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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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Disturbance Observer-Based Intelligent Control for Trajectory Tracking in Redundant Robotic Manipulators Al-Mola, Mohammed H. A.; Abdelmaksoud, Sherif I.
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.v5i3.1905

Abstract

Redundant robotic manipulators require advanced control strategies to maintain stability and precision in the presence of dynamic disturbances. This study proposes an intelligent control scheme integrating Active Force Control (AFC) with a Proportional–Integral–Derivative (PID) controller to enhance the performance of a two-degree-of-freedom (2-DOF) robotic manipulator. The proposed AFC-PID controller is designed to suppress the effects of external disturbances, including torque noise. Comparative simulations demonstrate that the AFC-PID approach outperforms the conventional PID controller, providing improved stability and tracking accuracy in both manipulator links. Moreover, it compared with the Sliding Mode Control (SMC) control to verify the efficiency of the proposed controller. Quantitatively, the Integral Square Error (ISE) improvements compared to PID for link 1 and link 2 are 82.83% and 65.57%, respectively. Under disturbance conditions, performance gains are also observed, with ISE reductions of 86.2% and 65.36% for links 1 and 2. These results confirm the robustness and effectiveness of the proposed controller in maintaining consistent performance under challenging conditions. This is a significant improvement, reflecting the superiority over the conventional systems.
Formulation of a Lyapunov-Based PID Controller for Level Control of a Coupled-Tank System Kamarudin, Muhammad Nizam; Md Rozali, Sahazati; Azam, Sazuan Nazrah Mohd; Hairi, Mohd Hendra; Zakaria, Muhammad Iqbal
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.v5i3.1947

Abstract

This manuscript proposes a Proportional-Integral-Derivative (PID) control algorithm based on Lyapunov stability criteria. To verify the technique, the study is further extended to investigate its feasibility in controlling the liquid level of a coupled-tank system. A comparative study is conducted with the well-established Ziegler-Nichols tuning technique, known for its rapid and aggressive response. While Ziegler-Nichols often achieves quick tuning, it is prone to instability or degraded performance, particularly in systems with slow dynamics, such as the coupled-tank system. The results demonstrate the practical viability of the Lyapunov-based PID approach. The findings show that the Lyapunov-PID controller significantly outperforms the Ziegler-Nichols PID, achieving a 33.63% reduction in overshoot and a 45.14% improvement in settling time. These improvements highlight the advantage of incorporating Lyapunov-based criteria in PID design for systems where stability and performance are critical. However, the proposed approach has limitations such as increased computational complexity and the need for abstract tuning effort, along with difficulty in selecting appropriate Lyapunov functions.
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.
Accelerating Convergence in Data Offloading Solutions: A Greedy-Assisted Genetic Algorithm Approach Zulfa, Mulki Indana; Chrismawan, Stephen Prasetya; Hartoyo, Adhwa Moyafi; Nursakti, Wafdan Musa; Ahmed, Waleed 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.v4i4.1652

Abstract

Data offloading, a technique that distributes data across the network, is crucial for alleviating congestion and enhancing system performance. One challenge in this process is optimizing web caching, which can be modeled as a dynamic knapsack problem in edge networks. This study introduces a Greedy-Assisted Genetic Algorithm (GA-Greedy) to tackle this challenge, accelerating convergence and improving solution quality. The greedy heuristic is integrated into the GA at two stages: during initialization to create a superior starting population, and at the end of each iteration to refine solutions generated through genetic operations. The GA-Greedy’s effectiveness was evaluated using the IRcache dataset, focusing on hit ratio—an indicator of successful cache accesses that reduces network load and speeds up data retrieval. Results show that GA-Greedy outperforms traditional GA and standalone greedy algorithms, especially with smaller cache sizes. For instance, with a 3K cache size, the half-greedy GA achieved a hit ratio of 0.55, compared to 0.2 for the pure GA and 0.1 for the greedy algorithm. Similarly, the full-greedy GA reached a hit ratio of 0.45. By enhancing convergence and guiding the search, GA-Greedy enables more efficient data distribution in edge networks, reducing latency and improving user experience.

Page 35 of 37 | Total Record : 361

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