Machine Learning, Combustion Engines And Real-Time Control @Raspberry_Pi #piday #raspberrypi @Raspberry_Pi
Combustion engine with machine learning algorithm using Raspberry Pi via raspberry.org
What you’re about to watch in the video below is a magnificently physical example of machine learning. Adam Vaughan is controlling an engine with an adaptive Extreme Learning Machine algorithm on his Pi, which predicts homogeneous charge compression ignition (HCCI – if you’re a petrolhead, you won’t have to look that up on Wikipedia like I did to discover that it’s a spark-free way of combusting fuel by putting it under pressure until it goes bang) in real time.
HCCI combustion is hard to predict – it’s near-chaotic – so the algorithm Adam designed has to take a huge number of samples (240,000 per second) to get enough data to learn how the engine behaves and to provide something so close to real-time control that you’d never know the difference. (It’s incredibly close to real time – there’s about 300 microseconds – that’s microseconds, or one millionth of a second; not milliseconds, which are a thousandth of a second – of latency here.)
The Pi is recording data about pressure in each of the engine’s cylinders, about the angle of the crank and about heat release – and on the back of that, it’s subsequently controlling the engine in real time over a controller area network.
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8-6-2021 (August 6, 2021) is the Snakiest day of the year and it’s also this year’s CircuitPython Day! The day highlights all things CircuitPython and Python on Hardware. See you there!
Stop breadboarding and soldering – start making immediately! Adafruit’s Circuit Playground is jam-packed with LEDs, sensors, buttons, alligator clip pads and more. Build projects with Circuit Playground in a few minutes with the drag-and-drop MakeCode programming site, learn computer science using the CS Discoveries class on code.org, jump into CircuitPython to learn Python and hardware together, TinyGO, or even use the Arduino IDE. Circuit Playground Express is the newest and best Circuit Playground board, with support for CircuitPython, MakeCode, and Arduino. It has a powerful processor, 10 NeoPixels, mini speaker, InfraRed receive and transmit, two buttons, a switch, 14 alligator clip pads, and lots of sensors: capacitive touch, IR proximity, temperature, light, motion and sound. A whole wide world of electronics and coding is waiting for you, and it fits in the palm of your hand.