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.
@article{arxiv.2302.13558,
title = {Deep Model Predictive Control},
author = {Prabhat K. Mishra and Mateus V. Gasparino and Andres E. B. Velasquez and Girish Chowdhary},
journal= {arXiv preprint arXiv:2302.13558},
year = {2023}
}
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arXiv admin note: text overlap with arXiv:2104.07171