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| Hauptseite > Publikationsdatenbank > End-to-End Reinforcement Learning of Koopman Models for Economic Nonlinear Model Predictive Control > print |
| Hauptseite > Publikationsdatenbank > End-to-End Reinforcement Learning of Koopman Models for Economic Nonlinear Model Predictive Control > print |
| 001 | 1021653 | ||
| 005 | 20240712112903.0 | ||
| 024 | 7 | _ | |a 10.48550/ARXIV.2308.01674 |2 doi |
| 024 | 7 | _ | |a 10.34734/FZJ-2024-00909 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2024-00909 |
| 100 | 1 | _ | |a Mayfrank, Daniel |0 P:(DE-Juel1)192151 |b 0 |u fzj |
| 245 | _ | _ | |a End-to-End Reinforcement Learning of Koopman Models for Economic Nonlinear Model Predictive Control |
| 260 | _ | _ | |c 2023 |b arXiv |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1706103588_7712 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a WORKING_PAPER |2 ORCID |
| 336 | 7 | _ | |a Electronic Article |0 28 |2 EndNote |
| 336 | 7 | _ | |a preprint |2 DRIVER |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Working Paper |2 DataCite |
| 520 | _ | _ | |a (Economic) nonlinear model predictive control ((e)NMPC) requires dynamic system models that are sufficiently accurate in all relevant state-space regions. These models must also be computationally cheap enough to ensure real-time tractability. Data-driven surrogate models for mechanistic models can be used to reduce the computational burden of (e)NMPC; however, such models are typically trained by system identification for maximum average prediction accuracy on simulation samples and perform suboptimally as part of actual (e)NMPC. We present a method for end-to-end reinforcement learning of dynamic surrogate models for optimal performance in (e)NMPC applications, resulting in predictive controllers that strike a favorable balance between control performance and computational demand. We validate our method on two applications derived from an established nonlinear continuous stirred-tank reactor model. We compare the controller performance to that of MPCs utilizing models trained by the prevailing maximum prediction accuracy paradigm, and model-free neural network controllers trained using reinforcement learning. We show that our method matches the performance of the model-free neural network controllers while consistently outperforming models derived from system identification. Additionally, we show that the MPC policies can react to changes in the control setting without retraining. |
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| 650 | _ | 7 | |a Machine Learning (cs.LG) |2 Other |
| 650 | _ | 7 | |a Systems and Control (eess.SY) |2 Other |
| 650 | _ | 7 | |a FOS: Computer and information sciences |2 Other |
| 650 | _ | 7 | |a FOS: Electrical engineering, electronic engineering, information engineering |2 Other |
| 700 | 1 | _ | |a Mitsos, Alexander |0 P:(DE-Juel1)172025 |b 1 |u fzj |
| 700 | 1 | _ | |a Dahmen, Manuel |0 P:(DE-Juel1)172097 |b 2 |e Corresponding author |u fzj |
| 773 | _ | _ | |a 10.48550/ARXIV.2308.01674 |
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