Optimization of PID Controllers Using Groupers and Moray Eels Optimization with Dual-Stream Multi-Dependency Graph Neural Networks for Enhanced Dynamic Performance DOI Creative Commons

Vaishali H. Kamble,

Manisha Dale, R. B. Dhumale

и другие.

Energies, Год журнала: 2025, Номер 18(8), С. 2034 - 2034

Опубликована: Апрель 16, 2025

Traditional proportional–integral–derivative (PID) controllers are often utilized in industrial control applications due to their simplicity and ease of implementation. This study presents a novel strategy that integrates the Groupers Moray Eels Optimization (GMEO) algorithm with Dual-Stream Multi-Dependency Graph Neural Network (DMGNN) optimize PID controller parameters. The approach addresses key challenges such as system nonlinearity, dynamic adaptation fluctuating conditions, maintaining robust performance. In proposed framework, GMEO technique is employed gain values, while DMGNN model forecasts behavior enables localized adjustments parameters based on feedback. tuning mechanism adapt effectively changes input voltage load variations, thereby enhancing accuracy, responsiveness, overall assessed contrasted existing strategies MATLAB platform. achieves significantly reduced settling time 100 ms, ensuring rapid response stability under varying conditions. Additionally, it minimizes overshoot 1.5% reduces steady-state error just 0.005 V, demonstrating superior accuracy efficiency compared methods. These improvements demonstrate system’s ability deliver optimal performance adapting environments, showcasing its superiority over techniques.

Язык: Английский

Optimization of PID Controllers Using Groupers and Moray Eels Optimization with Dual-Stream Multi-Dependency Graph Neural Networks for Enhanced Dynamic Performance DOI Creative Commons

Vaishali H. Kamble,

Manisha Dale, R. B. Dhumale

и другие.

Energies, Год журнала: 2025, Номер 18(8), С. 2034 - 2034

Опубликована: Апрель 16, 2025

Traditional proportional–integral–derivative (PID) controllers are often utilized in industrial control applications due to their simplicity and ease of implementation. This study presents a novel strategy that integrates the Groupers Moray Eels Optimization (GMEO) algorithm with Dual-Stream Multi-Dependency Graph Neural Network (DMGNN) optimize PID controller parameters. The approach addresses key challenges such as system nonlinearity, dynamic adaptation fluctuating conditions, maintaining robust performance. In proposed framework, GMEO technique is employed gain values, while DMGNN model forecasts behavior enables localized adjustments parameters based on feedback. tuning mechanism adapt effectively changes input voltage load variations, thereby enhancing accuracy, responsiveness, overall assessed contrasted existing strategies MATLAB platform. achieves significantly reduced settling time 100 ms, ensuring rapid response stability under varying conditions. Additionally, it minimizes overshoot 1.5% reduces steady-state error just 0.005 V, demonstrating superior accuracy efficiency compared methods. These improvements demonstrate system’s ability deliver optimal performance adapting environments, showcasing its superiority over techniques.

Язык: Английский

Процитировано

0