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

TL;DR

This paper introduces a gain-scheduling data-enabled predictive control framework for nonlinear systems, utilizing multiple local linear models and regional data representations to enhance control performance and computational efficiency.

Contribution

It proposes a novel GS-DeePC approach that partitions the operating range, constructs regional Hankel matrices, and ensures smooth transitions between regions, improving control of nonlinear systems.

Findings

Enhanced control performance on nonlinear DC-motor system

Reduced computational complexity with local data sequences

Effective handling of nonlinearities through regional modeling

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.