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

Meindl M, Molinari F, Lehmann D, Seel T (2021)

Publication Type: Journal article

Publication year: 2021

Journal

IEEE Transactions on Control Systems Technology Institute of Electrical and Electronics Engineers (IEEE)

DOI: 10.1109/TCST.2021.3109646

Abstract

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.

Authors with CRIS profile

Thomas Seel Professur für Intelligente Sensomotorische Systeme

Involved external institutions

Hochschule Karlsruhe – Technik und Wirtschaft DE Germany (DE) Technische Universität Berlin DE Germany (DE)

How to cite

APA:

Meindl, M., Molinari, F., Lehmann, D., & Seel, T. (2021). Collective Iterative Learning Control: Exploiting Diversity in Multi-Agent Systems for Reference Tracking Tasks. IEEE Transactions on Control Systems Technology. https://doi.org/10.1109/TCST.2021.3109646

MLA:

Meindl, Michael, et al. "Collective Iterative Learning Control: Exploiting Diversity in Multi-Agent Systems for Reference Tracking Tasks." IEEE Transactions on Control Systems Technology (2021).

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