Gain-Scheduled Data-Enabled Predictive Control: A DeePC Approach for Nonlinear Systems
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| Format: | Preprint |
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2025
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| _version_ | 1866912618210394112 |
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| author | Guerrero, Margarita A. Lakshminarayanan, Braghadeesh Rojas, Cristian R. |
| author_facet | Guerrero, Margarita A. Lakshminarayanan, Braghadeesh Rojas, Cristian R. |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_26334 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Gain-Scheduled Data-Enabled Predictive Control: A DeePC Approach for Nonlinear Systems Guerrero, Margarita A. Lakshminarayanan, Braghadeesh Rojas, Cristian R. Optimization and Control 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. |
| title | Gain-Scheduled Data-Enabled Predictive Control: A DeePC Approach for Nonlinear Systems |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2509.26334 |