Automated MIMO Motion Feedforward Control: Efficient Learning through Data-Driven Gradients via Adjoint Experiments and Stochastic Approximation

Leontine Aarnoudse, Tom Oomen

Research output: Contribution to journalConference articleScientificpeer-review

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Abstract

Parameterized feedforward control is at the basis of many successful control applications with varying references. The aim of this paper is to develop an efficient data-driven approach to learn the feedforward parameters for MIMO systems. To this end, a cost criterion is minimized using a stochastic gradient descent algorithm, in which both the search direction and step size are determined through system experiments. In particular, the search direction is chosen as an unbiased estimate of the gradient which is obtained from a single experiment, regardless of the size of the MIMO system. The approach is illustrated using a simulation example, in which it is shown to be superior to a deterministic method in terms of convergence speed and thus experimental cost.

Original languageEnglish
Pages (from-to)125-130
JournalIFAC-PapersOnline
Volume55
Issue number37
DOIs
Publication statusPublished - 2022
Event2nd Modeling, Estimation and Control Conference, MECC 2022 - Jersey City, United States
Duration: 2 Oct 20225 Oct 2022

Keywords

  • Basis functions
  • Data-driven control
  • Feedforward control
  • Iterative learning control
  • MIMO systems

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