Performing and extending aggressive maneuvers using iterative learning control

This paper presents an algorithm to iteratively perform an aggressive maneuver, i.e. drive a system quickly from one state to another. A simple model which captures the essential features of the system is used to compute the reference trajectory as the solution of an optimal control problem. Based on a lifted domain description of that same model an iterative learning controller is synthesized by solving a linear least-squares problem. The controller adjusts a feedforward signal using the results of experiments with the system. The non-causality of the approach makes it possible to anticipate recurring disturbances. Computational requirements are modest, allowing controller update in real-time. The experience gained from successful maneuvers can be used to adjust the model, which significantly reduces transients when performing similar motions. The algorithm is successfully applied to a real quadrotor unmanned aerial vehicle. The results are presented and discussed.

Research highlights
► Data-based algorithm to control non-linear system.
► Simple model captures essential system properties.
► Iterative learning controller synthesized in lifted domain.
► Experiments performed on real quadrotor UAV.
► Extension of maneuver after nonlinear parameter adaptation.