We address the design and synthesis of optimal control strategies for high-dimensional stochastic dynamical systems. Such systems may be deterministic nonlinear systems evolving from random initial states, or systems driven by random parameters or processes. The objective is to provide a validated new computational capability for optimal control which will be achieved more efficiently than current state-of-the-art methods. The new framework utilizes direct single or multi-shooting discretization, and is based on efficient vectorized gradient computation with adaptable memory management. The algorithm is demonstrated to be scalable to high-dimensional nonlinear control systems with random initial condition and unknown parameters.
@article{arxiv.1909.07960,
title = {A new scalable algorithm for computational optimal control under uncertainty},
author = {Panos Lambrianides and Qi Gong and Daniele Venturi},
journal= {arXiv preprint arXiv:1909.07960},
year = {2020}
}