  This paper considers a group of autonomous agents learning to track the same
given reference trajectory in a possibly small number of trials. We propose a
novel collective learning control method (namely, CILC) that combines Iterative
Learning Control (ILC) with a collective input update strategy. We derive
conditions for desirable convergence properties of such systems. We show that
the proposed method allows the collective to combine the advantages of the
agents' individual learning strategies and thereby overcomes trade-offs and
limitations of single-agent ILC. This benefit is leveraged by designing a
heterogeneous collective, i.e., a different learning law is assigned to each
agent. All theoretical results are confirmed in simulations and experiments
with two-wheeled-inverted-pendulums robots (TWIPRs) that jointly learn to
perform a desired maneuver.
