% Blob analysis object for ball detection using vision.BlobAnalysis
blobAnalyzer = vision.BlobAnalysis(…
‘MinimumBlobArea’, 200, … % Adjust to filter small noise
‘MaximumBlobArea’, 50000, … % Adjust to ensure the ball is detected
‘BoundingBoxOutputPort’, true, …
‘CentroidOutputPort’, true, …
‘AreaOutputPort’, true);

%% Create Figure and Axes
hFig = figure(‘DoubleBuffer’, ‘on’);
hAxes = axes(‘Parent’, hFig);
s = 1;
%% Main Loop

%% Measure Sampling Time
%% Servo Motor Control Initial Position
% Port at which your Arduino is connected
port = ‘COM5’;
% Model of your Arduino board
board = ‘Uno’;
% Creating Arduino object with servo library
arduino_board = arduino(port, board, ‘Libraries’, ‘Servo’);
% Creating servo motor object
servo_motor = servo(arduino_board, ‘D9′,’MinPulseDuration’, 500e-6, ‘MaxPulseDuration’, 2500e-6);
writePosition(servo_motor, 0.333); % 45 degrees in as per calculations and in radians = 0.333
servo_motor1 = servo(arduino_board, ‘D10′,’MinPulseDuration’, 500e-6, ‘MaxPulseDuration’, 2500e-6);
writePosition(servo_motor1, 0.333);
servo_motor2 = servo(arduino_board, ‘D11′,’MinPulseDuration’, 500e-6, ‘MaxPulseDuration’, 2500e-6);
writePosition(servo_motor2, 0.333);

A= 45;
B= 45;
C = 45;
disp(‘done here1’);
h = 1;
for i = 1:2
%% Image Processing Started
start = tic;
[Zp,alpha] = forwardkinfunc(A,B,C);
% Read frame from webcam
frame = snapshot(cap);

% Undistort the frame using the camera parameters
undistortedFrame = undistortImage(frame, cameraParams2);

% Resize the undistorted frame to a lower resolution (e.g., half the original size)
undistortedFrame = imresize(undistortedFrame, 0.2); % Reduce resolution to 50%
%% Calculate the center of the image
[frameHeight, frameWidth, ~] = size(undistortedFrame);
centerX = frameWidth / 2;
centerY = frameHeight / 2;

%% Circle (Dot) Detection
% Convert to grayscale and adjust contrast
I_gray = rgb2gray(undistortedFrame);
I_contrast = imadjust(I_gray);

% Detect circles in the image (dots)
[Centers, Radii] = imfindcircles(I_contrast, [1 5], ‘ObjectPolarity’, ‘dark’, ‘Sensitivity’, 0.9);

% Check if circles are detected
if isempty(Centers)
disp(‘No circles detected’);
else
disp([‘Detected ‘, num2str(size(Centers, 1)), ‘ circles’]);
end

% Display the undistorted frame and detected circles
imshow(undistortedFrame, ‘Parent’, hAxes);
hold(hAxes, ‘on’);
viscircles(hAxes, Centers, Radii, ‘EdgeColor’, ‘r’);
% Plot a magenta dot at the center of the image
plot(hAxes, centerX, centerY, ‘m.’, ‘MarkerSize’, 20); % Magenta dot

% Check if at least 2 circles are detected for line fitting
if size(Centers, 1) >= 2
% Fit lines to X and Y coordinates separately
x = Centers(:, 1);
yof = Centers(:, 2);
pX = polyfit(yof, x, 1); % Line fit for X as a function of Y
pY = polyfit(x, yof, 1); % Line fit for Y as a function of X

% Display fitted line coefficients
disp([‘Line fit for X (Y): slope = ‘, num2str(pX(1))]);
disp([‘Line fit for Y (X): slope = ‘, num2str(pY(1))]);

% Calculate the slopes
slopeX = pX(1);
slopeY = pY(1);

% Calculate roll and pitch angles from the slopes
Theta = -atan2(slopeX, 1); % Tilt along X-axis (roll)
Phi = atan2(slopeY, 1); % Tilt along Y-axis (pitch)

% Display calculated angles
disp([‘Roll Angle: ‘, num2str(Theta)]);
disp([‘Pitch Angle: ‘, num2str(Phi)]);

% Draw the X and Y axis lines
plot(hAxes, [0, size(I_contrast, 2)], [mean(yof), mean(yof)], ‘g–‘, ‘LineWidth’, 2); % X-axis line
plot(hAxes, [mean(x), mean(x)], [0, size(I_contrast, 1)], ‘y–‘, ‘LineWidth’, 2); % Y-axis line

% Draw the slope lines
yFitLineX = polyval(pX, [0, size(I_contrast, 1)]);
plot(hAxes, yFitLineX, [0, size(I_contrast, 1)], ‘b-‘, ‘LineWidth’, 2); % Slope line for X

xFitLineY = polyval(pY, [0, size(I_contrast, 2)]);
plot(hAxes, [0, size(I_contrast, 2)], xFitLineY, ‘r-‘, ‘LineWidth’, 2); % Slope line for Y

% Display the calculated angles
title(hAxes, sprintf(‘Roll: %.2f°, Pitch: %.2f°’, Theta, Phi));
else
title(hAxes, ‘Not enough circles detected’);
end

%% Ball Detection Using vision.BlobAnalysis
% Check if any blobs are detected
%% Ball Detection Using vision.BlobAnalysis
hsvFrame = rgb2hsv(undistortedFrame);

% Define thresholds for channel 1 based on histogram settings
channel1Min = 0.845;
channel1Max = 0.081;

% Define thresholds for channel 2 based on histogram settings
channel2Min = 0.133;
channel2Max = 1.000;

% Define thresholds for channel 3 based on histogram settings
channel3Min = 0.519;
channel3Max = 1.000;

% Create a binary mask based on chosen histogram thresholds
sliderBW = ((hsvFrame(:,:,1) >= channel1Min) | (hsvFrame(:,:,1) <= channel1Max)) & …
(hsvFrame(:,:,2) >= channel2Min) & (hsvFrame(:,:,2) <= channel2Max) & …
(hsvFrame(:,:,3) >= channel3Min) & (hsvFrame(:,:,3) <= channel3Max);
BW = sliderBW;
BW = imfill(BW, ‘holes’); % Fill any small holes
BW = bwareaopen(BW, 200); % Remove small noise (adjust 200 as needed)
% Apply morphological operations to clean up the mask
BW = imopen(BW, strel(‘disk’, 5));

BW = imclose(BW, strel(‘disk’, 20));

% Perform blob analysis
[area,centroid,bbox] = step(blobAnalyzer,BW);
if ~isempty(centroid)
x_p = centroid(1);
y_p = centroid(2);

% Draw the bounding box on the undistorted frame
rectangle(‘Position’, bbox, ‘EdgeColor’, ‘green’, ‘LineWidth’, 2);
plot(hAxes, x_p, y_p, ‘ro’, ‘MarkerSize’, 10);
pix_cm = 32/frameWidth;
xc = x_p*pix_cm;
yc = y_p*pix_cm
[xof1, yof1] = camera_base(xc,yc)

end
elapsed_time = toc(start); % Compute elapsed time dynamically
Ts = getElapsedTime();% Use elapsed time for PID calculations
tic;
Kp_x = 10; Ki_x = 0; Kd_x = 0
Kp_y = 10; Ki_y = 0; Kd_y = 0;
Kp_phi = 80; Ki_phi = 0; Kd_phi = 0.5;
Kp_theta =80; Ki_theta =0; Kd_theta = 0.5;

% Setpoints (target values)
setpoint_x = -0.1;
setpoint_y = -5.5;
setpoint_phi = 0.00;
setpoint_theta = 0.00;

previous_errorx = 0;
integral_errorx=0
derivative_errorx = 0

previous_errory = 0;
integral_errory=0
derivative_errory = 0

previous_errorphi = 0;
integral_errorphi=0
derivative_errorphi = 0

previous_errortheta = 0;
integral_errortheta=0
derivative_errortheta = 0
prev_filtest = 0;
filte_est =0;
prev_filtesty = 0;
filte_esty =0;
prev_filtestp = 0;
filte_estp =0;
prev_filtestth = 0;
filte_estth =0;
o = 0.05;
K_xp = 4/100;
K_yp= 4.8/100;
K_xr = 0.63/100;
K_yr = 3.05/100;

error_x= setpoint_x – xof1;
integral_errorx = integral_errorx + error_x * Ts;
derivative_errorx = (error_x – previous_errorx);
filte_est = (o*prev_filtest)+(1-o)*derivative_errorx
derivative = filte_est/Ts;
prev_filtest = filte_est;
control_signalx = Kp_x * error_x + Ki_x * integral_errorx + Kd_x * derivative;
previous_errorx = error_x;

% Cross-coupling effect on phi due to x_error
theta_coupling_effect = K_xr * error_x;

error_y= setpoint_y – yof1;
integral_errory = integral_errory + error_y * Ts;
derivative_errory = (error_y – previous_errory)
filte_esty = (o*prev_filtesty)+(1-o)*derivative_errory
derivativey = filte_esty/Ts;
prev_filtesty = filte_esty;
control_signaly = Kp_y * error_y + Ki_y * integral_errory + Kd_y * derivativey;
previous_errory = error_y;

% Cross-coupling effect on theta due to y_error
phi_coupling_effect = K_yp * error_y;

% Step 5: Compute final tilt angles
theta_desired =(K_yr * error_y) + phi_coupling_effect; % Roll (tilt about X-axis)
phi_desired = (K_xp * error_x) + theta_coupling_effect; % Pitch (tilt about Y-axis)

error_phi= phi_desired – Phi;
integral_errorphi = integral_errorphi + error_phi * Ts;
derivative_errorphi = (error_phi – previous_errorphi)
filte_estp = (o*prev_filtestp)+(1-o)*derivative_errorphi
derivativep = filte_estp/Ts;
prev_filtestp = filte_estp;
control_signalphi = Kp_phi * error_phi + Ki_phi * integral_errorphi + Kd_phi * derivativep;
previous_errorphi = error_phi;
Phi1 = (Phi + control_signalphi * Ts); % Update tilt
Phi1 = max(-0.6, min(Phi1, 0.6));
pidTuner(Phi1)

error_theta= theta_desired – Theta;
integral_errortheta = integral_errortheta + error_theta * Ts;
derivative_errortheta = (error_theta – previous_errortheta)
filte_estth = (o*prev_filtestth)+(1-o)*derivative_errortheta;
derivativeth = filte_estth/Ts;
prev_filtestth = filte_estth;
control_signaltheta = Kp_theta * error_theta + Ki_theta * integral_errortheta + Kd_theta * derivativeth;
previous_errortheta = error_theta;
Theta1 = (Theta + control_signaltheta * Ts); % Update tilt
Theta1 = max(-0.6, min(Theta1, 0.6));
xof1 = xof1+(control_signalx*Ts);
yof1 = yof1+(control_signaly*Ts)

disp(‘done here 3’)
xo= 82*sin(Phi);
yo =-82*sin(Theta);

[a11f,a22f,a33f] = inversekin(xof1,yof1,Phi1,Theta1,Zp,alpha)
% [a11f,a22f,a33f] = inversekin(xof1,yof1,phi_control,theta_control,Zp,gamma)
a11 = a11f;
a22 = a22f;
a33 = a33f;
disp(‘done here 4’)
% Clamp the angles to the range 40° to 50°
a11 = max(42, min(a11, 48));
a22 = max(42, min(a22, 48));
a33 = max(42, min(a33, 48));
currentTime = datestr(now, ‘HH:MM:SS.FFF’); % Get current time
fprintf(logFile, ‘%s,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.4f,%.4f,%.4fn’, …
currentTime, dist_1, dist_2, dist_3, Phi, Theta, xof1, yof1,a11, a22, a33);
% fprintf(stateLogFile, ‘%.3f,%.3f,%.3f,%.3f,%.3f,%.3f,%.3fn’, Ts, Phi, Theta, xof1, yof1, Zp, gamma);

%% Servo Motor Code
% Clamp the angles to the range 40° to 50°
a = normalize(a11);
b = normalize(a22);
c = normalize(a33);

writePosition(servo_motor,a)
writePosition(servo_motor1,b)
writePosition(servo_motor2,c)
stop_it = toc(start);
A = a11;
B = a22;
C= a33;
stop = toc(start)
disp(‘done here 5’);

if (xof1 <=0.5 && xof1>=-0.5)&&(yof1 <= 1.5 &&yof1>=-1.5)
% if (xof1==0 && yof1==0)
disp(‘im done’)
writePosition(servo_motor,0.3300)
writePosition(servo_motor1,0.3300)
writePosition(servo_motor2,0.3300)
break;
else
disp(‘continued’)
h = h+1
end

end

% Clean up
fclose(logFile); % Close the log file
clear arduino_board servo_motor servo_motor1 servo_motor2 s
clear cap;
release(blobAnalyzer);
close(hFig); % Close the figure when done
disp(‘System is Stable’);

function norm_position = normalize(angle_deg)
angle_deg = angle_deg/1.5;
angle_deg = 90 – angle_deg;
angle_deg = angle_deg – (angle_deg/100);
norm_position = angle_deg/180;
end control, image processing MATLAB Answers — New Questions

​

starting from values of intensity and direction of the wind, I created the graphs relating to the probability of obtaining certain values of intensity and direction of the wind at the same time. I used the bar3 function which creates my instigrams in 3d. is there a function that allows me to calculate the same probability but with a punctual graph? that the numerical values are given back to me.starting from values of intensity and direction of the wind, I created the graphs relating to the probability of obtaining certain values of intensity and direction of the wind at the same time. I used the bar3 function which creates my instigrams in 3d. is there a function that allows me to calculate the same probability but with a punctual graph? that the numerical values are given back to me. starting from values of intensity and direction of the wind, I created the graphs relating to the probability of obtaining certain values of intensity and direction of the wind at the same time. I used the bar3 function which creates my instigrams in 3d. is there a function that allows me to calculate the same probability but with a punctual graph? that the numerical values are given back to me. probability, graph, intensity, velocity MATLAB Answers — New Questions

​

I’m consistently getting an error when trying to connect the Arduino Board RP2040 to Matlab via bluetooth, i needed to plot some images from sensor data, using matlab, but withouth the connection being allowed, i can’t plot or see any interation between Arduino and Matlab, any solutions?
when using the code:
btlist = blelist; disp(btlist);
The board does appear in the BLE list, however, when:
device = ble("NanoRP2040");
The interactions does start, but stops almost imediatly, showing the error:
"Error using ble (line 149)
Failed to connect to device. Make sure device is within proper range and not connected elsewhere. For more
information, see Troubleshooting section in Documentation."

Any suggestions?!I’m consistently getting an error when trying to connect the Arduino Board RP2040 to Matlab via bluetooth, i needed to plot some images from sensor data, using matlab, but withouth the connection being allowed, i can’t plot or see any interation between Arduino and Matlab, any solutions?
when using the code:
btlist = blelist; disp(btlist);
The board does appear in the BLE list, however, when:
device = ble("NanoRP2040");
The interactions does start, but stops almost imediatly, showing the error:
"Error using ble (line 149)
Failed to connect to device. Make sure device is within proper range and not connected elsewhere. For more
information, see Troubleshooting section in Documentation."

Any suggestions?! I’m consistently getting an error when trying to connect the Arduino Board RP2040 to Matlab via bluetooth, i needed to plot some images from sensor data, using matlab, but withouth the connection being allowed, i can’t plot or see any interation between Arduino and Matlab, any solutions?
when using the code:
btlist = blelist; disp(btlist);
The board does appear in the BLE list, however, when:
device = ble("NanoRP2040");
The interactions does start, but stops almost imediatly, showing the error:
"Error using ble (line 149)
Failed to connect to device. Make sure device is within proper range and not connected elsewhere. For more
information, see Troubleshooting section in Documentation."

Any suggestions?! arduino, bluetooth, matlab MATLAB Answers — New Questions

​

I ‘m using contour option in Matlab R2014a to plot many curves: contour(x,y,c,cilevels); with cilevels = [0.0,.005,0.019];
but I need to select one curve (level) from the contour: the option peaks is not working? and I have tried : contour(x,y,c,[1 1]); but it was error also?
could you please help me?I ‘m using contour option in Matlab R2014a to plot many curves: contour(x,y,c,cilevels); with cilevels = [0.0,.005,0.019];
but I need to select one curve (level) from the contour: the option peaks is not working? and I have tried : contour(x,y,c,[1 1]); but it was error also?
could you please help me? I ‘m using contour option in Matlab R2014a to plot many curves: contour(x,y,c,cilevels); with cilevels = [0.0,.005,0.019];
but I need to select one curve (level) from the contour: the option peaks is not working? and I have tried : contour(x,y,c,[1 1]); but it was error also?
could you please help me? contour, single level MATLAB Answers — New Questions

​

I’m trying to wire a 4×4 membrane switch to be able to input a string of numbers. The problem is A.) don’t have a lot of experience in matlab, and B.) all of the code libraries and stuff I’ve found for the switch is all based in the arduino software. Does anyone have any idea on how I can transfer it over or write my own?
here is a example of the arduino library:
#include “Keypad.h”
const byte ROWS = 4; // number of rows
const byte COLS = 3; // number of columns
char keys[ROWS][COLS] = {
{‘1′,’2′,’3’},
{‘4′,’5′,’6’},
{‘7′,’8′,’9’},
{‘#’,’0′,’*’}
};
byte rowPins[ROWS] = {8, 7, 6, 5}; // row pinouts of the keypad R1 = D8, R2 = D7, R3 = D6, R4 = D5
byte colPins[COLS] = {4, 3, 2}; // column pinouts of the keypad C1 = D4, C2 = D3, C3 = D2
Keypad keypad = Keypad(makeKeymap(keys), rowPins, colPins, ROWS, COLS);
void setup()
{
Serial.begin(9600);
}
void loop()
{
char key = keypad.getKey();
if (key ~= NO_KEY)
Serial.println(key);
}I’m trying to wire a 4×4 membrane switch to be able to input a string of numbers. The problem is A.) don’t have a lot of experience in matlab, and B.) all of the code libraries and stuff I’ve found for the switch is all based in the arduino software. Does anyone have any idea on how I can transfer it over or write my own?
here is a example of the arduino library:
#include “Keypad.h”
const byte ROWS = 4; // number of rows
const byte COLS = 3; // number of columns
char keys[ROWS][COLS] = {
{‘1′,’2′,’3’},
{‘4′,’5′,’6’},
{‘7′,’8′,’9’},
{‘#’,’0′,’*’}
};
byte rowPins[ROWS] = {8, 7, 6, 5}; // row pinouts of the keypad R1 = D8, R2 = D7, R3 = D6, R4 = D5
byte colPins[COLS] = {4, 3, 2}; // column pinouts of the keypad C1 = D4, C2 = D3, C3 = D2
Keypad keypad = Keypad(makeKeymap(keys), rowPins, colPins, ROWS, COLS);
void setup()
{
Serial.begin(9600);
}
void loop()
{
char key = keypad.getKey();
if (key ~= NO_KEY)
Serial.println(key);
} I’m trying to wire a 4×4 membrane switch to be able to input a string of numbers. The problem is A.) don’t have a lot of experience in matlab, and B.) all of the code libraries and stuff I’ve found for the switch is all based in the arduino software. Does anyone have any idea on how I can transfer it over or write my own?
here is a example of the arduino library:
#include “Keypad.h”
const byte ROWS = 4; // number of rows
const byte COLS = 3; // number of columns
char keys[ROWS][COLS] = {
{‘1′,’2′,’3’},
{‘4′,’5′,’6’},
{‘7′,’8′,’9’},
{‘#’,’0′,’*’}
};
byte rowPins[ROWS] = {8, 7, 6, 5}; // row pinouts of the keypad R1 = D8, R2 = D7, R3 = D6, R4 = D5
byte colPins[COLS] = {4, 3, 2}; // column pinouts of the keypad C1 = D4, C2 = D3, C3 = D2
Keypad keypad = Keypad(makeKeymap(keys), rowPins, colPins, ROWS, COLS);
void setup()
{
Serial.begin(9600);
}
void loop()
{
char key = keypad.getKey();
if (key ~= NO_KEY)
Serial.println(key);
} keypad, membrane, button MATLAB Answers — New Questions

​

Ib(1:478,31:565)(~BW) = 255
Ib is a gray image. I want to set a fixed black region as blue, but this code is wrong.
Do we have simple method to finish it?Ib(1:478,31:565)(~BW) = 255
Ib is a gray image. I want to set a fixed black region as blue, but this code is wrong.
Do we have simple method to finish it? Ib(1:478,31:565)(~BW) = 255
Ib is a gray image. I want to set a fixed black region as blue, but this code is wrong.
Do we have simple method to finish it? matlab code MATLAB Answers — New Questions

​

I have a model with S-function which uses ssRegisterDataTypeFxpBinaryPoint for uint64, but I am getting error when try to create an executable with slbuild for my model.
It looks like linking error, how to link fixedpoint library when I create an executable of my model with slbuild.I have a model with S-function which uses ssRegisterDataTypeFxpBinaryPoint for uint64, but I am getting error when try to create an executable with slbuild for my model.
It looks like linking error, how to link fixedpoint library when I create an executable of my model with slbuild. I have a model with S-function which uses ssRegisterDataTypeFxpBinaryPoint for uint64, but I am getting error when try to create an executable with slbuild for my model.
It looks like linking error, how to link fixedpoint library when I create an executable of my model with slbuild. uint64, slbuild, simulink, lfixedpoint MATLAB Answers — New Questions

​