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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Buletin Ilmiah Sarjana Teknik Elektro International Journal of Robotics and Control Systems Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Abualigah, Laith
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering Transportation

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Controlling parameters proportional integral derivative of DC motor using a gradient-based optimizer Aribowo, Widi; Rahmadian, Reza; Widyartono, Mahendra; Wardani, Ayusta Lukita; Prapanca, Aditya; Abualigah, Laith
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i2.pp696-703

Abstract

In this paper, a gradient-based optimizer (GBO) algorithm is presented to optimize the parameters of a proportional integral derivative (PID) controller in DC motor control. The GBO algorithm which mathematically models and mimics is inspired by the gradient-based Newton method. It was developed to address various optimization issues. To determine the performance of the proposed method, a comparison method with the ant colony optimization (ACO) method. It was compared using the integral of time multiplied absolute error (ITAE). They are most popularly used in the literature. From the test results, the proposed method is promising and has better effectiveness. The proposed method, namely GBO-PID, shows the best performance.
A novel modified mountain gazelle optimizer for tuning parameter proportional integral derivative of DC motor Aribowo, Widi; Abualigah, Laith; Oliva, Diego; Prapanca, Aditya
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.5575

Abstract

This article presents a modified method of mountain gazelle optimizer (MMGO) as a direct current (DC) motor control. Mountain gazelle optimizer (MGO) is an algorithm inspired by the life of the mountain gazelle animal in nature. This animal concept has five essential steps that are duplicated in mathematical modeling. This article uses two tests to get the performance of the MMGO method. The first test uses a benchmark function test with a comparison method, namely the sine tree seed algorithm (STSA) and the original MGO. The second test is the application of MMGO as a DC motor control. The simulation results show that MMGO can reduce the overshoot of conventional proportional integral derivative (PID) control by 0.447% and has a better integral time square error (ITSE) value of 5.345 than conventional PID control. Thus, the MMGO method shows promising performance.
Aircraft Pitch Control via Filtered Proportional-Integral-Derivative Controller Design Using Sinh Cosh Optimizer Abualigah, Laith; Ekinci, Serdar; Izci, Davut
International Journal of Robotics and Control Systems Vol 4, No 2 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i2.1433

Abstract

An innovative approach to controlling aircraft pitch is shown in this research. This approach is accomplished by adopting a proportional-integral-derivative with filter (PID-F) mechanism. A novel metaheuristic approach that we propose is called the sinh cosh optimizer (SCHO), and it is intended to further optimize the settings of the PID-F controller that is used in the aircraft pitch control (APC) configuration. An in-depth comparison and contrast of the recommended method is carried out, and statistical and time domain assessments are utilized in order to ascertain the success of the method. When it comes to managing the APC system, the SCHO-based PID-F controller delivers superior performance compared to other modern and efficient PID controllers (salp swarm based PID, Harris hawks optimization based PID, grasshopper algorithm based PID, atom search optimization based PID, sine cosine algorithm based PID, and Henry gas solubility optimization based PID) that have been published in the literature. When compared to alternative approaches of regulating the APC system, the findings demonstrate that the way that was presented is among the most successful as better statistical (minimum of 0.0033, maximum of 0.0034, average of 0.0034 and standard deviation of 5.1151E−05) and transient response (overshoot of 0%, rise time of 0.0141 s, settling time of 0.0230 s, peak time of 0.0333 s and steady-state error of 0 %) values have been achieved.
Optimizing Aircraft Pitch Control Systems: A Novel Approach Integrating Artificial Rabbits Optimizer with PID-F Controller Abualigah, Laith; Izci, Davut; Ekinci, Serdar; Zitar, Raed Abu
International Journal of Robotics and Control Systems Vol 4, No 1 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i1.1347

Abstract

The precise control of aircraft pitch angles is critical in aviation for maintaining specific attitudes during flight, including straight and level flight, ascents, and descents. Traditional control strategies face challenges due to the non-linear and uncertain dynamics of flight. To address these issues, this study introduces a novel approach employing the artificial rabbits optimizer (ARO) for tuning a PID controller with a filtering mechanism (PID-F) in aircraft pitch control systems. This combination aims to enhance the stability and performance of the aircraft pitch control system by effectively mitigating the kick effect through the incorporation of a filter coefficient in the derivative gain. The study employs a time-domain-based objective function to guide the optimization process. Simulation results validate the stability and consistency of the proposed ARO/PID-F approach. Comparative analysis with various optimization algorithm-based controllers from the literature demonstrates the effectiveness of the proposed technique. Specifically, the ARO/PID-F controller exhibits a rapid response, zero overshoot, minimal settling time, and precise control during critical phases. The obtained results position the proposed methodology as a promising and innovative solution for optimizing aircraft pitch control systems, offering improved performance and reliability.
A Hybrid PSO-GCRA Framework for Optimizing Control Systems Performance Hussein, Ahmad MohdAziz; Alomari, Saleh Ali; Almomani, Mohammad H.; Zitar, Raed Abu; Migdady, Hazem; Smerat, Aseel; Snasel, Vaclav; Abualigah, Laith
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1738

Abstract

Optimization is essential for improving the performance of control systems, particularly in scenarios that involve complex, non-linear, and dynamic behaviors. This paper introduces a new hybrid optimization framework that merges Particle Swarm Optimization (PSO) with the Greater Cane Rat Algorithm (GCRA), which we call the PSO-GCRA framework. This hybrid approach takes advantage of PSO's global exploration capabilities and GCRA's local refinement strengths to overcome the shortcomings of each algorithm, such as premature convergence and ineffective local searches. We apply the proposed framework to a real-world load forecasting challenge using data from the Australian Energy Market Operator (AEMO). The PSO-GCRA framework functions in two sequential phases: first, PSO conducts a global search to explore the solution space, and then GCRA fine-tunes the solutions through mutation and crossover operations, ensuring convergence to high-quality optima. We evaluate the performance of this framework against benchmark methods, including EMD-SVR-PSO, FS-TSFE-CBSSO, VMD-FFT-IOSVR, and DCP-SVM-WO. Comprehensive experiments are carried out using metrics such as Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and convergence rate.  The proposed PSO-GCRA framework achieves a MAPE of 2.05% and an RMSE of 3.91, outperforming benchmark methods, such as EMD-SVR-PSO (MAPE: 2.85%, RMSE: 4.49) and FS-TSFE-CBSSO (MAPE: 2.98%, RMSE: 4.69), in terms of accuracy, stability, and convergence efficiency. Comprehensive experiments were conducted using Australian Energy Market Operator (AEMO) data, with specific attention to normalization, parameter tuning, and iterative evaluations to ensure reliability and reproducibility.
Enhanced RSA Optimized TID Controller for Frequency Stabilization in a Two-Area Power System Ekinci, Serdar; Eker, Erdal; Izci, Davut; Smerat, Aseel; Abualigah, Laith
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i4.1644

Abstract

This study presents an enhanced reptile search algorithm (ImRSA) optimized tilt-integral-derivative (TID) controller for load frequency control (LFC) in a two-area power system consisting of photovoltaic (PV) and thermal power units. The ImRSA integrates Lévy flight and logarithmic spiral search mechanisms to improve the balance between exploration and exploitation, resulting in more efficient optimization performance. The proposed controller is tested against the original reptile search algorithm (RSA) and other state-of-the-art optimization methods, such as modified grey wolf optimization with cuckoo search, black widow optimization, and gorilla troops optimization. Simulation results show that the ImRSA-optimized TID controller outperforms these approaches in terms of undershoot, overshoot, settling time, and the integral of time-weighted absolute error metric. Additionally, the ImRSA demonstrates robustness in managing frequency deviations caused by solar radiation fluctuations in PV systems. The results highlight the superior efficiency and reliability of the proposed method, especially for renewable energy integration in modern power systems.
Hybrid long short-term memory and decision tree model for optimizing patient volume predictions in emergency departments Abatal, Ahmed; Mzili, Mourad; Benlalia, Zakaria; Khallouki, Hajar; Mzili, Toufik; Billah, Mohammed El Kaim; Abualigah, Laith
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp669-676

Abstract

In this study, we address critical operational inefficiencies in emergency departments (EDs) by developing a hybrid predictive model that integrates long short-term memory (LSTM) networks with decision trees (DT). This model significantly enhances the prediction of patient volumes, a key factor in reducing wait times, optimizing resource allocation, and improving overall service quality in hospitals. By accurately forecasting the number of incoming patients, our model facilitates the efficient distribution of both human and material resources, tailored specifically to anticipated demand. Furthermore, this predictive accuracy ensures that EDs can maintain high service standards even during peak times, ultimately leading to better patient outcomes and more effective use of healthcare facilities. This paper demonstrates how advanced data analytics can be leveraged to solve some of the most pressing challenges faced by emergency medical services today.
Hybrid horned lizard optimization algorithm-aquila optimizer for DC motor Aribowo, Widi; Abualigah, Laith; Oliva, Diego; Mzili, Toufik; Sabo, Aliyu
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1673-1682

Abstract

This research presents a modification of the horned lizard optimization (HLO) algorithm to optimize proportional integral derivative (PID) parameters in direct current (DC) motor control. This hybrid method is called horned lizard optimization algorithm-aquila optimizer (HLAO). The HLO algorithm models various escape tactics, including blood spraying, skin lightening or darkening, crypsis, and cellular defense systems, using mathematical techniques. HLO enhancement by modifying additional functions of aquila optimizer improves HLO performance. This research validates the performance of HLAO using performance tests on the CEC2017 benchmark function and DC motors. From the CEC2017 benchmark function simulation, it is known that HLAO's performance has promising capabilities. By simulating using 3 types of benchmark functions, HLOA has the best value. Tests on DC motors showed that the HLAO-PID method had the best integrated of time-weighted squared error (ITSE) value. The ITSE value of HLOA is 89.25 and 5.7143% better than PID and HLO-PID.
A hybrid adaptive neuro-fuzzy inference system and reptile search algorithm model for wind power forecasting Al-Widyan, Mohamad I.; Abualigah, Laith; Jaradat, Ghaith M.; Alsmadi, Mutasem Khalil
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2857-2873

Abstract

Estimating the number of wind ranches generated in the upcoming minutes, hours, or days is the focus of wind power forecasting. Deep learning has garnered a lot of interest in wind control estimation because of how well they perform classification, grouping, and recurrence. The adaptive neuro-fuzzy inference system was successfully applied in wind power forecasting. However, its performance relies on optimal selection of hyperparameters. This study introduces a novel predictive model by incorporating the reptile search algorithm with adaptive neuro-fuzzy inference system (ANFIS) for short-term wind power forecasting. It employs reptile search algorithm (RSA), known for adjustable parameters, disentangled search, and consistent outcomes, to optimize ANFIS’s hyperparameters. Additionally, via exploitation during training, RSA performs a selection of best features in the dataset that contributes to the classification accuracy of ANFIS. This aims to enhance precision of the anticipated yield. Employing authentic wind power data from Jordan is undertaken to evaluate efficiency. The performance is compared with alternative techniques, including artificial neural networks, random forests, and support vector machines. Findings showed that ANFIS-RSA performs competitively for the well-known Chinese benchmark dataset (99.9% accuracy; 0.99 R2; 10.54 MAE; 11.62 RMSE) and is more robustly accurate than others over the Jordanian dataset (0.84.6% accuracy; 0.96 R2; 0.098 MAE; 0.203 RMSE).
Enhancing photovoltaic parameters based on modified puma optimizer Aribowo, Widi; Abualigah, Laith; Oliva, Diego; Elsayed Abd Elaziz, Mohamed; Soleimanian Gharehchopogh, Farhad; A. Shehadeh, Hisham; Sabo, Aliyu; Prapanca, Aditya
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8977

Abstract

This article presents a photovoltaic (PV) optimization approach using the puma optimizer (PO) approach, which has been enhanced by utilizing Lévy flight optimization. The name of this approach is modified puma optimizer (MPO). PV generation systems are essential for sustainable solar energy utilization. It is an innovation and clean energy. There is an urgent demand for suitable and reliable simulation and optimization techniques for PV systems. This will result in increased efficiency. Algorithms with a high degree of reliability are needed to ensure optimal PV parameters. This study was conducted with MATLAB software. This article introduces the original PO method as a means to evaluate the performance of the MPO approach. The root mean square error (RMSE) function serves as a benchmark. Based on the simulation findings, the MPO approach shows superior RMSE compared to the PO method, specifically at a value of 0.0026%.
Co-Authors A. Shehadeh, Hisham Abatal, Ahmed Aditya Prapanca Al-Widyan, Mohamad I. Aljohani, Abeer Almomani, Mohammad H. Alomari, Saleh Ali Alsmadi, Mutasem Khalil Amaliah, Fithrotul Irda As’ad Shidqy Aziz Ayusta Lukita Wardani B, Nur Vidia Laksmi Benlalia, Zakaria Billah, Mohammed El Kaim Eker, Erdal Ekinci, Serdar Elsayed Abd Elaziz, Mohamed Hussein, Ahmad MohdAziz Izci, Davut Jaradat, Ghaith M. Khallouki, Hajar Mahendra Widyartono Migdady, Hazem Mzili, Mourad Mzili, Toufik Oliva, Diego Rahmadian, Reza Sabo, Aliyu Smerat, Aseel Snasel, Vaclav Soleimanian Gharehchopogh, Farhad Widi Aribowo Zangana, Hewa Majeed Zitar, Raed Abu