19/05

method2/method2.ino

@@ -0,0 +1,49 @@
+#include <Servo.h>
+
+Servo myservo;  // create servo object to control a servo
+
+const unsigned long interval = 10;  // interrupt interval in milliseconds
+volatile bool interruptFlag = false;  // flag to indicate interrupt occurrence
+
+int potpin = A0;  // analog pin used to connect the potentiometer
+const int numReadings = 5;  // number of readings to average
+int readings[numReadings];  // array to store the readings
+int index = 0;  // index for storing the readings
+int total = 0;  // sum of the readings
+
+// Coefficients from the polynomial regression
+// Replace these with your actual coefficients
+const float a = 5.118e-09;  // Coefficient for x^2
+const float b = 1.289e-05;  // Coefficient for x
+const float c = 0.0118;  // Constant term
+const float d = 4.729;  // Coefficient for x
+const float e = 743.2;  // Constant term
+
+// Function to map analog values to weight using polynomial regression
+float map_analog_to_weight(int analog_value) {
+  float weight = a*analog_value*analog_value*analog_value*analog_value - b*analog_value*analog_value*analog_value + c*analog_value*analog_value-d*analog_value+e;
+  return weight;
+}
+
+
+void setup() {
+  myservo.attach(9);  // attaches the servo on pin 9 to the servo object
+  Serial.begin(9600); // begin serial baud rate
+
+}
+
+void loop() {
+  
+
+    int sensorValue = analogRead(potpin);  // Get the average of the readings
+
+   // Output the filtered sensor value
+
+    float weight = map_analog_to_weight(sensorValue);
+  	Serial.println(weight);// map analog value to weight
+    int mappedValue = map(weight, 0, 500, 180, 0);  // scale weight to angle (value between 0 and 180)
+    myservo.write(mappedValue);
+  	delay(10);// sets the servo position according to the scaled value
+
+
+}

polynomial regression degree 4.py

@@ -0,0 +1,18 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+weights = np.array([10, 20, 50, 100, 200, 500])  # weight in grams
+analog_values = np.array([920, 720, 360, 280, 180, 60])  # corresponding analog values
+
+degree = 4  # degree of the polynomial you want to fit, adjust as needed
+coefficients = np.polyfit(analog_values, weights, degree)
+
+polynomial = np.poly1d(coefficients)
+
+# Plotting the original data points and the polynomial regression for visualization
+x = np.linspace(60, 920, 400)
+y = polynomial(x)
+plt.scatter(analog_values, weights, color='blue')  # original data points
+plt.plot(x, y, color='red')  # polynomial regression
+plt.show()
+print(polynomial)