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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