Adaptive optimal tracking control of networked linear systems under two-channel stochastic dropouts

Yi Jiang; Lu Liu; Gang Feng

doi:10.1016/j.automatica.2024.111690

Adaptive optimal tracking control of networked linear systems under two-channel stochastic dropouts

Yi Jiang
, Lu Liu^*
, Gang Feng

^*Corresponding author for this work

Department of Biomedical Engineering

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

18 Citations (Scopus)

Abstract

This work investigates the adaptive optimal tracking control problem for networked discrete-time linear systems by directly using the data transmitted via communication networks. It is shown that the concerned problem can be addressed by solving two sub-problems: an adaptive optimal control one and an adaptive output regulation one. Two novel online reinforcement learning based policy iteration and value iteration algorithms are developed, which constitute an integrated framework to learn the optimal feedback control gain and the solutions to regulator equations by directly using the data transmitted via communication networks. Furthermore, it is shown that the tracking error of the closed-loop control system is mean square asymptotically stable even in the case of network packet stochastic dropouts. Simulation results demonstrate the efficacy of the proposed approaches. © 2024 Elsevier Ltd

Original language	English
Article number	111690
Journal	Automatica
Volume	165
Online published	27 Apr 2024
DOIs	https://doi.org/10.1016/j.automatica.2024.111690
Publication status	Published - Jul 2024

Funding

This work was supported by Research Grants Council of Hong Kong, Project No. CityU 11205221 and 11210222 , NSFC-Excellent Young Scientists Fund (Hong Kong and Macao), Project No. 62222318 , the fellowship award from the Research Grants Council of Hong Kong , Project No. CityU PDFS2324-1S02 . The material in this paper was not presented at any conference. This paper was recommended for publication in revised form by Associate Editor Chanying Li under the direction of Editor Miroslav Krstic.

Research Keywords

Communication networks
Optimal control
Output regulation
Reinforcement learning

RGC Funding Information

RGC-funded

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10.1016/j.automatica.2024.111690

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title = "Adaptive optimal tracking control of networked linear systems under two-channel stochastic dropouts",

abstract = "This work investigates the adaptive optimal tracking control problem for networked discrete-time linear systems by directly using the data transmitted via communication networks. It is shown that the concerned problem can be addressed by solving two sub-problems: an adaptive optimal control one and an adaptive output regulation one. Two novel online reinforcement learning based policy iteration and value iteration algorithms are developed, which constitute an integrated framework to learn the optimal feedback control gain and the solutions to regulator equations by directly using the data transmitted via communication networks. Furthermore, it is shown that the tracking error of the closed-loop control system is mean square asymptotically stable even in the case of network packet stochastic dropouts. Simulation results demonstrate the efficacy of the proposed approaches. {\textcopyright} 2024 Elsevier Ltd",

keywords = "Communication networks, Optimal control, Output regulation, Reinforcement learning",

author = "Yi Jiang and Lu Liu and Gang Feng",

year = "2024",

month = jul,

doi = "10.1016/j.automatica.2024.111690",

language = "English",

volume = "165",

journal = "Automatica",

issn = "0005-1098",

publisher = "Elsevier Ltd.",

}

TY - JOUR

T1 - Adaptive optimal tracking control of networked linear systems under two-channel stochastic dropouts

AU - Jiang, Yi

AU - Liu, Lu

AU - Feng, Gang

PY - 2024/7

Y1 - 2024/7

N2 - This work investigates the adaptive optimal tracking control problem for networked discrete-time linear systems by directly using the data transmitted via communication networks. It is shown that the concerned problem can be addressed by solving two sub-problems: an adaptive optimal control one and an adaptive output regulation one. Two novel online reinforcement learning based policy iteration and value iteration algorithms are developed, which constitute an integrated framework to learn the optimal feedback control gain and the solutions to regulator equations by directly using the data transmitted via communication networks. Furthermore, it is shown that the tracking error of the closed-loop control system is mean square asymptotically stable even in the case of network packet stochastic dropouts. Simulation results demonstrate the efficacy of the proposed approaches. © 2024 Elsevier Ltd

AB - This work investigates the adaptive optimal tracking control problem for networked discrete-time linear systems by directly using the data transmitted via communication networks. It is shown that the concerned problem can be addressed by solving two sub-problems: an adaptive optimal control one and an adaptive output regulation one. Two novel online reinforcement learning based policy iteration and value iteration algorithms are developed, which constitute an integrated framework to learn the optimal feedback control gain and the solutions to regulator equations by directly using the data transmitted via communication networks. Furthermore, it is shown that the tracking error of the closed-loop control system is mean square asymptotically stable even in the case of network packet stochastic dropouts. Simulation results demonstrate the efficacy of the proposed approaches. © 2024 Elsevier Ltd

KW - Communication networks

KW - Optimal control

KW - Output regulation

KW - Reinforcement learning

UR - https://www.scopus.com/pages/publications/85191336773

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85191336773&origin=recordpage

U2 - 10.1016/j.automatica.2024.111690

DO - 10.1016/j.automatica.2024.111690

M3 - RGC 21 - Publication in refereed journal

SN - 0005-1098

VL - 165

JO - Automatica

JF - Automatica

M1 - 111690

ER -

Adaptive optimal tracking control of networked linear systems under two-channel stochastic dropouts

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Research Keywords

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GRF: Distributed Optimal Coordination of Multiple Dynamical Systems under Practical Constraints and its Applications

GRF: Cooperative Adaptive Control of Networked Multiple Dynamical Systems with Unknown Parameters and Its Application

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Adaptive optimal tracking control of networked linear systems under two-channel stochastic dropouts

Abstract

Funding

Research Keywords

RGC Funding Information

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Projects

GRF: Distributed Optimal Coordination of Multiple Dynamical Systems under Practical Constraints and its Applications

GRF: Cooperative Adaptive Control of Networked Multiple Dynamical Systems with Unknown Parameters and Its Application

Cite this