Datamodel-Based Data Selection for Nonlinear Data-Enabled Predictive Control

TL;DR

This paper introduces a data-modeling approach for nonlinear predictive control that learns to select the most relevant data based on control context, improving tracking performance especially with limited data.

Contribution

It proposes a context-dependent data selection method using a datamodel framework, enhancing the effectiveness of data-driven control for nonlinear systems.

Findings

Task-aware data selection outperforms geometry-based heuristics.

Method improves tracking with small data subsets.

Learned influence functions adapt to specific control tasks.

Abstract

Data-Enabled Predictive Control (DeePC) has emerged as a powerful framework for controlling unknown systems directly from input-output data. For nonlinear systems, recent work has proposed selecting relevant subsets of data columns based on geometric proximity to the current operating point. However, such proximity-based selection ignores the control objective: different reference trajectories may benefit from different data even at the same operating point. In this paper, we propose a datamodel-based approach that learns a context-dependent influence function mapping the current initial trajectory and reference trajectory to column importance scores. Adapting the linear datamodel framework from machine learning, we model closed-loop cost as a linear function of column inclusion indicators, with coefficients that depend on the control context. Training on closed-loop simulations, our method captures which data columns actually improve tracking performance for specific control tasks. Experimental results demonstrate that task-aware selection substantially outperforms geometry-based heuristics, particularly when using small data subsets.