An Iterative Learning Scheme for High Performance, Periodic Quadrocopter Trajectories

Quadrocopters allow the execution of high-performance maneuvers under feedback control. However, repeated execution typically leads to a large part of the tracking errors being repeated. This paper evaluates an iterative learning scheme for an experiment where a quadrocopter flies in a circle while balancing an inverted pendulum. The scheme permits the non-causal compensation of periodic errors when executing the circular motion repeatedly, and is based on a Fourier series decomposition of the repeated tracking error and compensation input. The convergence of the learning scheme is shown for the linearized system dynamics. Experiments validate the approach and demonstrate its ability to significantly improve tracking performance.