PID Control
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Every robot has to make something go where it is told: a wheel to a speed, a drone to a level attitude, an arm to an angle, a heater to a temperature. Sensors tell it how far off it is. The PID controller turns that error into a motor command, and it does the job in most of the machines around you, from 1940s chemical plants to today’s flight controllers.
In this course you start with the intuition (push harder the further you are from the goal), meet the three terms one at a time, then build and tune a real controller in Python. The simulation drives a 20 kg rover 5 m up a ramp with a limited motor, so you can watch P overshoot, D calm it down, I remove the last 17 cm of error and anti-windup stop the integral from running away.
What you will be able to do
- Explain what the P, I and D terms each do and recognise their symptoms on a plot
- Write a discrete PID controller with output limits, anti-windup and a filtered D term
- Tune a loop by hand and with Ziegler-Nichols, reading overshoot, settling time and steady-state error
- Simulate a rover on a ramp in Python and compare P, PD and PID control
Before you start: School algebra and a little Python. No calculus needed: every sum and slope is explained with numbers.
Lessons
- The big idea: push harder the further you are▶ VideoFree preview
- How it was invented and its first uses▶ VideoLocked
- Where it is used today▶ VideoLocked
- The equations▶ VideoLocked
- Worked demo and tuning▶ VideoLocked
- Build your own, step by step▶ VideoLocked
- Python simulation labLocked
- Practice problems and quizLocked