Volume 31, Issue 18 pp. 9055-9073
RESEARCH ARTICLE

Stability of nonlinear distributed delay system with parameter uncertainties: Integral-based event-triggered impulsive control strategy

Hongfei Li

Hongfei Li

State Key Laboratory for Turbulence and Complex Systems, Department of Advanced Manufacturing and Robotics, BIC-ESAT, College of Engineering, Peking University, Beijing, China

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Liruo Zhang

Liruo Zhang

Department of Electrical and Computer Engineering, The University of Auckland, Auckland, New Zealand

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Xiaoyu Zhang

Xiaoyu Zhang

Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, China

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Junzhi Yu

Corresponding Author

Junzhi Yu

State Key Laboratory for Turbulence and Complex Systems, Department of Advanced Manufacturing and Robotics, BIC-ESAT, College of Engineering, Peking University, Beijing, China

Correspondence Junzhi Yu, State Key Laboratory for Turbulence and Complex Systems, Department of Advanced Manufacturing and Robotics, BIC-ESAT, College of Engineering, Peking University, Beijing 100871, China.

Email: [email protected]

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First published: 25 August 2021
Citations: 4

Funding information: Chongqing Natural Science Foundation Project, cstc2019jcyjmsxm2068; National Natural Science Foundation of China, 61903007; 61903052; 61906023; 62003008; U1909206; Post-Doctoral Science Foundation of China, 2021M690226

Abstract

This article investigates the stability of nonlinear uncertain distributed delay system via integral-based event-triggered impulsive control (IETIC) strategy. First, a IETIC mechanism is presented to reduce the redundant data transmission over the system, in which the integral-based event-triggered mechanism uses the integration of system states over a time period in the past. Second, a new lemma is proposed to eliminate the Zeno behavior of the established model through the IETIC mechanism. Third, a novel Lyapunov–Krasovskii functional (LKF) method related to probability density function is constructed to guarantee the stability of the established model based on LMI conditions, where a probability density function is introduced as a distributed delay kernel. Compared with existing methods, the constructed novel LKF method is less conservative or requiring less number of decision variables. Numerical examples are further provided to confirm the effectiveness and advantages of the proposed approach.

CONFLICT OF INTEREST

The authors declare no potential conflict of interests.

DATA AVAILABILITY STATEMENT

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

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