Gain-Scheduling Data-Enabled Predictive Control for Nonlinear Systems with Linearized Operating Regions

TL;DR

A Gain-Scheduled Data-Enabled Predictive Control framework for nonlinear systems based on multiple locally linear data representations that maintains the original DeePC problem structure and can achieve reduced computational complexity by requiring only short, locally informative data sequences.

Abstract

This paper presents a Gain-Scheduled Data-Enabled Predictive Control (GS-DeePC) framework for nonlinear systems based on multiple locally linear data representations. Instead of relying on a single global Hankel matrix, the operating range of a measurable scheduling variable is partitioned into regions, and regional Hankel matrices are constructed from persistently exciting data. To ensure smooth transitions between linearization regions and suppress region-induced chattering, composite regions are introduced, merging neighboring data sets and enabling a robust switching mechanism. The proposed method maintains the original DeePC problem structure and can achieve reduced computational complexity by requiring only short, locally informative data sequences. Extensive experiments on a nonlinear DC-motor with an unbalanced disc demonstrate the significantly improved control performance compared to standard DeePC.