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The goal of this work is to enable a team of quadrotors to learn how toaccurately track a desired trajectory while holding a given formation. We solvethis problem in a distributed manner, where each vehicle has only access to theinformation 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 itsneighbors' previous task repetitions, and adapts its feedforward input toimprove performance. Existing algorithms are extended in theory to make themmore applicable to real-world experiments. In particular, we prove stabilityfor any causal learning function with gains chosen according to a simple scalarcondition. Previous proofs were restricted to a specific learning function thatonly depends on the tracking error derivative (D-type ILC). Our extensionprovides more degrees of freedom in the ILC design and, as a result, betterperformance can be achieved. We also show that stability is not affected by alinear dynamic coupling between neighbors. This allows us to use an additionalconsensus feedback controller to compensate for non-repetitive disturbances.Experiments with two quadrotors attest the effectiveness of the proposeddistributed multi-agent ILC approach. This is the first work to showdistributed ILC in experiment.

Distributed Iterative Learning Control for a Team of Quadrotors