A Decoupled Data Based Approach to Stochastic Optimal Control Problems
Abstract
This paper studies the stochastic optimal control problem for systems with unknown dynamics. A novel decoupled data based control (D2C) approach is proposed, which solves the problem in a decoupled "open loop-closed loop" fashion that is shown to be near-optimal. First, an open-loop deterministic trajectory optimization problem is solved using a black-box simulation model of the dynamical system using a standard nonlinear programming (NLP) solver. Then a Linear Quadratic Regulator (LQR) controller is designed for the nominal trajectory-dependent linearized system which is learned using input-output experimental data. Computational examples are used to illustrate the performance of the proposed approach with three benchmark problems.
Cite
@article{arxiv.1807.01164,
title = {A Decoupled Data Based Approach to Stochastic Optimal Control Problems},
author = {Dan Yu and Mohammandhussen Rafieisakhaei and Suman Chakravorty},
journal= {arXiv preprint arXiv:1807.01164},
year = {2018}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1711.01167, arXiv:1705.09761