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Self-Evolving Data Cloud-Based PID-Like Controller for Nonlinear Uncertain Systems

Research output: Contribution to journalJournal articlepeer-review

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Standard

Self-Evolving Data Cloud-Based PID-Like Controller for Nonlinear Uncertain Systems. / Yang, Z.-X.; Rong, H.-J.; Wong, P.K.; Angelov, P.; Wang, H.

In: IEEE Transactions on Industrial Electronics, Vol. 68, No. 5, 01.05.2021, p. 4508-4518.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Yang, Z-X, Rong, H-J, Wong, PK, Angelov, P & Wang, H 2021, 'Self-Evolving Data Cloud-Based PID-Like Controller for Nonlinear Uncertain Systems', IEEE Transactions on Industrial Electronics, vol. 68, no. 5, pp. 4508-4518. https://doi.org/10.1109/TIE.2020.2982094

APA

Yang, Z-X., Rong, H-J., Wong, P. K., Angelov, P., & Wang, H. (2021). Self-Evolving Data Cloud-Based PID-Like Controller for Nonlinear Uncertain Systems. IEEE Transactions on Industrial Electronics, 68(5), 4508-4518. https://doi.org/10.1109/TIE.2020.2982094

Vancouver

Yang Z-X, Rong H-J, Wong PK, Angelov P, Wang H. Self-Evolving Data Cloud-Based PID-Like Controller for Nonlinear Uncertain Systems. IEEE Transactions on Industrial Electronics. 2021 May 1;68(5):4508-4518. https://doi.org/10.1109/TIE.2020.2982094

Author

Yang, Z.-X. ; Rong, H.-J. ; Wong, P.K. ; Angelov, P. ; Wang, H. / Self-Evolving Data Cloud-Based PID-Like Controller for Nonlinear Uncertain Systems. In: IEEE Transactions on Industrial Electronics. 2021 ; Vol. 68, No. 5. pp. 4508-4518.

Bibtex

@article{98f43bda902b49c9aace59677a51fad6,
title = "Self-Evolving Data Cloud-Based PID-Like Controller for Nonlinear Uncertain Systems",
abstract = "In this article, a novel self-evolving data cloud-based proportional integral derivative (PID) (SEDCPID) like controller is proposed for uncertain nonlinear systems. The proposed SEDCPID controller is constructed by using fuzzy rules with nonparametric data cloud-based antecedence and PID-like consequence. The antecedent data clouds adopt the relative data density to represent the fuzzy firing strength of input variables instead of the explicit design of the membership functions in the classical sense. The proposed SEDCPID controller has the advantages of evolving structure and adapting parameter concurrently in an online manner. The density and distance information of data clouds are proposed to achieve the addition and deletion of data clouds and also a stable recursive method is proposed to update the parameters of the PID-like subcontrollers for the fast convergence performance. Based on the Lyapunov stability theory, the stability of the proposed controller is proven and the proof shows the tracking errors converge to a small neighborhood. Numerical and experimental results illustrate the effectiveness of the proposed controller in handling the uncertain nonlinear dynamic systems. ",
keywords = "Data clouds, proportional integral derivative (PID), self-evolving, stability, Controllers, Fuzzy inference, Membership functions, Nonlinear dynamical systems, Two term control systems, Distance information, Fast convergence, Lyapunov stability theory, Nonlinear uncertain systems, PID like controllers, Proportional integral derivatives, Recursive methods, Uncertain nonlinear systems, Proportional control systems",
author = "Z.-X. Yang and H.-J. Rong and P.K. Wong and P. Angelov and H. Wang",
note = "{\textcopyright}2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. ",
year = "2021",
month = may,
day = "1",
doi = "10.1109/TIE.2020.2982094",
language = "English",
volume = "68",
pages = "4508--4518",
journal = "IEEE Transactions on Industrial Electronics",
issn = "0278-0046",
publisher = "IEEE",
number = "5",

}

RIS

TY - JOUR

T1 - Self-Evolving Data Cloud-Based PID-Like Controller for Nonlinear Uncertain Systems

AU - Yang, Z.-X.

AU - Rong, H.-J.

AU - Wong, P.K.

AU - Angelov, P.

AU - Wang, H.

N1 - ©2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2021/5/1

Y1 - 2021/5/1

N2 - In this article, a novel self-evolving data cloud-based proportional integral derivative (PID) (SEDCPID) like controller is proposed for uncertain nonlinear systems. The proposed SEDCPID controller is constructed by using fuzzy rules with nonparametric data cloud-based antecedence and PID-like consequence. The antecedent data clouds adopt the relative data density to represent the fuzzy firing strength of input variables instead of the explicit design of the membership functions in the classical sense. The proposed SEDCPID controller has the advantages of evolving structure and adapting parameter concurrently in an online manner. The density and distance information of data clouds are proposed to achieve the addition and deletion of data clouds and also a stable recursive method is proposed to update the parameters of the PID-like subcontrollers for the fast convergence performance. Based on the Lyapunov stability theory, the stability of the proposed controller is proven and the proof shows the tracking errors converge to a small neighborhood. Numerical and experimental results illustrate the effectiveness of the proposed controller in handling the uncertain nonlinear dynamic systems.

AB - In this article, a novel self-evolving data cloud-based proportional integral derivative (PID) (SEDCPID) like controller is proposed for uncertain nonlinear systems. The proposed SEDCPID controller is constructed by using fuzzy rules with nonparametric data cloud-based antecedence and PID-like consequence. The antecedent data clouds adopt the relative data density to represent the fuzzy firing strength of input variables instead of the explicit design of the membership functions in the classical sense. The proposed SEDCPID controller has the advantages of evolving structure and adapting parameter concurrently in an online manner. The density and distance information of data clouds are proposed to achieve the addition and deletion of data clouds and also a stable recursive method is proposed to update the parameters of the PID-like subcontrollers for the fast convergence performance. Based on the Lyapunov stability theory, the stability of the proposed controller is proven and the proof shows the tracking errors converge to a small neighborhood. Numerical and experimental results illustrate the effectiveness of the proposed controller in handling the uncertain nonlinear dynamic systems.

KW - Data clouds

KW - proportional integral derivative (PID)

KW - self-evolving

KW - stability

KW - Controllers

KW - Fuzzy inference

KW - Membership functions

KW - Nonlinear dynamical systems

KW - Two term control systems

KW - Distance information

KW - Fast convergence

KW - Lyapunov stability theory

KW - Nonlinear uncertain systems

KW - PID like controllers

KW - Proportional integral derivatives

KW - Recursive methods

KW - Uncertain nonlinear systems

KW - Proportional control systems

U2 - 10.1109/TIE.2020.2982094

DO - 10.1109/TIE.2020.2982094

M3 - Journal article

VL - 68

SP - 4508

EP - 4518

JO - IEEE Transactions on Industrial Electronics

JF - IEEE Transactions on Industrial Electronics

SN - 0278-0046

IS - 5

ER -