{"ID":2862834,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.26334","arxiv_id":"2509.26334","title":"Gain-Scheduled Data-Enabled Predictive Control: A DeePC Approach for Nonlinear Systems","abstract":"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.","short_abstract":"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 s...","url_abs":"https://arxiv.org/abs/2509.26334","url_pdf":"https://arxiv.org/pdf/2509.26334v1","authors":"[\"Margarita A. Guerrero\",\"Braghadeesh Lakshminarayanan\",\"Cristian R. Rojas\"]","published":"2025-09-30T14:44:02Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[]","has_code":false}