Please use this identifier to cite or link to this item:
Title: Adaptive optimal output tracking of continuous-time systems via output-feedback-based reinforcement learning
Authors: Chen, Ci
Xie, Lihua
Xie, Kan
Lewis, Frank L.
Xie. Shengli
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Chen, C., Xie, L., Xie, K., Lewis, F. L. & Xie. Shengli (2022). Adaptive optimal output tracking of continuous-time systems via output-feedback-based reinforcement learning. Automatica, 146, 110581-.
Journal: Automatica
Abstract: Reinforcement learning provides a powerful tool for designing a satisfactory controller through interactions with the environment. Although off-policy learning algorithms were recently designed for tracking problems, most of these results either are full-state feedback or have bounded control errors, which may not be flexible or desirable for engineering problems in the real world. To address these problems, we propose an output-feedback-based reinforcement learning approach that allows us to find the optimal control solution using input–output data and ensure the asymptotic tracking control of continuous-time systems. More specifically, we first propose a dynamical controller revised from the standard output regulation theory and use it to formulate an optimal output tracking problem. Then, a state observer is used to re-express the system state. Consequently, we address the rank issue of the parameterization matrix and analyze the state re-expression error that are crucial for transforming the off-policy learning into an output-feedback form. A comprehensive simulation study is given to demonstrate the effectiveness of the proposed approach.
ISSN: 0005-1098
DOI: 10.1016/j.automatica.2022.110581
Schools: School of Electrical and Electronic Engineering 
Rights: © 2022 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

Citations 50

Updated on Dec 4, 2023

Web of ScienceTM
Citations 50

Updated on Oct 31, 2023

Page view(s)

Updated on Dec 7, 2023

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.