Varying Horizon Learning Economic MPC With Unknown Costs of Disturbed Nonlinear Systems
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866918141174480896 |
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| author | Xiong, Weiliang He, Defeng Du, Haiping Mu, Jianbin |
| author_facet | Xiong, Weiliang He, Defeng Du, Haiping Mu, Jianbin |
| contents | This paper proposes a novel varying horizon economic model predictive control (EMPC) scheme without terminal constraints for constrained nonlinear systems with additive disturbances and unknown economic costs. The general regression learning framework with mixed kernels is first used to reconstruct the unknown cost. Then an online iterative procedure is developed to adjust the horizon adaptively. Again, an elegant horizon-dependent contraction constraint is designed to ensure the convergence of the closed-loop system to a neighborhood of the desired steady state. Moreover, sufficient conditions ensuring recursive feasibility and input-to-state stability are established for the system in closed-loop with the EMPC. The merits of the proposed scheme are verified by the simulations of a continuous stirred tank reactor and a four-tank system in terms of robustness, economic performance and online computational burden. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_11823 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Varying Horizon Learning Economic MPC With Unknown Costs of Disturbed Nonlinear Systems Xiong, Weiliang He, Defeng Du, Haiping Mu, Jianbin Systems and Control 93D15, 93D09 This paper proposes a novel varying horizon economic model predictive control (EMPC) scheme without terminal constraints for constrained nonlinear systems with additive disturbances and unknown economic costs. The general regression learning framework with mixed kernels is first used to reconstruct the unknown cost. Then an online iterative procedure is developed to adjust the horizon adaptively. Again, an elegant horizon-dependent contraction constraint is designed to ensure the convergence of the closed-loop system to a neighborhood of the desired steady state. Moreover, sufficient conditions ensuring recursive feasibility and input-to-state stability are established for the system in closed-loop with the EMPC. The merits of the proposed scheme are verified by the simulations of a continuous stirred tank reactor and a four-tank system in terms of robustness, economic performance and online computational burden. |
| title | Varying Horizon Learning Economic MPC With Unknown Costs of Disturbed Nonlinear Systems |
| topic | Systems and Control 93D15, 93D09 |
| url | https://arxiv.org/abs/2509.11823 |