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eess.SY2021★ 21 cited
Collective Iterative Learning Control: Exploiting Diversity in Multi-Agent Systems for Reference Tracking Tasks
Michael Meindl, Fabio Molinari, Dustin Lehmann +1
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 coll…
eess.SY2020★ 6 cited
Overcoming Output Constraints in Iterative Learning Control Systems by Reference Adaptation
Michael Meindl, Fabio Molinari, Jörg Raisch +1
Iterative Learning Control (ILC) schemes can guarantee properties such as asymptotic stability and monotonic error convergence, but do not, in general, ensure adherence to output c…