I’ve implemented PID control far too many times now, and I’ve never been that happy with the results.

It is simple to implement, but tricky to tune.  My thick control theory book says that 95% of commercial in-use PID systems are sub-optimally tuned.  And I can believe it.

I’ve been recently playing with other systems, and currently I really like Model Predictive Control.  It does require some significant compute power, which was a big problem when it was invented, but not so much any more.   (As a side point, I’d like to implement MPC in tensorflow.  That would be cool, and could probably be done in a weekend).

I wrote a PID control system for a self driving car.  It is dumbly auto-tuned, so to speak, by a simple twiddle method.  It tests out a particular set of PID values for one minute, resets the track, then picks a new set of PID values based on the best seen values, changed slightly.

The results are…  very underwhelming.

 

The same driving but using Model Predictive Control, below, starts to look a lot nicer.  For reference, the PID control took a total of two days to implement, and the MPC took a total of three days.   The MPC version also has an additional complication – there’s a simulated delay of 100ms.

But I’m very pleased with the results.  It’s a bit jerky here due to random latency variations, made worse by the screen recording software.  I’ve got some ideas on trying to solve that by timestamping, but I don’t think I’ll have time to work on it.

I would love to try applying this MPC system to a Quadcopter.  I’ve written quite a bit previously about latency problems with PID in a quadcopter:  (In this video, the ufo-shaped quadcopter is attempting to move to, and maintain, a fixed position above the chair.  Ignore the blue man 🙂 )