A new scalable algorithm for computational optimal control under uncertainty

TL;DR

This paper introduces a scalable computational algorithm for optimal control of high-dimensional stochastic systems, improving efficiency and applicability to systems with randomness and uncertainty.

Contribution

It presents a novel framework utilizing direct discretization and vectorized gradient computation, enabling efficient control synthesis for complex stochastic systems.

Findings

Algorithm is scalable to high-dimensional systems

Demonstrates efficiency over existing methods

Applicable to systems with random initial states and unknown parameters

Abstract

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.