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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 361 Documents
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Recent Developments and Future Prospects in Collision Avoidance Control for Unmanned Aerial Vehicles (UAVS): A Review Harun, Mohamad Haniff; Abdullah, Shahrum Shah; Aras, Mohd Shahrieel Mohd; Bahar, Mohd Bazli; Ali@Ibrahim, Fariz
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1482

Abstract

The industry has been significantly enhanced by recent developments in UAV collision avoidance systems. They made collision avoidance controllers for self-driving drones both affordable and hazardous. These low-maintenance, portable devices provide continuous monitoring in near-real time. It is inaccurate due to the fact that collision avoidance controllers necessitate trade-offs regarding data reliability. Collision avoidance control research is expanding significantly and is disseminated through publications, initiatives, and grey literature. This paper provides a concise overview of the most recent research on the development of autonomous vehicle collision avoidance systems from 2017 to 2024. In this paper, the state-of-the-art collision avoidance system used in drone systems, the capabilities of the sensors used, and the distinctions between each type of drone are discussed. The pros and cons of current approaches are analyzed using seven metrics: complexity, communication dependency, pre-mission planning, resilience, 3D compatibility, real-time performance, and escape trajectories.
Corrosion Prediction in the Oil Industry Using Deep Learning Techniques Al-Khalidi, Mustafa R.; Abdulsadda, Ahmad T
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1371

Abstract

Corrosion presents a significant challenge in the oil industry, causing both immediate and long-term damage. Effective early prediction and monitoring of corrosion are crucial to mitigating economic losses and environmental impacts. However, traditional methods for predicting and detecting corrosion are often time-consuming and inefficient. This study leverages convolutional neural networks (CNNs) within a deep learning framework to develop two automated detection models for internal and external corrosion. These models can extract hierarchical features directly from raw pixel data, enhancing prediction accuracy and efficiency. Our dataset, provided by the Iraqi Oil Company, includes drone-captured images (162 photos: 91 depicting corrosion and 71 showing no signs of corrosion) and ultrasonic sensor readings (250 rows of oil pipeline thickness measurements). We assess the performance of our CNN models using metrics such as accuracy, precision, recall, and F-score, and we perform regression analysis to evaluate prediction errors. This research introduces two innovative systems: a 2D CNN for classifying the presence or absence of external corrosion, and a 1D CNN for assessing internal corrosion levels, identifying areas with the highest corrosion rates, and estimating the remaining operational lifespan based on these rates. Additionally, we develop a user-friendly interface for these systems. Comparative analysis demonstrates the superior efficiency of our proposed approach over traditional and alternative methods. Our findings advance the understanding of artificial intelligence applications in corrosion prediction, offering robust models to prevent unexpected corrosion failures. Future work will explore the integration of additional factors, such as humidity and temperature sensors, to further enhance the system's accuracy and reliability.
Long Short-Term Memory vs Gated Recurrent Unit: A Literature Review on the Performance of Deep Learning Methods in Temperature Time Series Forecasting Furizal, Furizal; Fawait, Aldi Bastiatul; Maghfiroh, Hari; Ma’arif, Alfian; Firdaus, Asno Azzawagama; Suwarno, Iswanto
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1546

Abstract

Temperature forecasting is a crucial aspect of meteorology and climate change studies, but challenges arise due to the complexity of time series data involving seasonal patterns and long-term trends. Traditional methods often fall short in handling this variability, necessitating more advanced solutions to enhance prediction accuracy. LSTM and GRU models have emerged as promising alternatives for modeling temperature data. This study is a literature review comparing the effectiveness of LSTM and GRU based on previous research in temperature forecasting. The goal of this review is to evaluate the performance of both models using various evaluation metrics such as MSE, RMSE, and MAE to identify gaps in previous research and suggest improvements for future studies. The method involves a comprehensive analysis of previous studies using LSTM and GRU for temperature forecasting. Assessment is based on RMSE values and other metrics to compare the accuracy and consistency of both models across different conditions and temperature datasets. The analysis results show that LSTM has an RMSE range of 0.37 to 2.28. While LSTM demonstrates good performance in handling long-term dependencies, GRU provides more stable and accurate performance with an RMSE range of 0.03 to 2.00. This review underscores the importance of selecting the appropriate model based on data characteristics to improve the reliability of temperature forecasting.
Theoretical and Experimental Investigation of the Effect of Linear Fluid Power Control System Design on its Static and Dynamic Performance Qassim, A. I.; Sadak, Tahany W.; Rizk, Mahassen
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.v5i1.1550

Abstract

Fluid power control systems are widely used in automated systems like manufacturing, biomedical treatments, and food handling, as well as in optimizing aircraft wing design, mobile applications, and thermal management in electronic devices, energy transformation, and aerospace applications. This study investigated the static and dynamic characteristics of a linear fluid power control system utilizing either a servo control valve (SV) or a proportional directional flow control valve (PV). The study focused on evaluating performance differences between these two valve types while maintaining a constant oil temperature at 30°C. Experimental tests were conducted under varying supply pressures, loads, and valve types. A system was built to conduct real-time experiments. In this paper we studied the effect of valve flow rate at full opening, the actual supply pressure-decay, and studied the effect of the loading system on the performance. The aim of this paper is to find out which control valve is better in static and dynamic performance in real-world. Through comparing two hydraulic control valves designs, the experiment results show that the servo control valve (SV) offers a clear advantage over the proportional directional flow control valve (PV) in linear fluid power control systems operating at a constant temperature. The SV designs demonstrated superior performance in terms of flow rate, pressure retention, and dynamic response. This makes SV an optimal choice for applications requiring high flow rates, consistent pressure, and precise, rapid adjustments, especially in high-speed operations.
Self-Motion Control Exoskeleton for Upper Limb Rehabilitation with Perceptron Neuron Motion Capture Osman, Mohamad Afwan; Azlan, Norsinnira Zainul; Suwarno, Iswanto; Samewoi, Abdul Rahman; Kamarudzaman, Nohaslinda
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.v5i1.1030

Abstract

Upper limb rehabilitation robot can facilitate patients to regain their original impaired arm function and reduce therapist’ workload. However, the patient does not have a direct control over his/ her arm movement, which may lead to discomfort or even injury. This paper focuses on the development of a self-motion rehabilitation robot using Perception Neuron motion capture, where the movement of the impaired arm imitates the motion of the healthy limb.  The Axis Neuron software receives the healthy upper limb’s motion data from Perception Neuron. Unity serves as the simulation engine software that provides a 3-dimensional animation. ARDUnity acts as the communication platform between Unity software with Arduino. Arduino code is generated using Wire Editor, which avoids the need of the programming to be written in C++ or C#. Finally, Arduino instructs the exoskeleton motors that are connected to the impaired arm to move, following the healthy joint’s motion. The forward kinematics analysis for the robotic exoskeleton has been carried out to identify its workspace. Hardware experimental tests on the elbow and wrist flexion/ extension have shown the root-mean-square errors (RMSE) between the healthy and impaired arms movement to be 1.5809○ and 12.1955○ respectively. The average time delay between the healthy and impaired elbow movement is 0.1 seconds. For the wrist motion, the time delay is 1 second. The experimental results verified the feasibility and effectiveness of the Perception Neuron in realizing the self-motion control robot for upper limb rehabilitation. The proposed system enables the patients to conduct the rehabilitation therapy in a safer and more comfortable way as they can directly adjust the speed or stop the movement of the affected limb whenever they feel pain or discomfort.
Nonlinear Model Predictive Control of a Magnetic Levitation System Using Artificial Protozoa Optimizer Noaman, Mohanad N.; Ayoub, Abdurahman Basil; Mahmood, Saif S.
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.v4i4.1668

Abstract

A magnetic levitation system (Maglev) is a sensitive, multi-parameter, nonlinear, and unstable system that is utilized to levitate a ferromagnetic object in free space. Due to its vast applications, various research studies in the field of control strategy have become extremely important and challenging. This work proposes the design of a nonlinear model predictive (NMPC) control scheme for the object position control against the nonlinearities and uncertainties of a Maglev system. A novel bio-inspired Artificial Protozoa Optimization (APO) algorithm is used to fine-tune the NMPC parameters, which include best weighting matrices ( ), shorter prediction horizons ( ), and shorter time steps ( ) to minimize the objective cost function. The effective performance of the NMPC is verified using simulation-based results in MATLAB. The CasADi toolbox is utilized to solve nonlinear optimization problems and handle the nonlinearity of the Maglev system model. Simulations are implemented for three trajectories tracking (step, sine, and square) with 20% and without Maglev parameters perturbations. To prove the superiority of the proposed controller, comparisons are made with the conventional Linear Quadratic Regulator (LQR) and proportional-integral-derivative (PID) controllers. Two performance indices are introduced, Integral of Squared Error (ISE) and Integral of Absolute Error (IAE), to examine the tracking performances of the NMPC, LQR, and PID controller.  The NMPC controller has shown more efficient performance and accurate results than other controllers. The contributions of this work include a new optimization technique of APO, a new engineering application of the APO integrated with NMPC to control a Maglev system, consideration of inherent nonlinearities and system constraints, and robustness improvement under perturbation.
Design and Quality Evaluation of the Position and Attitude Control System for 6-DOF UAV Quadcopter Using Heuristic PID Tuning Methods Mien, Trinh Luong; Tu, Tran Ngoc
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.v4i4.1594

Abstract

Nowadays, UAV quadcopters are widely used in many fields, specially in transporting the lightweight goods parcels. This article aims to design and evaluation of the quality of the 6-DOF UAV quadcopter control system using heuristic PID tuning methods to ensure stable control of flight position and attitude. Firstly, the article presents the dynamic mathematical model of the 6-DOF UAV quadcopter, including 3 Euler angle variables and 3 flight position and altitude variables. From there, the article proposes the 6-DOF UAV control syste structure with two single control loops for flight attitude, yaw angle and two dual control loops for roll-pitch angles, flight position. And then, the article presents the application of the heuristic PID tuning methods to each control loop of a 6-DOF UAV quadcopter to calculate the PID controller parameters to ensure stable control the desired flight position and altitude. The simulation results and evaluating the 6-DOF UAV quadcopter control system quality in Matlab, using the proposed heuristic PID controllers, show that the PID controllers according to the Tyreus-Luyben method gives the best quality, with a steady-state error of less than 1%. The main contribution of this article is the comparative analysis of three heuristic PID tuning methods - Ziegler-Nichols, Tyreus-Luyben, PID tuner - for controlling the position and attitude of a 6-DOF UAV quadcopter.  These findings demonstrate that the proposed PID controllers can be effectively implemented in practical UAV applications, enhancing the stability and performance of quadcopters in various fields.
Optimized Vector Control Using Swarm Bipolar Algorithm for Five-Level PWM Inverter-Fed Three-Phase Induction Motor Yaseen, Farazdaq R.; Al-Khazraji, Huthaifa
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.v5i1.1713

Abstract

Induction motors (IMs) are commonly used in various applications such as robotics and automotive industries. This paper proposes an optimization of two proportional-integral (PI) controllers for a multi-level pulse width modulation (PWM) voltage-fed inverter linked to a three-phase IM. The paper aims to enhance inverter output quality, minimize harmonic distortion, and ensure robust, stable performance. The swarm bipolar algorithm (SBA) is introduced to elaborate the searching of the best settings of the PI controllers to achieve the desired response.  Harmonics lead to increased system losses by creating negative torque components. To address this problem, two modulation algorithms are proposed to generate three-phase voltage with minimum harmonics including space vector PWM (SVPWM) inverter and sinusoidal PWM (SPWM). Simulation results based on MATLAB/Simulink environment for various operation conditions such as sudden loads change and speed changes reveal that the proposed controller enhances the system's performance. Moreover, the five-level SVPWM inverter has a minimum threshold harmonic distortion (THD) compared to the five-level SPWM inverter where the THD is decreased from 40.24% for SPWM method to 13.67% for the SVPWM method.
A Review of Advanced Force Torque Control Strategies for Precise Nut-to-Bolt Mating in Robotic Assembly Ting, Terence Sy Horng; Goh, Yeh Huann; Chin, Kar Mun; Tan, Yan Kai; Chiew, Tsung Heng; Ma, Ge; How, Chong Keat
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.v5i1.1604

Abstract

Achieving precise alignment in high-precision robotic assembly is critical, where even minor misalignments can cause significant issues. Various control strategies have been developed to tackle these challenges, including passive compliance control (PCC), active control (AC), and manual teaching method (MTC). While AC is valued for its real-time adaptability, PCC and MTC offer advantages in simpler, cost-effective applications.   This review evaluates these strategies, emphasizing the integration of AI and machine learning to address the limitations of traditional AC methods, such as spiral and tilt searches, which are rigid, slow, and computationally demanding, making them unsuitable for dynamic environments. Machine Learning (ML) and Artificial Intelligence (AI) offer data-driven improvements in performance and adaptability over time. Techniques like Linear Regression, Artificial Neural Networks (ANNs), and Reinforcement Learning (RL) are explored for enhancing precision and real-time adaptability in complex tasks. These AI methods are applied in real-world industries, such as automotive and electronics manufacturing. The review compares control strategies and AI techniques, analyzing trade-offs in accuracy, speed, computational efficiency, and cost. It also discusses future directions, including hybrid control systems, advanced sensor integration, and more sophisticated AI algorithms. Ethical and safety considerations are highlighted, particularly in industrial settings where reliability and human-robot interaction are critical. This comprehensive review demonstrates AI's potential to enhance precision, reduce manual intervention, and improve performance in high-precision robotic assembly while guiding the selection of appropriate methods for specific applications.
Simulation and Modeling with Designing for the Proportional, Integral and Derivative Control of Industrial Robotic Arm by Using MATLAB/Simulink Shneen, Salam Waley; Juhi, Hasan H.; Najim, Hiba Ali
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.v4i4.1581

Abstract

This study aims to develop a control system for a robot arm, designed to perform precise movements along a predefined path, suitable for various industrial applications. The robot arm's movements are driven by three electric motors, each responsible for controlling a joint, enabling the arm to follow the required path accurately. To manage the complexity of multiple motors and dynamic movement requirements, an automated control system has been developed, tailored to meet the specific demands of the proposed task. A highly efficient, reliable, and safe control system design is being developed and simulated to evaluate its effectiveness in executing the required path. A simulation model is being constructed to assess the system's ability to follow the prescribed path, its responsiveness to disturbances and transient conditions, and the overall accuracy of the arm's movements. Simulation results will be analyzed to determine the system's performance across various scenarios, evaluating its adaptability to the work environment and its ability to achieve tasks with high accuracy, thereby enhancing system effectiveness.

Page 23 of 37 | Total Record : 361

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