Distributed Iterative Learning Control for a Team of Quadrotors

TL;DR

This paper introduces a novel distributed iterative learning control method enabling a team of quadrotors to learn trajectory tracking and formation maintenance collaboratively, with proven stability and successful real-world experiments.

Contribution

It extends existing ILC algorithms to be more applicable to real-world multi-quadrotor systems, allowing distributed learning with stability guarantees and disturbance compensation.

Findings

Successful experimental validation with two quadrotors.

Extended stability proof for general causal learning functions.

Enhanced performance through additional consensus feedback control.

Abstract

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.