PID controller in flight stabilization in Drone Programming - Full Explanation

Introduction

Keeping a drone steady in the air is tricky because wind and movement can push it off balance. To solve this, drones use a special system that helps them adjust quickly and stay stable during flight.

Explanation

Proportional Control

This part looks at how far the drone is from its desired position or angle. It makes a correction that is directly related to this difference, so the bigger the error, the stronger the correction. This helps the drone react quickly to changes.

Proportional control fixes errors by applying a correction proportional to the current difference.

Integral Control

Integral control focuses on the total accumulated error over time. If the drone has been off balance for a while, this part adds extra correction to eliminate any leftover drift. It helps the drone reach the exact target position without steady errors.

Integral control removes long-term steady errors by considering the sum of past errors.

Derivative Control

This part predicts future errors by looking at how fast the error is changing. It helps slow down the correction to avoid overshooting or oscillations. This makes the drone's movements smoother and more stable.

Derivative control anticipates error changes to prevent overshooting and smooth the response.

Combining PID for Flight Stability

The drone combines proportional, integral, and derivative controls to adjust its motors in real time. This combination allows it to quickly correct position errors, eliminate steady drift, and avoid jerky movements. Together, they keep the drone balanced and stable during flight.

The PID controller blends three controls to maintain smooth and accurate drone stability.

Real World Analogy

Imagine balancing a broomstick on your hand. You move your hand quickly to catch it if it starts to fall (proportional). If it keeps leaning a bit, you slowly adjust your hand position to fix that steady lean (integral). And if it suddenly starts falling faster, you prepare to move your hand faster to stop it (derivative).

Proportional Control → Moving your hand quickly to catch the broomstick when it starts to fall

Integral Control → Slowly adjusting your hand position to fix a steady lean of the broomstick

Derivative Control → Preparing to move your hand faster when the broomstick starts falling faster

Combining PID for Flight Stability → Using all these hand movements together to keep the broomstick balanced smoothly

Diagram

Diagram

┌───────────────┐      ┌───────────────┐      ┌───────────────┐
│   Error (e)   │─────▶│ Proportional  │─────▶│               │
│ (Desired -    │      │   Control (P) │      │               │
│  Actual)      │      └───────────────┘      │               │
└───────────────┘                             │               │
                                              │               │
┌───────────────┐      ┌───────────────┐      │               │
│   Error Sum   │─────▶│ Integral Ctrl │─────▶│               │
│ (Accumulated) │      │    (I)        │      │   PID Output  │────▶ Drone Motors
└───────────────┘      └───────────────┘      │               │
                                              │               │
┌───────────────┐      ┌───────────────┐      │               │
│ Error Change  │─────▶│ Derivative    │─────▶│               │
│ (Rate of e)   │      │ Control (D)   │      └───────────────┘
└───────────────┘      └───────────────┘

This diagram shows how the error signal splits into three parts: proportional, integral, and derivative controls, which combine to produce the PID output that adjusts the drone's motors.

Key Facts

Proportional Control → Corrects errors based on the current difference between desired and actual values.

Integral Control → Corrects accumulated past errors to remove steady-state drift.

Derivative Control → Predicts future errors by measuring the rate of change of the error.

PID Controller → A control system combining proportional, integral, and derivative parts to maintain stability.

Flight Stabilization → The process of keeping a drone balanced and steady during flight.

Common Confusions

Believing proportional control alone can keep the drone perfectly stable.

Believing proportional control alone can keep the drone perfectly stable. Proportional control reacts to current errors but cannot fix steady drift; integral control is needed to remove persistent errors.

Thinking derivative control speeds up corrections.

Thinking derivative control speeds up corrections. Derivative control actually slows down corrections to prevent overshoot and oscillations by anticipating error changes.

Summary

A PID controller helps drones stay stable by combining three types of corrections: proportional, integral, and derivative.

Proportional control reacts to current errors, integral control fixes steady drift, and derivative control smooths movements by predicting changes.

Together, these controls adjust the drone's motors to keep it balanced and steady during flight.

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