Deep Model Predictive Control
| Autor: | Mishra, Prabhat K., Gasparino, Mateus V., Velasquez, Andres E. B., Chowdhary, Girish |
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| Rok vydání: | 2023 |
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| Zdroj: | Conference on Robot Learning (CoRL'22): Workshop on Learning to Adapt and Improve in the Real World, 2022 |
| Druh dokumentu: | Working Paper |
| Popis: | This paper presents a deep learning based model predictive control algorithm for control affine nonlinear discrete time systems with matched and bounded state-dependent uncertainties of unknown structure. Since the structure of uncertainties is not known, a deep neural network (DNN) is employed to approximate the disturbances. In order to avoid any unwanted behavior during the learning phase, a tube based model predictive controller is employed, which ensures satisfaction of constraints and input-to-state stability of the closed-loop states. Comment: arXiv admin note: text overlap with arXiv:2104.07171 |
| Databáze: | arXiv |
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