Sebastian Thrun presented a simple algorithm for tuning PID in his "How to Program a Robotic Car" class. It's called "twiddle", he describes it here: https://www.youtube.com/watch?v=2uQ2BSzDvXs

Twiddle is very prone to finding local minima--this means that you could come up with a set of three constants that are okay, but not optimal for the situation. The problem of tuning PID constants is a subset of a more general search problem to find certain parameters to maximize utility (in this case, minimizing error of the PID algorithm). You can look into other general solutions to this problem, like hill-climbing, simulated annealing, genetic algorithms, etc. that might end up finding more optimal solutions.

Sebastian Thrun presented a simple algorithm for tuning PID in his "How to Program a Robotic Car" class. It's called "twiddle", he describes it here.

Twiddle is very prone to finding local minima--this means that you could come up with a set of three constants that are okay, but not optimal for the situation. The problem of tuning PID constants is a subset of a more general search problem to find certain parameters to maximize utility (in this case, minimizing error of the PID algorithm). You can look into other general solutions to this problem, like hill-climbing, simulated annealing, genetic algorithms, etc. that might end up finding more optimal solutions.