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arXiv · 1807.01164

A Decoupled Data Based Approach to Stochastic Optimal Control Problems

Dan Yu·Mohammandhussen Rafieisakhaei·Suman Chakravorty
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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.

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Dan Yu, Mohammandhussen Rafieisakhaei, Suman Chakravorty. 2018-09-10. A Decoupled Data Based Approach to Stochastic Optimal Control Problems. https://arxiv.org/abs/1807.01164

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