Model predictive control is a well established control technology for trajectory tracking. Its use requires the availability of an accurate model of the plant, but obtaining such a model is often time consuming and costly. Data-Enabled Predictive Control (DeePC) addresses this shortcoming in the linear time-invariant setting, by skipping the model building step and instead relying directly on input-output data. Unfortunately, many real systems are nonlinear and exhibit strong operating-point dependence. Building on classical linear parameter-varying control, we introduce DeePC-GS, a gain-scheduled extension of DeePC for unknown, regime-varying systems. The key idea is to allow DeePC to switch between different local Hankel matrices -- selected online via a measurable scheduling variable -- thereby uniting classical gain scheduling tools with identification-free, data-driven MPC. We test the effectiveness of our DeePC-GS formulation on a nonlinear ship-steering benchmark, demonstrating that it outperforms state-of-the-art data-driven MPC while maintaining tractable computation.
@article{arxiv.2509.26334,
title = {Gain-Scheduled Data-Enabled Predictive Control: A DeePC Approach for Nonlinear Systems},
author = {Margarita A. Guerrero and Braghadeesh Lakshminarayanan and Cristian R. Rojas},
journal= {arXiv preprint arXiv:2509.26334},
year = {2025}
}