I guess you can think of it as a "self-tuning" PID controller. It "learns" how to ride a bike.
If it were really "just" a PID, you would need some arbitrarily complex procedure involving a human for tuning all the parameters?
If it were really "just" a PID, you would need some arbitrarily complex procedure involving a human for tuning all the parameters?
From the paper: "The three constants c_i need to be set by the implementor"
Under "Future Directions": "One obvious thing to do with this system is to have it learn and tune its parameters with experience." The work presented in the paper does not do this.
"This network was designed in an ad-hoc, if traditional, way. First, a human tried to control the bicycle with the simulator. After many attempts, the human finally became a somewhat skilled operator of the bicycle ... the human at this point was able to describe the key parameters which were being attended to, and based on this, the two-neuron network was designed." By "designed," the author means "the three constants were selected."
This sounds like the "arbitrarily complex procedure" you are looking for.
This is one of the classic texts: https://www.amazon.com/Adaptive-Control-Karl-Johan-Astrom/dp...
In short, many techniques that look a lot like modern machine learning have been in use for decades in control theory for tasks exactly like you describe (e.g. automatic tuning of PIDs). If you want to dig into this a bit deeper you might look up the "MIT Rule", which is a gradient style approach for dynamically tuning (and re-tuning) adaptive control systems, and is fairly common for PID tuning.