Novel GPID: Grünwald–Letnikov Fractional PID for Enhanced Adaptive Cruise Control DOI Creative Commons

Diaa Eldin Elgezouli,

Hassan Eltayeb, Mohamed A. Abdoon

et al.

Fractal and Fractional, Journal Year: 2024, Volume and Issue: 8(12), P. 751 - 751

Published: Dec. 20, 2024

This study demonstrates that the Grünwald–Letnikov fractional proportional–integral–derivative (GPID) controller outperforms traditional PID controllers in adaptive cruise control systems, while conventional struggle with nonlinearities, dynamic uncertainties, and stability, GPID enhances robustness provides more precise across various driving conditions. Simulation results show improves accuracy, reducing errors better than controller. Additionally, maintains a consistent speed reaches target faster, demonstrating superior control. The GPID’s performance different orders highlights its adaptability to changing road conditions, which is crucial for ensuring safety comfort. By leveraging calculus, also acceleration deceleration profiles. These findings emphasize potential revolutionize control, significantly enhancing Numerical obtained α=0.99 from have shown accuracy consistency, adapting conditions improved demonstrated faster stabilization of at 60 km/h smaller reduced error 0.59 50 s compared 0.78 PID.

Language: Английский

AI-enabled frequency synchronization control considering FDI attack using metaheuristic algorithm DOI
Hamad Ahmad, Muhammad Majid Gulzar, Ghulam Mustafa

et al.

Neural Computing and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 7, 2025

Language: Английский

Citations

0
Study on PID gain parameter optimization for a quadcopter under static wind turbulence using bio-inspired algorithms DOI Creative Commons
Olukunle Kolawole Soyinka,

Monica Ngunan Ikpaya,

Lumi Luka

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: 2(1)

Published: Feb. 17, 2025

Language: Английский

Citations

0
Fractional Order PID Controller Design for an AVR System Using the Artificial Hummingbird Optimizer Algorithm DOI
Elouahab Bouguenna, Samir Ladaci, Badis Lekouaghet

et al.

International Journal of Robust and Nonlinear Control, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 26, 2025

ABSTRACT Optimizing the fractional‐order PID (FOPID) controller using metaheuristic algorithms has gained significant popularity across various engineering domains. This paper introduces a novel approach by employing artificial hummingbird algorithm (AHA), an innovative optimization technique inspired unique flight and foraging behaviors of hummingbirds, to fine‐tune FOPID for automatic voltage regulator (AVR) system in synchronous generators, critical component maintaining stability. The proposed method is rigorously tested MATLAB/Simulink simulations under challenging conditions, including nonsmoothed higher‐order dynamics control plant, parameter variations, time delays, nonlinearities. effectiveness AHA‐based strategy on AVR comprehensively evaluated through extensive tests analyses, focusing aspects such as transient response, robustness, stability, trajectory tracking. Moreover, comparative assessment against established algorithms, namely particle swarm (PSO), genetic (GA), gray wolf optimizer (GWO), bee colony (ABC) conducted. results demonstrate superiority strategy, which significantly increases convergence speed. evidenced 25% faster rise 45.74% shorter settling compared GA‐FOPID controller, closest performance these metrics. Additionally, achieves 92% reduction steady‐state oscillations ABC‐FOPID nearest competitor this aspect. These improvements highlight controller's superior efficiency rapid response achieving optimal performance. Hence, shows remarkable success enhancing stability making it highly suitable design practical high‐performance applications.

Language: Английский

Citations

0
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

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(8), P. 2034 - 2034

Published: April 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.

Language: Английский

Citations

0
Modified and Improved TID Controller for Automatic Voltage Regulator Systems DOI Creative Commons
Abdülsamed Tabak

Fractal and Fractional, Journal Year: 2024, Volume and Issue: 8(11), P. 654 - 654

Published: Nov. 11, 2024

This paper proposes a fractional order integral-derivative plus second-order derivative with low-pass filters and tilt controller called IλDND2N2-T to improve the control performance of an automatic voltage regulator (AVR). In this study, equilibrium optimisation (EO), multiverse (MVO), particle swarm (PSO) algorithms are used optimise parameters proposed statistical tests performed data obtained from application these three AVR problem. Afterwards, is demonstrated by comparing transient responses results in recently published papers. addition, extra disturbances such as frequency deviation, load variation, short circuit faults generator applied system. The has outperformed compared against disturbances. Finally, robustness test terms deterioration system parameters. show that outperforms controllers under all conditions exhibits robust effect on perturbed

Language: Английский

Citations

2
PID control algorithm based on multistrategy enhanced dung beetle optimizer and back propagation neural network for DC motor control DOI Creative Commons

Wei-Bin Kong,

Haonan Zhang, Xiaofang Yang

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Nov. 16, 2024

Traditional Proportional-Integral-Derivative (PID) control systems often encounter challenges related to nonlinearity and time-variability. Original dung beetle optimizer (DBO) offers fast convergence strong local exploitation capabilities. However, they are limited by poor exploration capabilities, imbalance between phases, insufficient precision in global search. This paper proposes a novel adaptive PID algorithm based on enhanced (EDBO) back propagation neural network (BPNN). Firstly, the diversity of is increased incorporating merit-oriented mechanism into rolling behavior. Then, sine learning factor introduced balance Additionally, dynamic spiral search strategy $$t$$ -distribution disturbance presented enhance capability. The BPNN employed fine-tune both parameters, leveraging its powerful generalization ability model nonlinear system dynamics. In simplified motor experiments, proposed controller achieved lowest overshoot (0.5%) shortest response time (0.012 s), with settling 0.02 s steady-state error just 0.0010. another set recorded an 0.7% 0.0010 s, across five DC tests. These results demonstrate has superior performance optimizing as well improving robustness stability.

Language: Английский

Citations

2
Identification of Transformer Parameters Using Dandelion Algorithm DOI Creative Commons
Mahmoud A. El‐Dabah, Ahmed M. Agwa

Applied System Innovation, Journal Year: 2024, Volume and Issue: 7(5), P. 75 - 75

Published: Aug. 29, 2024

Researchers tackled the challenge of finding right parameters for a transformer-equivalent circuit. They achieved this by minimizing difference between actual measurements (currents, powers, secondary voltage) during transformer load test and values predicted model using different parameter settings. This process considers limitations on what can have. research introduces application new effective optimization algorithm called dandelion (DA) to determine these parameters. Information from real-time tests (single- three-phase transformers) is fed into computer program that uses DA find best aforementioned difference. Tests confirm reliable accurate tool estimating It achieves excellent performance stability in optimal precisely reflect how behaves. The significantly lower fitness function value 0.0136101 case, while single-phase case it reached 0.601764. indicates substantially improved match estimated measured electrical model. By comparing with six competitive algorithms prove well each method minimized predictions, could be shown outperforms other techniques.

Language: Английский

Citations

1
Automatic Voltage Regulator Betterment Based on a New Fuzzy FOPI+FOPD Tuned by TLBO DOI Creative Commons
Mokhtar Shouran,

Mohammed Alenezi

Fractal and Fractional, Journal Year: 2024, Volume and Issue: 9(1), P. 21 - 21

Published: Dec. 31, 2024

This paper presents a novel Fuzzy Logic Controller (FLC) framework aimed at enhancing the performance and stability of Automatic Voltage Regulators (AVRs) in power systems. The proposed system combines fuzzy control theory with Fractional Order Proportional Integral Derivative (FOPID) technique employs cascading to significantly improve reliability robustness. unique architecture, termed (PI) plus (PD) (Fuzzy FOPI+FOPD+I), integrates advanced methodologies achieve superior performance. To optimize controller parameters, Teaching–Learning-Based Optimization (TLBO) algorithm is utilized conjunction Time Absolute Error (ITAE) objective function, ensuring precise tuning for optimal behavior. methodology validated through comparative analyses controllers reported prior studies, highlighting substantial improvements metrics. Key findings demonstrate significant reductions peak overshoot, undershoot, settling time, emphasizing controller’s effectiveness. Additionally, robustness extensively evaluated under challenging scenarios, including parameter uncertainties load disturbances. Results confirm its ability maintain across wide range conditions, outperforming existing methods. study notable contribution by introducing an innovative structure that addresses critical challenges AVR systems, paving way more resilient efficient operations.

Language: Английский

Citations

1
Novel GPID: Grünwald–Letnikov Fractional PID for Enhanced Adaptive Cruise Control DOI Creative Commons

Diaa Eldin Elgezouli,

Hassan Eltayeb, Mohamed A. Abdoon

et al.

Fractal and Fractional, Journal Year: 2024, Volume and Issue: 8(12), P. 751 - 751

Published: Dec. 20, 2024

This study demonstrates that the Grünwald–Letnikov fractional proportional–integral–derivative (GPID) controller outperforms traditional PID controllers in adaptive cruise control systems, while conventional struggle with nonlinearities, dynamic uncertainties, and stability, GPID enhances robustness provides more precise across various driving conditions. Simulation results show improves accuracy, reducing errors better than controller. Additionally, maintains a consistent speed reaches target faster, demonstrating superior control. The GPID’s performance different orders highlights its adaptability to changing road conditions, which is crucial for ensuring safety comfort. By leveraging calculus, also acceleration deceleration profiles. These findings emphasize potential revolutionize control, significantly enhancing Numerical obtained α=0.99 from have shown accuracy consistency, adapting conditions improved demonstrated faster stabilization of at 60 km/h smaller reduced error 0.59 50 s compared 0.78 PID.

Language: Английский

Citations

0