Event-Based Adaptive Neural Asymptotic Tracking Control for Networked Nonlinear Stochastic Systems

Yuan Xin Li; Xiao Yan Hu; Choon Ki Ahn; Zhong Sheng Hou; Hyun Ho Kang

doi:10.1109/TNSE.2022.3161645

Event-Based Adaptive Neural Asymptotic Tracking Control for Networked Nonlinear Stochastic Systems

Yuan Xin Li, Xiao Yan Hu, Choon Ki Ahn, Zhong Sheng Hou, Hyun Ho Kang

Research output: Contribution to journal › Article › peer-review

11 Citations (Scopus)

Abstract

This paper investigates the adaptive asymptotic tracking control for networked nonlinear stochastic systems. Different from having the necessity of prior knowledge of the unknown control coefficients in the conventional adaptive control of nonlinear stochastic systems, in this study, the limitation of control coefficients in the stability analysis is relaxed by constructing a new Lyapunov function that contains the lower bounds of the control gain function. By constructing a smooth function with a positive time-varying integral function and utilizing the boundary estimation method, asymptotic tracking control can be guaranteed. At the same time, for nonlinear stochastic systems with unknown control coefficients, a neural adaptive event-triggered strategy that greatly saves communication resources while ensuring system performance is proposed. Finally, simulation results show that the proposed control scheme can guarantee the realization of the control objectives.

Original language	English
Pages (from-to)	2290-2300
Number of pages	11
Journal	IEEE Transactions on Network Science and Engineering
Volume	9
Issue number	4
DOIs	https://doi.org/10.1109/TNSE.2022.3161645
Publication status	Published - 2022

Bibliographical note

Funding Information:
This work was supported in part by the Funds of National Science of China under Grants 61973146 and 62173172, in part by Distinguished Young Scientific Research Talents Plan in Liaoning Province under Grant XLYC1907077, in part by the Taishan Scholar Project of Shandong Province of China under Grant tsqn201909097, and in part by the National Research Foundation of Korea (NRF) funded by the Korea Government (Ministry of Science and ICT) under Grant NRF-2020R1A2C1005449.

Publisher Copyright:
© 2013 IEEE.

Keywords

Event-triggered control (ETC)
adaptive asymptotic tracking
networked nonlinear stochastic systems
neural networks (NNs)

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science Applications
Computer Networks and Communications

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10.1109/TNSE.2022.3161645

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title = "Event-Based Adaptive Neural Asymptotic Tracking Control for Networked Nonlinear Stochastic Systems",

abstract = "This paper investigates the adaptive asymptotic tracking control for networked nonlinear stochastic systems. Different from having the necessity of prior knowledge of the unknown control coefficients in the conventional adaptive control of nonlinear stochastic systems, in this study, the limitation of control coefficients in the stability analysis is relaxed by constructing a new Lyapunov function that contains the lower bounds of the control gain function. By constructing a smooth function with a positive time-varying integral function and utilizing the boundary estimation method, asymptotic tracking control can be guaranteed. At the same time, for nonlinear stochastic systems with unknown control coefficients, a neural adaptive event-triggered strategy that greatly saves communication resources while ensuring system performance is proposed. Finally, simulation results show that the proposed control scheme can guarantee the realization of the control objectives.",

keywords = "Event-triggered control (ETC), adaptive asymptotic tracking, networked nonlinear stochastic systems, neural networks (NNs)",

author = "Li, {Yuan Xin} and Hu, {Xiao Yan} and Ahn, {Choon Ki} and Hou, {Zhong Sheng} and Kang, {Hyun Ho}",

note = "Funding Information: This work was supported in part by the Funds of National Science of China under Grants 61973146 and 62173172, in part by Distinguished Young Scientific Research Talents Plan in Liaoning Province under Grant XLYC1907077, in part by the Taishan Scholar Project of Shandong Province of China under Grant tsqn201909097, and in part by the National Research Foundation of Korea (NRF) funded by the Korea Government (Ministry of Science and ICT) under Grant NRF-2020R1A2C1005449. Publisher Copyright: {\textcopyright} 2013 IEEE.",

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AU - Hou, Zhong Sheng

AU - Kang, Hyun Ho

N1 - Funding Information: This work was supported in part by the Funds of National Science of China under Grants 61973146 and 62173172, in part by Distinguished Young Scientific Research Talents Plan in Liaoning Province under Grant XLYC1907077, in part by the Taishan Scholar Project of Shandong Province of China under Grant tsqn201909097, and in part by the National Research Foundation of Korea (NRF) funded by the Korea Government (Ministry of Science and ICT) under Grant NRF-2020R1A2C1005449. Publisher Copyright: © 2013 IEEE.

PY - 2022

Y1 - 2022

N2 - This paper investigates the adaptive asymptotic tracking control for networked nonlinear stochastic systems. Different from having the necessity of prior knowledge of the unknown control coefficients in the conventional adaptive control of nonlinear stochastic systems, in this study, the limitation of control coefficients in the stability analysis is relaxed by constructing a new Lyapunov function that contains the lower bounds of the control gain function. By constructing a smooth function with a positive time-varying integral function and utilizing the boundary estimation method, asymptotic tracking control can be guaranteed. At the same time, for nonlinear stochastic systems with unknown control coefficients, a neural adaptive event-triggered strategy that greatly saves communication resources while ensuring system performance is proposed. Finally, simulation results show that the proposed control scheme can guarantee the realization of the control objectives.

AB - This paper investigates the adaptive asymptotic tracking control for networked nonlinear stochastic systems. Different from having the necessity of prior knowledge of the unknown control coefficients in the conventional adaptive control of nonlinear stochastic systems, in this study, the limitation of control coefficients in the stability analysis is relaxed by constructing a new Lyapunov function that contains the lower bounds of the control gain function. By constructing a smooth function with a positive time-varying integral function and utilizing the boundary estimation method, asymptotic tracking control can be guaranteed. At the same time, for nonlinear stochastic systems with unknown control coefficients, a neural adaptive event-triggered strategy that greatly saves communication resources while ensuring system performance is proposed. Finally, simulation results show that the proposed control scheme can guarantee the realization of the control objectives.

KW - Event-triggered control (ETC)

KW - adaptive asymptotic tracking

KW - networked nonlinear stochastic systems

KW - neural networks (NNs)

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Event-Based Adaptive Neural Asymptotic Tracking Control for Networked Nonlinear Stochastic Systems

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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