Optimising a flying robot - Controller optimisation using a genetic algorithm on a real-world robot

This work presents the optimisation of the heading controller of a small flying robot. A genetic algorithm (GA) has been used to tune the proportional, integral, and derivative (PID) parameters of the helicopter's controller. Instead of evaluating each individual's fitness in an artificial simulation, the actual flying robot has been used. The perfonnance of a hand-tuned PID controller is compared to the GA-tuned controller. Tests on the helicopter confirm that the GA's solutions result in a better controller performance. Further more, results suggest that evaluating the GA's individuals on the real flying robot increases the controller's robustness.