On the Role of Models in Learning Control: Actor-Critic Iterative Learning Control

Maurice Poot (Corresponding author), Jim Portegies, Tom Oomen

Research output: Contribution to journalConference articlepeer-review

5 Citations (Scopus)
99 Downloads (Pure)


Learning from data of past tasks can substantially improve the accuracy of mechatronic systems. Often, for fast and safe learning a model of the system is required. The aim of this paper is to develop a model-free approach for fast and safe learning for mechatronic systems. The developed actor-critic iterative learning control (ACILC) framework uses a feedforward parameterization with basis functions. These basis functions encode implicit model knowledge and the actor-critic algorithm learns the feedforward parameters without explicitly using a model. Experimental results on a printer setup demonstrate that the developed ACILC framework is capable of achieving the same feedforward signal as preexisting model-based methods without using explicit model knowledge.
Original languageEnglish
Pages (from-to)1450-1455
Number of pages6
Issue number2
Publication statusPublished - 2020
Event21st World Congress of the International Federation of Aufomatic Control (IFAC 2020 World Congress) - Berlin, Germany
Duration: 12 Jul 202017 Jul 2020
Conference number: 21


  • Feedforward control
  • Iterative learning control
  • Learning control
  • Reinforcement learning
  • Function approximation
  • Model-free control
  • Model-based control
  • Markov decision problems
  • Learning algorithms
  • Function-approximation


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