From a Frequency-Domain Willems' Lemma to Data-Driven Predictive Control

Abstract: Willems' fundamental lemma has recently received an impressive amount of attention from the (data-driven) control community. In this paper, we formulate a version of this celebrated result based on frequency-domain data. In doing so, we bridge the gap between recent developments in data-driven analysis and control, and the readily-available techniques and extensive expertise for non-parametric frequency-domain identification in academia and industry. In addition, we generalize our results to allow multiple frequency-domain data sets to be carefully combined to form a sufficiently rich data set. Building on these results, we propose a data-driven predictive control scheme based on measured frequency-domain data of the plant. This novel scheme provides a frequency-domain counterpart of the well-known data-enabled predictive control scheme DeePC based on time-domain data. We prove that, under appropriate conditions, the new frequency-domain data-driven predictive control (FreePC) scheme is equivalent to the corresponding DeePC scheme, and we demonstrate the benefits of FreePC and the use of frequency-domain data in a numerical case study. These benefits include the ability to collect data in closed loop with a pre-stabilizing controller, dealing with noisy data, without increasing computational complexity, and intuitively visualizing the uncertainty in the frequency-domain data. In addition, we further showcase the potential of our frequency-domain Willems' fundamental lemma in applications to data-driven simulation, and the linear-quadratic regulator (LQR) problem. Finally, we show that our results can be used to evaluate the transfer function of the system at a desired frequency based on a finite amount of frequency-domain data.