  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.

Systems and Control;Systems and Control