Many applications require solving non-linear control problems that are classically not well behaved. This paper develops a simple and efficient chattering algorithm that learns near optimal decision policies through an open-loop feedback strategy. The optimal control problem reduces to a series of linear optimization programs that can be easily solved to recover a relaxed optimal trajectory. This algorithm is implemented on a real-time enterprise scheduling and control process.
@article{arxiv.1703.06485,
title = {Near Optimal Hamiltonian-Control and Learning via Chattering},
author = {Peeyush Kumar and Wolf Kohn and Zelda B. Zabinsky},
journal= {arXiv preprint arXiv:1703.06485},
year = {2017}
}