A Frequency Domain Iterative Feed-Forward Learning Scheme for High Performance Periodic Quadrocopter Maneuvers

Quadrocopters exhibit complex high-speed flight dynamics, and the accurate modeling of these dynamics has proven difficult. Due to the use of simplified models in the design of feedback control algorithms, the execution of high-performance flight maneuvers under pure feedback control typically leads to large tracking errors. This paper investigates an iterative learning scheme aimed at the non-causal compensation of repeatable trajectory tracking errors over the course of multiple executions of periodic maneuvers. The learning is carried out in the frequency domain and uses a simplified model of the closed-loop dynamics of quadrocopter and feedback controller. The resulting algorithm requires little computational power and memory, and its convergence is shown for the nominal model. This paper further introduces a time-scaling method that allows the initial learning to occur at reduced speeds, thus extending the applicability of the algorithm for high performance maneuvers. The presented algorithms are validated in experiments, with a quadrocopter flying a figure-eight maneuver at high speed.