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PID Control in AI & Artificial Intelligence

Learn about PID Control in this comprehensive AI & Artificial Intelligence tutorial. Master the most important control algorithm in engineering. Explore the Proportional, Integral, and Derivative components, learn the 'Ziegler-Nichols' method for parameter tuning, and understand how to prevent oscillation and overshoot in real-world robotic systems.

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PID Hub

Feedback logic.

Quick Quiz //

Which term is most likely to fix a robot that 'Almost' reaches the goal but stops slightly short every time?


Without control, a robot is just a chaotic mess of metal and motors. The PID controller is the mathematical anchor that keeps the machine on its intended path.

1P: The Present Power

The Proportional (P) component is the primary driver of the system. It calculates the Error (Setpoint - Current State) and applies a force proportional to that error. If a drone is 5 meters too low, the P-term tells the motors to spin much faster. If it's only 0.1 meters low, it applies a tiny nudge. The problem with P-only control is Oscillation: the robot often speeds toward the goal so fast that it overshoots, then has to turn around and speed back, creating a never-ending 'Wobble'.

2D: The Future Brake

The Derivative (D) component is the 'Dampener'. It looks at how fast the error is changing. If the robot is approaching the setpoint very quickly, the D-term sees the 'Velocity' and applies a counter-force (braking) before the robot actually reaches the goal. This effectively 'Predicts' the overshoot and stops it before it happens, allowing for a smooth, exponential approach to the target. In robotics, PD control is often sufficient for high-speed systems like drones.

3I: The Past Memory

The Integral (I) component handles Steady-State Error. Imagine a robot trying to drive up a hill. The P-term might settle at a point where the motor force perfectly balances gravity, but the robot is still 2cm below the target. The I-term sums up the error over time. As long as the robot is even slightly off-target, the 'Integral' value keeps growing until it provides enough extra power to push the robot exactly onto the setpoint. However, too much I-term can lead to Wind-up, a dangerous state where the system over-reacts after being stuck.

?Frequently Asked Questions

Pascual Vila

Pascual Vila

Frontend Instructor // Code Syllabus

Lesson Glossary

[01]PID

Proportional-Integral-Derivative: A control loop mechanism that uses feedback to maintain a desired state.

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The Controller

[02]Error

The difference between the desired setpoint and the actual measured value.

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The Gap

[03]Overshoot

When a system exceeds its target setpoint before settling down.

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Flying Past

[04]Settling Time

The time it takes for the system to reach and stay within a certain range of the setpoint.

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Calm Down Time

[05]Tuning

The process of selecting the Kp, Ki, and Kd constants for optimal system performance.

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Dialing In

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