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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 25 Documents
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Search results for , issue "Vol 5, No 3 (2025)" : 25 Documents clear
A Systematic Review of Inverse Kinematics Methods for Fixed-Base Serial Manipulators: Analytical, Numerical, and Machine Learning Methods Trullo, Hernan Dario; Alban, Oscar Andres Vivas
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.1820

Abstract

Inverse kinematics is essential for precision tasks in fixed-base serial robots, such as surgical robotics or high-speed manufacturing, where delays or errors can have critical consequences. Current inverse kinematic methods face a fundamental trade-off: analytical solutions are fast but limited to spherical-wrist manipulators, while numerical and AI-based approaches sacrifice speed for generality. Despite prior reviews comparing performance metrics, no study provides a unified quantitative framework to guide method selection based on robot structure or application requirements. This systematic review addresses this lack of (1) quantitatively contrasting (response time, accuracy) analytical, numerical, and AI-based methods using studies in fields such as industrial robotics, medicine, and collaborative spaces and (2) identifying optimal hybrid strategies for real-time applications such as path planning. Using PRISMA, we analyzed 47 peer-reviewed articles from Scopus/Web of Science between 2019-2024, excluding algorithms for continuous, parallel, or mobile robots to focus solely on fixed-base serial architectures; selecting topics like ’inverse kinematics and serial robots and analytical or numeric or machine learning methods’. The review reveals that 32% of the analyzed methods are numerical, while 30% are AI-based approaches, reflecting the growing interest in data-driven solutions for IK problems; this scenario highlights the implementation of these methods given the limitations of analytical methods. Moreover, 56% of the nonanalytical approaches achieve an accuracy better than 0.01 mm; and about 70% of these approaches have response times exceeding 20 ms or don´t evaluate the metric, highlighting a critical bottleneck for real-time use. We conclude that hybrid IK methods, combined with standardized validation protocols, are essential for critical applications like robotic surgery. Future work must address benchmarking gaps, especially in AI-based IK, to enable reliable adoption in industry.
Improved of Sliding Mode Control for Maximum Power Point Tracking in Solar Photovoltaic Applications Under Varying Conditions Hassan, Alaq F.; Nawfal, Mohanad; Al-Khazraji, Huthaifa; Humaidi, Amjad J.
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.1925

Abstract

The solar energy generation sector has received widespread interest compared to other types of sustainable energy generation. This is owing to its high efficiency and the availability of environmental factors essential to the operation of these systems in various parts of the world. However, increased the power extracted from these systems are a critical issue as their conversion efficiency is low. Therefore, a maximum power point tracking (MPPT) controller is necessary in a photovoltaic generation system (PV) for maximum power extraction. This study aims to explore the performance of the MPPT system that uses an improved sliding mode controller (SMC) to identify and track a maximum power point (MPP) of a PV system and compares it to synergetic algorithm control (SACT). To implementing this purpose, MATLAB/Simulink model of a stand-alone PV panel is developed. Then, the analysis of the performance efficiency of the PV system based on the proposed MPPT methods are implemented under varying environmental conditions. Being able to track the MPP perfectly in the case of a sudden change in environment conditions, the improved SMC is proven by the results to be superior in stabilizing the boost converter's operation, leading to enhanced PV system stability. This has led to a reduction in power losses and an increase in efficiency.
Artificial Intelligence-Enhanced Sensorless Vector Control of Induction Motors Using Adaptive Neuro-Fuzzy Systems: Experimental Validation and Benchmark Analysis Bekhiti, Belkacem; Fragulis, George F.; Hariche, Kamel; Sharkawy, Abdel-Nasser
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.1950

Abstract

This study addresses the limitations of traditional Model Reference Adaptive Systems (MRAS) in sensorless induction motor (IM) control, particularly the degraded performance at low speeds and under dynamic load conditions. The main objective is to enhance speed and torque estimation accuracy by replacing the classical proportional-integral (PI) adaptation mechanism with an adaptive neuro-fuzzy architecture. The research contribution lies in developing and experimentally validating two intelligent adaptation schemes: one based on fuzzy logic and another combining fuzzy inference with a recurrent neural network (RNN) within a sensorless field-oriented control (FOC) framework. The proposed system integrates a fuzzy logic-based estimator and an RNN-driven torque predictor to improve tracking precision and robustness. Real-time implementation was carried out on a 1.1 kilowatt, 1430 revolutions per minute induction motor using a dSPACE DS1104 platform. Comparative experiments were conducted under two challenging benchmark profiles that include load disturbances, parameter mismatches, and full-speed reversals. Results showed that the hybrid neuro-fuzzy controller reduced the steady-state speed error by 91 %, from 0.65 rad/s to 0.08 rad/s, and improved torque estimation accuracy by 42%, reducing SMAPE from 45.2 % to 26.3 %, compared to the PI-based MRAS. It also outperformed the standalone fuzzy and neural MRAS controllers in rise time, tracking error, overshoot suppression, and adaptation quality. These findings confirm that the proposed method provides improved estimation fidelity, enhanced control robustness, and reliable sensorless operation suitable for real-time industrial applications. The study concludes that the integration of neuro-fuzzy intelligence into MRAS-based control structures offers a technically effective and scalable solution for advanced IM drives.
Boost Converter Control Using Proportional-Integral-Derivative Controller Optimized by Whale Optimization Algorithm Thanoon, Mohammad A; Almaged, Mohammed; Abdulla, Abdulla Ibrahim
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.1912

Abstract

This work offers an improved control approach for a boost converter called WOA_PID by combining a Whale Optimization Algorithm (WOA) with a Proportional-Integral-Derivative (PID) controller. The main goal is to optimize the PID controller gains for better voltage control and improved system stability and performance. Although boost converters are employed for step-up DC-DC conversion, they have nonlinear dynamics and sudden load changes that create major problems in conventional controller tuning. This work guarantees improved transient response and lower steady-state error by using the WOA employed as an optimization tool to effectively optimize the PID gains by minimizing the Integral Square Error (ISE) performance index. Simulations are used to assess the suggested WOA_PID controller, which showed better performance than traditional PID tuning techniques. The key aspects are zero overshoot, quicker rise and settling time of 0.216 and 0.654 respectively as well as improved output voltage control under changing load situations. Findings verify the efficiency of the WOA-based tuning approach in optimizing the PID controller for boost converters, providing a robust solution for practical applications in power electronics.
High Gain Observer Based Backstepping Control Design for Nonlinear Single-Axis Driven Systems Mahmod, Rawnaq A.; Kadhima, Russul A.; Nawfal, Mohanad; Al-Khazraji, Huthaifa
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.1984

Abstract

In this paper, a backstepping (BS) control design approach is proposed for tracking angular position control problem of a single-input and single-output (SISO) nonlinear single-axis driven system. To implement proposed BS control, the states of the system should be available. To address this problem, a high gain observer (HGO) is introduced for estimating the states. The design parameters of the HGO based BS controller have been optimized using the circle search algorithm (CSA). Compare to other optimization algorithm, the CSA explores the search space in a circular trajectory which can enhance local exploitation. The CSA uses integral of absolute error (IAE) as the performance index for the tuning process. The effectiveness of the proposed controller is demonstrated through simulations. Firstly, for observer evaluation, simulation outcomes indicate that the HGO is capable to estimate the states of the system successfully. However, to evaluate the BS with other nonlinear controllers for tracking control problem, the synergetic (SG) control is proposed. The simulated data results based on IAE index revealed that the BS control has lower IAE value than the SG control where the value of the IAE of the system with the BS control is reduced by 19.4% in compares with the system with the SG control.
Autonomous Mobile Robots Path Planning with Integrative Edge Cloud-Based Ant Colony Optimization Siti Nur Lyana Karmila, Nor Azmi; Apandi, Nur Ilyana Anwar; Rafique, Majid; Muhammad, Nor Aishah
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.1884

Abstract

In recent years, Automated Mobile Robots (AMRs) have gained significant attention in industry and research applications, requiring efficient path-planning algorithms to optimize task performance. While widely adopted, conventional Ant Colony Optimization (ACO) algorithms suffer from low convergence rates and delays in task execution, particularly in dynamic environments due to insufficient exploration of this context. However, traditional Ant Colony Optimization (ACO) algorithms, widely used for AMR path planning, exhibit limitations such as low convergence rates and redundant recalculations, particularly in environments with frequently changing obstacles. To address these challenges, this study proposes an Integrative Edge Cloud-Based Ant Colony Optimization (IECACO) algorithm. IECACO incorporates a novel path retrieval mechanism and edge cloud computing infrastructure to minimize redundant path computation and improve convergence efficiency. The proposed algorithm is tested within a simulated 2D occupancy grid environment using both a 4×4 map for controlled experiments and a 20×20 map for comparative evaluation against a prior Improved ACO (IACO) study. Experimental simulation results, based on 50 independent runs in settings, demonstrate that IECACO achieves at least 4.76% reduction compared to traditional ACO. Based on the observation of 10 independent runs between IECACO and IACO, IECACO leading a significant reduction in both static and dynamic settings. Although this study is conducted in a simulated environment, the findings lay a foundation for future real-world implementations.
Intelligent Control of Rigid-Link Manipulators: A Systematic Review of Recent Advances and Future Trends Alwardat, M. Y.; M’bolo, O. E.-L.; Benslimane, Y.; Alwan, H. M.
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.2019

Abstract

As robotic manipulators increasingly operate in dynamic and safety-critical environments, the need for intelligent control strategies that ensure adaptability, robustness, and real-time performance has become critical. While earlier reviews have addressed aspects of this domain, they often lacked systematic rigor, overlooked emerging hybrid and learning-based approaches, or provided limited quantitative synthesis. The research contribution is a PRISMA-compliant systematic review of 80 peer-reviewed studies on intelligent control of rigid-link manipulators (RLMs) published between 2016 and 2024, offering both qualitative and structured comparative analysis. The methods reviewed include PID, sliding mode control (SMC), fuzzy logic, artificial neural networks (ANN), reinforcement learning (RL), genetic algorithms (GA), and hybrid combinations. Studies were assessed according to methodological clarity, experimental validation, reported performance metrics, and publication impact. A comparative summary of 25 representative studies-selected based on citation impact, methodological rigor, and diversity of control approaches-highlights performance trade-offs and strengths across techniques. The findings indicate a growing shift toward hybrid intelligent controllers, which demonstrate enhanced adaptability in addressing nonlinear dynamics and uncertainties. However, most studies remain simulation-based, with limited real-world validation and reproducibility. Major research gaps include the lack of standardized benchmarking, insufficient explainability, and limited generalizability across application domains. These insights support the development of deployable, interpretable, and reliable robotic controllers, particularly for industrial automation and medical robotics, where transparency and safety are paramount.
Optimization of a Robust Sigmoid PID Controller for Automatic Voltage Regulation Using the Nonlinear Sine-Cosine Algorithm with Amplifier Feedback Dynamic Weighted (AFDW) System Ahmed, Islam; Suid, Mohd Helmi; Ahmad, Mohd Ashraf; Ahmad, Salmiah; Jusof, Mohd Falfazli Mat; Tumari, Zaidi Mohd
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.1930

Abstract

The given paper presents a robust Sigmoid-based Proportional-Integral-Derivative (SPID) controller for Automatic Voltage Regulator (AVR) systems, optimized using the Nonlinear Sine Cosine Algorithm (NSCA) enhanced with the Amplifier Feedback Dynamic Weighted (AFDW) system. Conventional PID controllers are frequently struggling with parameter variations and external interruptions that lead to instability and reduced performances in AVR systems. The proposed SPID controller overcomes these limitations by incorporating nonlinear sigmoid functions, improving the AVR system's robustness and dynamic response. While the AFDW system improves stability and responsiveness by dynamically adjusting the feedback weight, the NSCA balances exploration and exploitation to optimize controller parameters. The primary contribution of the present research is an overview of the NSCA-SPID controller, which offers superior results in voltage regulation compared to traditional PID and other metaheuristic-tuned controllers, enhancement in settling time, elimination of overshoot, and improvement in steady-state error. Additionally, performance index and statistical performances are used to validate the proposed SPID controller. Simulation results demonstrate significant achievements that emphasize the effectiveness of the NSCA-SPID controller toward enhancing the AVR system stability and controller design’s performance under varying load conditions. Finally, the proposed NSCA-SPID controller provides a promising solution for Enhancing the regulation of voltage in power systems, providing Superior and efficient technique for practical applications.
Real-Time Experimental Study of Speed Control for PMSM Drive System on OPAL-RT Simulator Using Radial Basis Function Neural Network Hoang, Xuan Hung; Tran, Thanh Hai; Than, Phan Minh; Ngo, Thanh Quyen; Nguyen, Van Sy; Le, Tong Tan Hoa
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.2048

Abstract

This paper addresses the problem of improving speed control accuracy and disturbance rejection capability for Permanent Magnet Synchronous Motors (PMSMs), which are widely used in industrial applications requiring high-performance drives. Conventional controllers such as PID often exhibit limited performance under nonlinear and time-varying conditions. The sliding mode control combined with a Radial Basis Function Neural Network (RBFNN) is proposed to enhance robustness and adaptability to overcome these limitations. The main contribution of this study is the integration of an adaptive RBFNN to estimate and compensate for unknown disturbances in real time, ensuring precise and stable motor operation. The theoretical stability of the system is guaranteed based on Lyapunov’s theory. The proposed method is implemented in a MATLAB/Simulink environment and tested on the OPAL-RT OP5707XG real-time hardware platform. The control system includes a speed loop using the RBFNN and a current loop for field-oriented control. The motor is subjected to varying speed commands in three stages to evaluate performance under dynamic conditions. Simulation results show that the RBFNN controller significantly improves speed tracking accuracy, reduces overshoot, and adapts better to sudden changes compared to conventional PID control. Real-time experimental results further confirm the effectiveness of the controller, despite the presence of noise and hardware delays. Current control performance also demonstrates better torque production and phase symmetry under dynamic loading with the RBFNN. A comparative analysis between simulation and experimental data highlights the practical applicability of the proposed approach.
Person and Activity Recognition Based on Joint Motion Features Using Deep Learning with Drone Camera Yunardi, Riky Tri; Sardjono, Tri Arief; Mardiyanto, Ronny
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.1949

Abstract

The increasing demand for drone-based surveillance systems has raised significant concerns about advancements in person and activity recognition based on joint motion features within visual monitoring frameworks. This study contributes to developing deep learning models that improve surveillance systems by using RGB video data recorded by drone cameras. In this study, a framework for person and activity recognition based on 120 datasets is proposed, from drone camera-recorded videos of 10 subjects, each performing six movements: walking, running, jogging, boxing, waving, and clapping. Joint motion features, including joint positions and joint angles, were extracted and processed as one-dimensional series data. The 1D-CNN, LeNet, AlexNet, and AlexNet-LSTM architectures were developed and evaluated for classification tasks. Evaluation results show that AlexNet-LSTM outperformed the other models in person recognition, achieving a classification accuracy of 0.8544, a precision of 0.9161, a recall of 0.8575, and an F1-score of 0.8332, while AlexNet delivered superior performance in activity recognition with an accuracy of 0.8571, a precision of 0.8442, a recall of 0.8599, and an F1-score of 0.8463. The relatively small dataset size used likely favors simpler architectures like AlexNet. These findings highlight the effectiveness of joint motion features for person identification and emphasize the suitability of simpler classifier architectures for activity classification when working with small datasets.

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