# Collective Iterative Learning Control: Exploiting Diversity in Multi-Agent Systems for Reference Tracking Tasks

arXiv: https://arxiv.org/abs/2104.07620 · HTML: https://mlsanity.com/paper/2104.07620/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 33 references that are in the map.
- First posted on arXiv 15 Apr 2021.

## Builds on (most cited first, 3 of 3)

- [Distributed Iterative Learning Control for a Team of Quadrotors](https://mlsanity.com/paper/1603.05933/) (2016)
- [Automation Of Road Intersections Using Consensus-based Auction Algorithms](https://mlsanity.com/paper/1804.10411/) (2018)
- [Overcoming Output Constraints in Iterative Learning Control Systems by Reference Adaptation](https://mlsanity.com/paper/2002.00662/) (2020)

## Built on by (most cited first, 1 of 1)

- [Structured Diversity Control: A Dual-Level Framework for Group-Aware Multi-Agent Coordination](https://mlsanity.com/paper/2506.18651/) (2025)

## Abstract (arXiv)

Multi-agent systems (MASs) can autonomously learn to solve previously unknown tasks by means of each agent's individual intelligence as well as by collaborating and exploiting collective intelligence. This article 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 that combines iterative learning control (ILC) with a collective 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 achieved 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-pendulum robots (TWIPRs) that jointly learn to perform the desired maneuver.