Conjugate Gradient MIMO Iterative Learning Control Using Data-Driven Stochastic Gradients

Conference paper (2021)

Authors

Leontine Aarnoudse Eindhoven University of Technology
Tom Oomen Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering , Eindhoven University of Technology
Research Group
Team Jan-Willem van Wingerden (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2021 Leontine Aarnoudse, T.A.E. Oomen
DOI: https://doi.org/10.1109/CDC45484.2021.9683362
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Published Date

2021

Language

English

Reuse Rights

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Jan-Willem van Wingerden

Abstract

Data-driven iterative learning control can achieve high performance for systems performing repeating tasks without the need for modeling. The aim of this paper is to develop a fast data-driven method for iterative learning control that is suitable for massive MIMO systems through the use of efficient unbiased gradient estimates. A stochastic conjugate gradient descent algorithm is developed that uses dedicated experiments to determine the conjugate search direction and optimal step size at each iteration. The approach is illustrated on a multivariable example, and it is shown that the method is superior to both the earlier stochastic gradient descent and deterministic conjugate gradient descent methods.

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