Abstract
The goal of this paper is to solve a class of stochastic optimal control problems numerically, in which the state process is governed by an Itô type stochastic differential equation with control process entering both in the drift and the diffusion, and is observed partially. The optimal control of feedback form is determined based on the available observational data. We call this type of control problems the data driven feedback control. The computational framework that we introduce to solve such type of problems aims to find the best estimate for the optimal control as a conditional expectation given the observational information. To make our method feasible in providing timely feedback to the controlled system from data, we develop an efficient stochastic optimization algorithm to implement our computational framework.
| Original language | English |
|---|---|
| Article number | 51 |
| Journal | Journal of Scientific Computing |
| Volume | 85 |
| Issue number | 2 |
| DOIs | |
| State | Published - Nov 2020 |
Funding
This work is partially supported by the Scientific Discovery through Advanced Computing (SciDAC) program funded by U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research through FASTMath Institute and CompFUSE project. The second author also acknowledges support by U.S. National Science Foundation under Contract DMS-1720222. The third author acknowledges the partial support by NSF Grant DMS-1812921.
| Funders | Funder number |
|---|---|
| FASTMath Institute | |
| National Science Foundation | DMS-1720222, 1812921, DMS-1812921 |
| U.S. Department of Energy | |
| Office of Science |
Keywords
- Data driven
- Maximum principle
- Nonlinear filtering
- Stochastic optimal control
- Stochastic optimization