Data-driven predictive control with improved performance using segmented trajectories

TL;DR

This paper introduces a segmentation approach in data-driven predictive control based on Willems' lemma, improving long-horizon tracking accuracy under disturbances and noise.

Contribution

It proposes a novel segmentation of prediction trajectories that enhances control performance and robustness in data-driven control methods.

Findings

Reduced tracking error with segmentation over longer horizons

Consistent performance across various prediction horizons

Effective application to building energy management

Abstract

A class of data-driven control methods has recently emerged based on Willems' fundamental lemma. Such methods can ease the modelling burden in control design but can be sensitive to disturbances acting on the system under control. In this paper, we propose a restructuring of the problem to incorporate segmented prediction trajectories. The proposed segmentation leads to reduced tracking error for longer prediction horizons in the presence of unmeasured disturbance and noise when compared to an unsegmented formulation. The performance characteristics are illustrated in a set-point tracking case study in which the segmented formulation enables more consistent performance over a wide range of prediction horizons. The method is then applied to a building energy management problem using a detailed simulation environment. The case studies show that good tracking performance is achieved for a range of horizon choices, whereas performance degrades with longer horizons without segmentation.