International Journal of Robust and Nonlinear Control, Volume (34), No (8), Year (2024-5) , Pages (5419-5437)

Title : ( Off‐policy reinforcement learning algorithm for robust optimal control of uncertain nonlinear systems )

Authors: Ali Amirparast , Seyed Kamal Hosseini Sani ,

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Abstract

In this article an optimal control scheme is proposed to solve robust control problem for matched and unmatched system. In the proposed optimal approach the value functions are designed such that the obtained optimal control law guarantees asymptotic stability of the uncertain nonlinear system. Since the proposed robust optimal control problem is not straightforward to solve, an off-policy reinforcement-learning algorithm based on neural networks approximation is developed to obtain robust optimal control law iteratively. The robust control law for matched uncertain systems can be achieved via proposed off-policy learning algorithm without requiring exact knowledge of system’s dynamics. The advantages of the proposed robust optimal controller are verified by comparative simulations on an uncertain model of a car suspension system and a mathematical nonlinear model.

Keywords

, off-policy learning, optimal control theory, parametric uncertainty, reinforcement learning, robust control theory
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@article{paperid:1102808,
author = {Amirparast, Ali and Hosseini Sani, Seyed Kamal},
title = {Off‐policy reinforcement learning algorithm for robust optimal control of uncertain nonlinear systems},
journal = {International Journal of Robust and Nonlinear Control},
year = {2024},
volume = {34},
number = {8},
month = {May},
issn = {1049-8923},
pages = {5419--5437},
numpages = {18},
keywords = {off-policy learning; optimal control theory; parametric uncertainty; reinforcement learning; robust control theory},
}

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%0 Journal Article
%T Off‐policy reinforcement learning algorithm for robust optimal control of uncertain nonlinear systems
%A Amirparast, Ali
%A Hosseini Sani, Seyed Kamal
%J International Journal of Robust and Nonlinear Control
%@ 1049-8923
%D 2024

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