RBFNN-based event-triggered MFAC for discrete-time nonlinear systems with disturbances and data quantization

Xianming Wang; Tu Zhang; Xingzheng Wu; Wen Qin; Liwei Li; Mouquan Shen

doi:10.1109/CCDC55256.2022.10034390

RBFNN-based event-triggered MFAC for discrete-time nonlinear systems with disturbances and data quantization

Xianming Wang, Tu Zhang, Xingzheng Wu, Wen Qin, Liwei Li, Mouquan Shen

COLLEGE OF ELECTRICAL ENGINEERING AND CONTROL SCIENCE

Nanjing Tech University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

This parper concentrates on neural network-based event-triggered MFAC for discrete-time nonlinear systems with disturbances and data quantization. First, the logarithmic quantizer is adopted to quantify the input signal. By uti-lizing the radial basis function neural network, an estimation method is presented to estimate the unknown pseudopartial derivative and disturbances. To reduce the influence of disturbances, an improved control law is built on the estimated disturbances. By using the tracking error and the estimated disturbance, an event-triggered mechanism is designed to save the communication resources. Conditions are presented to guarantee that the tracking error is uniformly ultimately bounded. The validity of the proposed method is verified by simulation example.

Original language	English
Title of host publication	Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4042-4047
Number of pages	6
ISBN (Electronic)	9781665478960
DOIs	https://doi.org/10.1109/CCDC55256.2022.10034390
State	Published - 2022
Event	34th Chinese Control and Decision Conference, CCDC 2022 - Hefei, China Duration: 15 Aug 2022 → 17 Aug 2022

Publication series

Name	Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022

Conference

Conference	34th Chinese Control and Decision Conference, CCDC 2022
Country/Territory	China
City	Hefei
Period	15/08/22 → 17/08/22

Keywords

Event-triggered control
RBF neural network
model-free adaptive control

Access to Document

10.1109/CCDC55256.2022.10034390

Cite this

Wang, X., Zhang, T., Wu, X., Qin, W., Li, L., & Shen, M. (2022). RBFNN-based event-triggered MFAC for discrete-time nonlinear systems with disturbances and data quantization. In Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022 (pp. 4042-4047). (Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCDC55256.2022.10034390

Wang, Xianming ; Zhang, Tu ; Wu, Xingzheng et al. / RBFNN-based event-triggered MFAC for discrete-time nonlinear systems with disturbances and data quantization. Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 4042-4047 (Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022).

@inproceedings{eefccdeed28b41e38a8822031c42ab8e,

title = "RBFNN-based event-triggered MFAC for discrete-time nonlinear systems with disturbances and data quantization",

abstract = "This parper concentrates on neural network-based event-triggered MFAC for discrete-time nonlinear systems with disturbances and data quantization. First, the logarithmic quantizer is adopted to quantify the input signal. By uti-lizing the radial basis function neural network, an estimation method is presented to estimate the unknown pseudopartial derivative and disturbances. To reduce the influence of disturbances, an improved control law is built on the estimated disturbances. By using the tracking error and the estimated disturbance, an event-triggered mechanism is designed to save the communication resources. Conditions are presented to guarantee that the tracking error is uniformly ultimately bounded. The validity of the proposed method is verified by simulation example.",

keywords = "Event-triggered control, RBF neural network, model-free adaptive control",

author = "Xianming Wang and Tu Zhang and Xingzheng Wu and Wen Qin and Liwei Li and Mouquan Shen",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 34th Chinese Control and Decision Conference, CCDC 2022 ; Conference date: 15-08-2022 Through 17-08-2022",

year = "2022",

doi = "10.1109/CCDC55256.2022.10034390",

language = "英语",

series = "Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "4042--4047",

booktitle = "Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022",

address = "美国",

}

Wang, X, Zhang, T, Wu, X, Qin, W, Li, L & Shen, M 2022, RBFNN-based event-triggered MFAC for discrete-time nonlinear systems with disturbances and data quantization. in Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022. Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022, Institute of Electrical and Electronics Engineers Inc., pp. 4042-4047, 34th Chinese Control and Decision Conference, CCDC 2022, Hefei, China, 15/08/22. https://doi.org/10.1109/CCDC55256.2022.10034390

RBFNN-based event-triggered MFAC for discrete-time nonlinear systems with disturbances and data quantization. / Wang, Xianming; Zhang, Tu; Wu, Xingzheng et al.
Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 4042-4047 (Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - RBFNN-based event-triggered MFAC for discrete-time nonlinear systems with disturbances and data quantization

AU - Wang, Xianming

AU - Zhang, Tu

AU - Wu, Xingzheng

AU - Qin, Wen

AU - Li, Liwei

AU - Shen, Mouquan

PY - 2022

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N2 - This parper concentrates on neural network-based event-triggered MFAC for discrete-time nonlinear systems with disturbances and data quantization. First, the logarithmic quantizer is adopted to quantify the input signal. By uti-lizing the radial basis function neural network, an estimation method is presented to estimate the unknown pseudopartial derivative and disturbances. To reduce the influence of disturbances, an improved control law is built on the estimated disturbances. By using the tracking error and the estimated disturbance, an event-triggered mechanism is designed to save the communication resources. Conditions are presented to guarantee that the tracking error is uniformly ultimately bounded. The validity of the proposed method is verified by simulation example.

AB - This parper concentrates on neural network-based event-triggered MFAC for discrete-time nonlinear systems with disturbances and data quantization. First, the logarithmic quantizer is adopted to quantify the input signal. By uti-lizing the radial basis function neural network, an estimation method is presented to estimate the unknown pseudopartial derivative and disturbances. To reduce the influence of disturbances, an improved control law is built on the estimated disturbances. By using the tracking error and the estimated disturbance, an event-triggered mechanism is designed to save the communication resources. Conditions are presented to guarantee that the tracking error is uniformly ultimately bounded. The validity of the proposed method is verified by simulation example.

KW - Event-triggered control

KW - RBF neural network

KW - model-free adaptive control

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Wang X, Zhang T, Wu X, Qin W, Li L, Shen M. RBFNN-based event-triggered MFAC for discrete-time nonlinear systems with disturbances and data quantization. In Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 4042-4047. (Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022). doi: 10.1109/CCDC55256.2022.10034390

RBFNN-based event-triggered MFAC for discrete-time nonlinear systems with disturbances and data quantization

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