  The goal of this work is to enable a team of quadrotors to learn how to
accurately track a desired trajectory while holding a given formation. We solve
this problem in a distributed manner, where each vehicle has only access to the
information of its neighbors. The desired trajectory is only available to one
(or few) vehicles. We present a distributed iterative learning control (ILC)
approach where each vehicle learns from the experience of its own and its
neighbors' previous task repetitions, and adapts its feedforward input to
improve performance. Existing algorithms are extended in theory to make them
more applicable to real-world experiments. In particular, we prove stability
for any causal learning function with gains chosen according to a simple scalar
condition. Previous proofs were restricted to a specific learning function that
only depends on the tracking error derivative (D-type ILC). Our extension
provides more degrees of freedom in the ILC design and, as a result, better
performance can be achieved. We also show that stability is not affected by a
linear dynamic coupling between neighbors. This allows us to use an additional
consensus feedback controller to compensate for non-repetitive disturbances.
Experiments with two quadrotors attest the effectiveness of the proposed
distributed multi-agent ILC approach. This is the first work to show
distributed ILC in experiment.
