A model-based deep reinforcement learning method applied to finite-horizon optimal control of nonlinear control-affine system-Reference-Cited by-同舟云学术

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A model-based deep reinforcement learning method applied to finite-horizon optimal control of nonlinear control-affine system

Published:2020-03 Issue: Volume:87 Page:166-178
ISSN:0959-1524
Container-title:Journal of Process Control
language:en
Short-container-title:Journal of Process Control

Author:

Kim Jong Woo,Park Byung Jun,Yoo Haeun,Oh Tae Hoon,Lee Jay H.,Lee Jong Min

Funder

National Research Foundation of Korea

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Modeling and Simulation,Control and Systems Engineering

Reference59 articles.

1. Nearly optimal control laws for nonlinear systems with saturating actuators using a neural network HJB approach;Abu-Khalaf;Automatica,2005

2. Fixed final time optimal control approach for bounded robust controller design using HamiltonJacobiâ–Bellman solution;Adhyaru;IET Control Theory Appl.,2009

3. Dynamic optimization of dissipative PDE systems using nonlinear order reduction;Armaou;Chem. Eng. Sci.,2002

4. The explicit linear quadratic regulator for constrained systems;Bemporad;Automatica,2002

5. Dynamic Programming and Optimal Control;Bertsekas,2005

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