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2024-05-18 | 🕹️ PID 🫛 Pod 🏁 Racer

CodinGame: Mad Pod Racing

Exciting!
I was very interested when I learned about PID controllers in college, but I’ve never gotten around to implementing one.
I’ve finally found an application that’s literally asking for it!
I wrote a simple, intuitive control algorithm to drive my bot last night.
I’m impressed with the performance of such a similar algorithm!
But I’m excited to apply some rigor and see how much juice we can squeeze from theory.
I revisit one of my favorites YouTube channels for a refresher on PID application
PID controllers are nice because they are simple and effective.
We just need to determine a couple of modeling assumptions to get started.
A PID controller aims to minimize an error signal by manipulating a control variable.
For this application, a good initial choice for the control variable is obvious: thrust!
Deciding on an error signal to minimize is more nuanced.
In my intuitive control algorithm, I used the angle between my pop’s aim and the next checkpoint as a sort of error signal.
I reduce the thrust proportionally to this angle.
If the angle is zero, we’re pointed at the target, so it’s full speed ahead!
If there’s a big angle between our pod’s aim and the checkpoint, we’re not headed in the right direction, so we may want to slow down.
That was my thinking anyway.
I suspect there may be better choices for error signals, as the point of a race isn’t just to point in the right direction.
But I think this will serve as a good enough starting point while we implement our first PID controller!
We can always iterate and tune when we have the infrastructure in place.
Also, I suspect that my intuitive algorithm is basically a propotional controller (the P in PID) and it works pretty well already.
Let’s see how much it improves with the addition of integral and derivative signals (I and D, respectively).

A Google search led me to a nice, simple reference implementation to get started.

def PID(Kp, Ki, Kd, setpoint, measurement):  
    global time, integral, time_prev, e_prev  
  
    # Value of offset - when the error is equal zero  
    offset = 320  
      
    # PID calculations  
    e = setpoint - measurement  
          
    P = Kp*e  
    integral = integral + Ki*e*(time - time_prev)  
    D = Kd*(e - e_prev)/(time - time_prev)  
  
    # calculate manipulated variable - MV   
    MV = offset + P + integral + D  
      
    # update stored data for next iteration  
    e_prev = e  
    time_prev = time  
    return MV  

Any easy read at 11 lines of Python!
This should only take a few minutes to translate, and maybe a half hour or so to get up and running in our pod racer.