  We consider the problem of data-driven predictive control for an unknown
discrete-time linear time-periodic (LTP) system of known period. Our proposed
strategy generalizes both Data-enabled Predictive Control (DeePC) and Subspace
Predictive Control (SPC), which are established data-driven control techniques
for linear time-invariant (LTI) systems. The approach is supported by an
extensive theoretical development of behavioral systems theory for LTP systems,
culminating in a generalization of the fundamental lemma. Our algorithm
produces results identical to standard Model Predictive Control (MPC) for
deterministic LTP systems. Robustness of the algorithm to noisy data is
illustrated via simulation of a regularized version of the algorithm applied to
a stochastic multi-input multi-output LTP system.
