Unmatched uncertainty mitigation through neural network supported model predictive control
Abstract
This paper presents a deep learning based model predictive control (MPC) algorithm for systems with unmatched and bounded state-action dependent uncertainties of unknown structure. We utilize a deep neural network (DNN) as an oracle in the underlying optimization problem of learning based MPC (LBMPC) to estimate unmatched uncertainties. Generally, non-parametric oracles such as DNN are considered difficult to employ with LBMPC due to the technical difficulties associated with estimation of their coefficients in real time. We employ a dual-timescale adaptation mechanism, where the weights of the last layer of the neural network are updated in real time while the inner layers are trained on a slower timescale using the training data collected online and selectively stored in a buffer. Our results are validated through a numerical experiment on the compression system model of jet engine. These results indicate that the proposed approach is implementable in real time and carries the theoretical guarantees of LBMPC.
Cite
@article{arxiv.2304.11315,
title = {Unmatched uncertainty mitigation through neural network supported model predictive control},
author = {Mateus V. Gasparino and Prabhat K. Mishra and Girish Chowdhary},
journal= {arXiv preprint arXiv:2304.11315},
year = {2023}
}