This paper proposes a computational technique based on "deep unfolding" to solving the finite-time maximum hands-off control problem for discrete-time nonlinear stochastic systems. In particular, we seek a sparse control input sequence that stabilizes the system such that the expected value of the square of the final states is small by training a deep neural network. The proposed technique is demonstrated by a numerical experiment.
@article{arxiv.2104.01755,
title = {Temporal Deep Unfolding for Nonlinear Maximum Hands-off Control},
author = {Masako Kishida and Masaki Ogura},
journal= {arXiv preprint arXiv:2104.01755},
year = {2021}
}
Comments
Accepted as a position paper for SICE Annual Conference 2021