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Alfian Ma'arif
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International Journal of Robotics and Control Systems
Published by Association for Scientific Computing Electronics and Engineering
ISSN : -     EISSN : 27752658     DOI : https://doi.org/10.31763/ijrcs
Core Subject : Engineering,
Control & Systems Engineering Electrical & Electronics Engineering
International Journal of Robotics and Control Systems is open access and peer-reviewed international journal that invited academicians (students and lecturers), researchers, scientists, and engineers to exchange and disseminate their work, development, and contribution in the area of robotics and control technology systems experts. Its scope includes Industrial Robots, Humanoid Robot, Flying Robot, Mobile Robot, Proportional-Integral-Derivative (PID) Controller, Feedback Control, Linear Control (Compensator, State Feedback, Servo State Feedback, Observer, etc.), Nonlinear Control (Feedback Linearization, Sliding Mode Controller, Backstepping, etc.), Robust Control, Adaptive Control (Model Reference Adaptive Control, etc.), Geometry Control, Intelligent Control (Fuzzy Logic Controller (FLC), Neural Network Control), Power Electronic Control, Artificial Intelligence, Embedded Systems, Internet of Things (IoT) in Control and Robot, Network Control System, Controller Optimization (Linear Quadratic Regulator (LQR), Coefficient Diagram Method, Metaheuristic Algorithm, etc.), Modelling and Identification System.
Arjuna Subject : Teknik (semua kategori) - Teknik Kontrol dan Sistem
Articles 35 Documents
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Search results for , issue "Vol 5, No 1 (2025)" : 35 Documents clear
Performance Enhancement of DC Motor Drive Systems Using Genetic Algorithm-Optimized PID Controller for Improved Transient Response and Stability Aziz, Ghada Adel; Abdullah, Fatin Nabeel; Shneen, Salam Waley
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.v5i1.1602

Abstract

Some systems require mechanical power, which can be used in many applications, including rotating vehicle wheels, pulling elevators, and moving robot limbs, etc. Mechanical or kinetic energy can be produced and generated from electrical machines, which can be represented by an electric motor, which is a machine that operates on electrical energy, i.e. input energy, and produces mechanical energy, i.e. output energy. One of the most common and widely used motors is the DC motor, which has features that make it a matter of interest to researchers, producing and manufacturing companies to develop and improve its performance. The motor is characterized by flexibility, low cost, durability, and the ability to control the speed and position of the rotating member using traditional, expert and intelligent control systems to achieve appropriate performance according to the field of application. In linear systems, traditional systems (Proportional-Integral-Derivative Controller (PID) have succeeded, while their performance is weak and unacceptable in nonlinear systems. Therefore, expert and intelligent control systems are relied upon to improve the performance of electric motors. It is proposed to implement and operate an electric motor control system using the genetic algorithm to verify its effectiveness in improving performance compared to the traditional one (PID). The genetic algorithm was chosen to address the optimization challenges because it is commonly used in artificial intelligence applications in various fields that are suitable for real time. Therefore, this study presented improving the performance of the traditional controller using the genetic algorithm. Through comparison, the possibility of improving the system performance with changing operating conditions was verified by adjusting the parameters of the traditional controller. The simulation was performed using Matlab, and the DC motor specifications included a rated voltage of 32.4 V, a rated current of 2 A, a rated speed of 536 rad/s, and a power of 54 watts. The conventional controller is responsible for the basic feedback control, while the GA-PID controller optimizes the control parameters to improve the system performance.
Optimizing Low-Voltage Ride-Through in DFIG Wind Turbines via QPQC-Based Predictive Control for Grid Compliance Badawi, Ahmed; Soliman, Mostafa; Elzein, I. M.; Alqaisi, Walid
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.v5i1.1661

Abstract

This paper introduces a novel Model Predictive Control (MPC)-based strategy to enhance Low-Voltage Ride-Through (LVRT) capability for wind turbines equipped with Doubly Fed Induction Generators (DFIGs). According to modern grid codes, grid-connected wind turbines must remain operational during voltage dips and support the grid by injecting both active and reactive power. However, voltage dips pose significant challenges for (DFIG)-based wind turbines because voltage dips can induce significant large inrush current in the rotor, potentially damaging the rotor converter. Conventional control methods employ proportional-integral (PI) controllers for rotor current regulation and crowbar circuits to protect the converter by diverting high rotor currents away from the converter when they exceed their safe limit. While effective in protecting the hardware, crowbar activation temporarily disconnects the rotor from control, leading to a loss of power injection capabilities and noncompliance with grid codes. To overcome these limitations, this paper proposes an MPC-based rotor current controller formulated as a Quadratically-Constrained Quadratic Programming (QCQP) optimization problem. This controller explicitly incorporates rotor current and voltage constraints while optimizing control performance during grid faults. MATLAB-based simulations for both low- and medium-voltage dips demonstrate the superiority of the proposed approach over conventional PI controllers. The results confirm that the MPC strategy ensures LVRT compliance without the need for a crowbar circuit, maintaining stability and improving performance during a wide-range of fault conditions.
Euler-Maclaurin Method for Approximating Solutions of Initial Value Problems Alomari, Mohammad W.; Batiha, Iqbal M.; Alkasasbeh, Wala’a Ahmad; Anakira, Nidal; Jebril, Iqbal H.; Momani, Shaher
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.v5i1.1560

Abstract

This work is dedicated to advancing the approximation of initial value problems through the introduction of an innovative and superior method inspired by the Euler-Maclaurin formula. This results in a higher-order implicit corrected method that outperforms Taylor’s and Runge–Katta’s methods in terms of accuracy. We derive an error bound for the Euler-Maclaurin higher-order method, showcasing its stability, convergence, and greater efficiency compared to the conventional Taylor and Runge-Katta methods. To substantiate our claims, numerical experiments are provided, highlighting the exceptional efficiency of our proposed method over the traditional well-known methods.
Parametric Analysis of Climate Factors for Monthly Weather Prediction in Ghardaïa District Using Machine Learning-Based Approach: ANN-MLPs Dahmani, Abdennasser; Ammi, Yamina; Ikram, Kouidri; Kherrour, Sofiane; Hanini, Salah; Al-Sabur, Raheem; Laidi, Maamar; Ma’arif, Alfian; Sharkawy, Abdel-Nasser
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.v5i1.1651

Abstract

In the rapidly developing field of smart cities, accurately predicting weather conditions plays a vital role in various sectors, including industry, tourism, agriculture, social planning, architecture, and economic development. Unfortunately, the instruments used (such as pyranometers, barometers, and thermometers) often suffer from low accuracy, high computational costs, and a lack of robustness. This limitation affects the reliability of weather predictions and their application across these critical areas. This study proposes artificial neural network-multilayer perceptrons (ANN-MLPs). A dataset of 480 data points was used, with 80% allocated for the training phase, 10% for the validation phase, and 10% for the testing phase. The best results were obtained with the structure 6-17-1 (6 inputs, 17 hidden neurons, and 1 output neuron) to predict weather condition data in the Ghardaïa district. Weather conditions parameters include air temperature, relative humidity, wind speed, and cumulative precipitation. Results showed that the most relevant input factors are, in order of importance: earth-sun distance (DT-S) with a relative importance (RI) of 31.10%, factor conversion (d) with an RI of 26.05%, and solar radiation (SR) with an RI of 16.26%. The contribution of the elevation of the sun (HI) has an RI of 13.29%. The optimal configuration includes seventeen neurons in the hidden layer with a logistic sigmoid activation function and a Levenberg–Marquardt learning algorithm, resulting in a root mean square error (RMSE) of 3.3043% and a correlation coefficient (R) of 0.9683. The proposed model can predict both short- and long-term climate factors such as solar radiation, air temperature, and wind energy in areas with similar conditions.
Comparative Analysis of Sensor Fusion for Angle Estimation Using Kalman and Complementary Filters Chotikunnan, Phichitphon; Khotakham, Wanida; Ma'arif, Alfian; Nirapai, Anuchit; Javana, Kanyanat; Pisa, Pawichaya; Thajai, Phanassanun; Keawkao, Supachai; Roongprasert, Kittipan; Chotikunnan, Rawiphon; Imura, Pariwat; Thongpance, Nuntachai
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.v5i1.1674

Abstract

In engineering, especially for robots, navigation, and biomedical uses, accurate angle estimation is absolutely crucial. Using data from the IMU6050 sensor, which combines accelerometer and gyroscope readings, this work contrasts two sensor fusion methods: the Kalman filter and the complementary filter. The aim of the research is to find the most efficient filtering method for preserving accuracy and resilience throughout several motion contexts, including low-noise (standard rotation) and high-noise (external disturbances). With an eye toward improving sensor accuracy in dynamic applications, the study contribution is a thorough investigation of filter performance under different noise levels. MATLAB quantified estimate accuracy using key metrics like root mean square error (RMSE) and mean absolute error (MAE). Under controlled noise levels, our approach included methodical error analysis of both filters. Results show that, especially under low-noise conditions, the Kalman filter beats the complementary filter in terms of lower MAE and RMSE; it also shows adaptability and robustness in high-noise environments with much fewer errors than accelerometer-only and complementary filter outputs. These results show the relevance of the Kalman filter in practical settings like robotic control, motion tracking, and possible biomedical equipment, including patient positioning systems and wheelchairs with balance control. Future studies might investigate the implementation of the Kalman filter in sophisticated systems requiring accuracy, such as telemedicine robots or autonomous navigation. This work develops sensor fusion techniques and offers understanding of consistent sensor data processing in several operating environments.
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.
Design, Modeling, and Simulation of A New Adaptive Backstepping Controller for Permanent Magnet Linear Synchronous Motor: A Comparative Analysis Maamar, Yahiaoui; Elzein, I. M.; Alnami, Hashim; Brahim, Brahimi; Benameur, Afif; Mohamed, Horch; Mahmoud, Mohamed Metwally
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.v5i1.1425

Abstract

In this paper, a nonlinear adaptive position controller for a permanent magnet linear synchronous motor based on a newly developed adaptive backstepping control approach is discussed and analyzed. The backstepping approach is a systematic method; it is used for non-linear systems such as the linear synchronous motor. This controller combines the notion of the Lyapunov function, which is based on the definition of a positive energy function; to ensure stability in the sense of Lyapunov, it is necessary to ensure the negativity of this function by a judicious choice of a control variable called virtual control. But this method is mainly based on the mathematical model of the permanent magnet linear synchronous machine (PMLSM) which makes this control sensitive to the variation of the parameters of the machine, to overcome this problem an adaptive control was proposed, the adaptive backstepping control approach is utilized to obtain the robustness for mismatched parameter uncertainties and disturbance load force. The overall stability of the system controller and adaptive low is shown using the Lyapunov theorem. The validity of the proposed controller is supported by computer simulation results.
Development of a Testbed for Autonomous Navigation of an Off-Shelf Quadrotor Based on Ultra-Wide-Band Real-Time Localization Gachoki, Nelson Muchiri; Kamau, Stanley; Ikua, Bernard
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.v5i1.1650

Abstract

Recent advances in autonomous aerial vehicle research, from theoretical simulations to experimental validations, has triggered demand for reliable proof-of-concept test-beds. Although such test-beds have been developed in some advanced drone research laboratories, their cost, expertise and complexity place them out of reach for upcoming research teams. This raises the need for development of less complex and affordable testbeds for quadrotor research. The contribution of this research is provision of low-cost autonomous quadrotor test-bed for proof-of-concept. The development of the proposed testbed entails configuration of Ultra-Wide-Band (UWB) based Real-Time Localization System (RTLS) to transmit position data of multiple agents to LabVIEW software for analysis and decision making. The autonomous navigation commands for the quadrotor are generated from the LabVIEW software and relayed through customized USB interface to the flight control module. The commands alter the digital state of Arduino board pins which are connected to the flight controller hence manipulating navigation pitch and roll parameters. The validation tests performed in the test-bed involved quadrotor hover and navigation in pursuit of the ground agent. The results demonstrate that UWB based RTLS achieves high precision of 99% when the modules are stationary but the precision reduced to 90% when the modules were in motion, which may be attributed actuating signal transmission delays. The results also showed that the Arduino based electronic flight controller is capable of generating flight paths to follow the ground robot in real-time with precision deviations of under 10% which is at par with other research test beds. This novel testbed provides a costeffective and accurate solution for autonomous flight testing, with precision comparable to visual-based testbeds, but at a much lower cost. Further research is encouraged to explore how the system performs with more than two agents and on a wider test arena.
Function Approximation Technique-based Adaptive Force-Tracking Impedance Control for Unknown Environment Azlan, Norsinnira Zainul; Yamaura, Hiroshi; Suwarno, Iswanto
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.v5i1.1029

Abstract

An accurate force-tracking in various applications may not be achieved without a complete knowledge of the environment parameters in the force-tracking impedance control strategy. Adaptive control law is one of the methods that is capable of compensating parameter uncertainties. However, the direct application of this technique is only effective for time-invariant unknown parameters. This paper presents a Function Approximation Technique (FAT)-based adaptive impedance control to overcome uncertainties in the environment stiffness and location with consideration of the approximation error in the FAT representation. The target impedance for the control law have been derived for unknown time-varying environment location and constant or time-varying environment stiffness using Fourier Series. This allows the update law to be derived easily based on Lyapunov stability method. The update law is formulated based on the force error feedback. Simulation results in MATLAB environment have verified the effectiveness of the developed control strategy in exerting the desired amount of force on the environment in x-direction, while precisely follows the required trajectory along y-direction, despite the constant or time-varying uncertainties in the environment stiffness and location. The maximum force error for all unknown environment tested has been found to be less than 0.1 N. The test outcomes for various initial assumption of unknown stiffness between 20000N/m to 120000N/m have shown consistent and excellent force tracking. It is also evident from the simulation results that the proposed controller is effective in tracking time-varying desired force under the limited knowledge of the environment stiffness and location.
Performance Enhancement of Dual-Star Induction Machines Using Neuro-Fuzzy Control and Multi-Level Inverters: A Comparative Study with PI Controllers Mezaache, Salah Eddine; Zaidi, Elyazid
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.v5i1.1670

Abstract

This paper proposes a hybrid speed control strategy for Dual-Star Induction Machines (DSIMs) supplied by Multi-Level Inverters (MLIs). The proposed approach integrates a Neuro-Fuzzy Controller (NFC) with an Indirect Field-Oriented Control (IFOC) technique, leveraging the adaptive learning capabilities of an Artificial Neural Network (ANN) to optimize the NFC parameters. This strategy achieves significant enhancements in speed regulation performance, including a 20% reduction in settling time, a 15% decrease in overshoot, and minimized steady-state error. The NFC's online adaptive learning capability enables real-time adjustments, outperforming the PI controller in handling rotor resistance variations and load disturbances. Simulation results demonstrate a 35% reduction in torque ripple and a 20% improvement in speed regulation compared to PI controllers. The NFC also exhibits faster response times during torque change and remains unaffected by 50% rotor resistance variations. Additionally, the NFC controller achieves up to 51% reduction in Total Harmonic Distortion (THD) compared to the PI controller.  Increasing the inverter voltage level from m=2 to m=7 significantly reduces electromagnetic torque ripple, demonstrating a direct correlation between higher inverter levels and improved torque ripple performance. These improvements position the NFC-based strategy as a promising solution for industrial applications requiring precise speed control, such as robotics, electric vehicles, and automation systems.

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