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Research on Gate Opening Control Based on Improved Beetle Antennae Search.
Wang, Lijun; Wang, Yibo; Kang, Yehao; Shen, Jie; Cheng, Ruixue; Zhang, Jianyong; Shi, Shuheng.
Afiliação
  • Wang L; School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China.
  • Wang Y; School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China.
  • Kang Y; School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China.
  • Shen J; School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China.
  • Cheng R; School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK.
  • Zhang J; School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK.
  • Shi S; School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China.
Sensors (Basel) ; 24(13)2024 Jul 08.
Article em En | MEDLINE | ID: mdl-39001204
ABSTRACT
To address the issues of sluggish response and inadequate precision in traditional gate opening control systems, this study presents a novel approach for direct current (DC) motor control utilizing an enhanced beetle antennae search (BAS) algorithm to fine-tune the parameters of a fuzzy proportional integral derivative (PID) controller. Initially, the mathematical model of the DC motor drive system is formulated. Subsequently, employing a search algorithm, the three parameters of the PID controller are optimized in accordance with the control requirements. Next, software simulation is employed to analyze the system's response time and overshoot. Furthermore, a comparative analysis is conducted between fuzzy PID control based on the improved beetle antennae search algorithm, and conventional approaches such as the traditional beetle antennae search algorithm, the traditional particle swarm algorithm, and the enhanced particle swarm algorithm. The findings indicate the superior performance of the proposed method, characterized by reduced oscillations and accelerated convergence compared to the alternative methods.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article