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

Yang, Z.-X. and Rong, H.-J. and Wong, P.K. and Angelov, P. and Wang, H. (2021) Self-Evolving Data Cloud-Based PID-Like Controller for Nonlinear Uncertain Systems. IEEE Transactions on Industrial Electronics, 68 (5). pp. 4508-4518. ISSN 0278-0046

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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.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Transactions on Industrial Electronics
Additional Information:
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Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2208
Subjects:
ID Code:
152908
Deposited By:
Deposited On:
18 Mar 2021 16:00
Refereed?:
Yes
Published?:
Published
Last Modified:
25 Oct 2021 04:46