PID controller in flight stabilization in Drone Programming - Mini Project: Build & Apply

PID Controller in Flight Stabilization

📖 Scenario: You are programming a drone's flight system. To keep the drone stable in the air, you use a PID controller. This controller adjusts the drone's motor speeds based on how far off it is from the desired position.Imagine the drone wants to stay level at 0 degrees tilt. The PID controller helps correct any tilt by calculating adjustments.

🎯 Goal: Build a simple PID controller in code that calculates the correction needed to stabilize the drone's tilt angle.

📋 What You'll Learn

Create variables to hold the current tilt error values.

Set PID constants for proportional, integral, and derivative terms.

Calculate the PID output using the error values.

Print the PID output correction value.

💡 Why This Matters

🌍 Real World

PID controllers are used in drones to keep them balanced and stable during flight by continuously correcting their position.

💼 Career

Understanding PID control is important for drone programmers, robotics engineers, and anyone working with automated control systems.

1

Setup the initial error values

Create three variables called current_error, previous_error, and integral_error with the values 5.0, 3.0, and 0.0 respectively.

Drone Programming

# Create variables for current_error, previous_error, and integral_error
# Your code here

Hint

Think of these as the drone's tilt measurements: current tilt, last tilt, and total tilt over time.

2

Set PID constants

Create three variables called Kp, Ki, and Kd and set them to 1.2, 0.01, and 0.5 respectively.

Drone Programming

current_error = 5.0
previous_error = 3.0
integral_error = 0.0
# Set PID constants Kp, Ki, and Kd
# Your code here

Hint

These constants control how strongly the drone reacts to errors.

3

Calculate the PID output

Update integral_error by adding current_error. Then calculate derivative_error as current_error - previous_error. Finally, calculate pid_output as Kp * current_error + Ki * integral_error + Kd * derivative_error.

Drone Programming

current_error = 5.0
previous_error = 3.0
integral_error = 0.0
Kp = 1.2
Ki = 0.01
Kd = 0.5
# Update integral_error, calculate derivative_error, and compute pid_output
# Your code here

Hint

Think of integral_error as the total past error, and derivative_error as how fast the error is changing.

4

Display the PID output

Write a print statement to display the value of pid_output.

Drone Programming

current_error = 5.0
previous_error = 3.0
integral_error = 0.0
Kp = 1.2
Ki = 0.01
Kd = 0.5
integral_error += current_error
derivative_error = current_error - previous_error
pid_output = Kp * current_error + Ki * integral_error + Kd * derivative_error
# Print the pid_output value
# Your code here

Hint

The output shows how much the drone should adjust to stay stable.

Practice

1. What is the main purpose of a PID controller in drone flight stabilization?

easy

A. To keep the drone balanced by adjusting its movements smoothly

B. To increase the drone's speed to maximum

C. To make the drone fly in a straight line without any control

D. To turn off the drone automatically when battery is low

Solution

1. Step 1: Understand the role of PID controller

   A PID controller uses error, integral, and derivative to adjust movements smoothly.

2. Step 2: Connect PID function to drone stabilization

   This adjustment helps keep the drone balanced and stable during flight.

3. Final Answer:

   To keep the drone balanced by adjusting its movements smoothly -> Option A

4. Quick Check:

   PID controls balance = To keep drone balanced [OK]

Hint: PID controls balance and smooth movement in drones [OK]

Common Mistakes:

• Thinking PID only controls speed
• Confusing PID with battery management
• Assuming PID makes drone fly straight without control

2. Which of the following is the correct way to calculate the derivative term in a PID controller code snippet?

easy

A. derivative = previous_error - current_error * delta_time

B. derivative = current_error + previous_error * delta_time

C. derivative = current_error * previous_error / delta_time

D. derivative = (current_error - previous_error) / delta_time

Solution

1. Step 1: Recall derivative formula in PID

   The derivative term measures how fast error changes: (current_error - previous_error) / delta_time.

2. Step 2: Match formula with options

   derivative = (current_error - previous_error) / delta_time matches the correct derivative calculation formula exactly.

3. Final Answer:

   derivative = (current_error - previous_error) / delta_time -> Option D

4. Quick Check:

   Derivative = error change over time [OK]

Hint: Derivative = change in error divided by time [OK]

Common Mistakes:

• Adding errors instead of subtracting
• Multiplying errors incorrectly
• Ignoring division by delta_time

3. Given this Python-like pseudocode for a PID controller update step:

error = target_angle - current_angle
integral += error * dt
derivative = (error - last_error) / dt
output = Kp * error + Ki * integral + Kd * derivative
last_error = error
print(round(output, 2))

If target_angle = 10, current_angle = 7, last_error = 2, integral = 5, dt = 1, Kp = 1, Ki = 0.1, and Kd = 0.05, what is the printed output?

medium

A. 4.65

B. 5.15

C. 3.85

D. 6.00

Solution

1. Step 1: Calculate error, integral, and derivative

   error = 10 - 7 = 3
   integral = 5 + 3 * 1 = 8
   derivative = (3 - 2) / 1 = 1

2. Step 2: Calculate output using PID formula

   output = 1 * 3 + 0.1 * 8 + 0.05 * 1 = 3 + 0.8 + 0.05 = 3.85
   print(round(3.85, 2)) prints 3.85

3. Final Answer:

   3.85 -> Option C

4. Quick Check:

   PID output = 3.85 [OK]

Hint: Calculate error, integral, derivative stepwise [OK]

Common Mistakes:

• Forgetting to update integral before output
• Mixing up last_error and error in derivative
• Rounding errors or ignoring dt

4. Identify the error in this PID controller snippet and choose the correct fix:

integral = 0
dt = 0.1
error = target - current
integral += error
output = Kp * error + Ki * integral * dt + Kd * (error - last_error) / dt

medium

A. Multiply integral by dt before adding error: integral += error * dt

B. Remove dt multiplication from Ki term: output = Kp * error + Ki * integral + Kd * (error - last_error) / dt

C. Divide integral by dt in output: output = Kp * error + Ki * (integral / dt) + Kd * (error - last_error) / dt

D. Add dt to derivative term: output = Kp * error + Ki * integral * dt + Kd * (error - last_error) * dt

Solution

1. Step 1: Check integral update

   Integral should accumulate error over time, so multiply error by dt when adding: integral += error * dt.

2. Step 2: Verify output formula

   Ki term multiplies integral, which already includes dt, so no extra dt needed in output.

3. Final Answer:

   Multiply integral by dt before adding error: integral += error * dt -> Option A

4. Quick Check:

   Integral update needs dt multiplication [OK]

Hint: Integral sums error over time, multiply error by dt [OK]

Common Mistakes:

• Adding error directly to integral without dt
• Multiplying integral by dt twice
• Changing derivative term incorrectly

5. You want to tune a PID controller to make a drone respond faster but avoid overshooting. Which tuning approach is best?

hard

A. Increase Kp a lot, keep Ki and Kd low

B. Increase Kd, keep Kp moderate, reduce Ki

C. Increase Kp moderately, increase Ki slightly, decrease Kd

D. Set all constants to zero and increase gradually

Solution

1. Step 1: Understand tuning effects

   Kp controls speed of response, Ki removes steady error, Kd reduces overshoot by damping.

2. Step 2: Choose tuning to speed up and reduce overshoot

   Increasing Kd helps reduce overshoot, moderate Kp keeps response fast but stable, reducing Ki avoids windup.

3. Final Answer:

   Increase Kd, keep Kp moderate, reduce Ki -> Option B

4. Quick Check:

   More Kd reduces overshoot, moderate Kp speeds response [OK]

Hint: Use Kd to reduce overshoot, moderate Kp for speed [OK]

Common Mistakes:

• Increasing Kp too much causing oscillations
• Ignoring Kd's role in damping
• Setting all constants to zero initially