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Journal of Robotics and Control (JRC)
Published by Universitas Muhammadiyah Yogyakarta
ISSN : 27155056     EISSN : 27155072     DOI : https://doi.org/10.18196/jrc
Core Subject : Science, Engineering,
Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering
Journal of Robotics and Control (JRC) is an international open-access journal published by Universitas Muhammadiyah Yogyakarta. The journal invites students, researchers, and engineers to contribute to the development of theoretical and practice-oriented theories of Robotics and Control. Its scope includes (but not limited) to the following: Manipulator Robot, Mobile Robot, Flying Robot, Autonomous Robot, Automation Control, Programmable Logic Controller (PLC), SCADA, DCS, Wonderware, Industrial Robot, Robot Controller, Classical Control, Modern Control, Feedback Control, PID Controller, Fuzzy Logic Controller, State Feedback Controller, Neural Network Control, Linear Control, Optimal Control, Nonlinear Control, Robust Control, Adaptive Control, Geometry Control, Visual Control, Tracking Control, Artificial Intelligence, Power Electronic Control System, Grid Control, DC-DC Converter Control, Embedded Intelligence, Network Control System, Automatic Control and etc.
Arjuna Subject : Teknik (semua kategori) - Teknik Kontrol dan Sistem
Articles 24 Documents
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Search results for , issue "Vol 5, No 4 (2024)" : 24 Documents clear
Design Intelligent Control Based on Fuzzy Neural Network and GA Algorithm for Prediction and Identification Nguyen, Van-Truong; Pham, Duc-Hung; Nguyen, Hoang-Nam
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.22115

Abstract

One of the central aspects in system identification and prediction is dealing with nonlinearity and uncertainties. This need involves the design of a novel method for achieving high efficiency and effectiveness, which is crucial for several applications. In this paper, a new intelligent control based on a hybrid fuzzy neural network (FNN) combined with a genetic algorithm (GA) is proposed for the prediction and identification of nonlinear systems. Two adaptations are considered in the proposed method: the backpropagation (BP) algorithm and the genetic algorithm method to correct various parameters in the neural network. Through adjustment, the proposed method not only achieves error convergence efficiently and quickly but also ensures continuous error reduction while avoiding the limitation of the regional optimal solution. Mackey-Glass differential delay and fuzzy neural system are utilized for system prediction and identification, respectively. Finally, the performance of the proposed method is justified through an application on a nonlinear system. Based on the findings, this paper proposed a hybrid strategy combining BP-GA and FNN where the outcome is greatly influenced by the balance of accuracy and computational efficiency.
Unlocking Solar Potential: Advancements in Automated Solar Tracking Systems for Enhanced Energy Utilization Hussain, Abadal-Salam T.; Hakim, Baqer A; Ahmed, Saadaldeen Rashid; Abed, Tareq Hamad; Taha, Taha A.; Hasan, Taif. S.; Hasan, Raed Abdulkareem; Hashim, Abdulghafor Mohammed; Tawfeq, Jamal Fadhil
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.19931

Abstract

The use of solar tracking systems has become vital and has established itself as a vital element in the generation of solar energy by enhancing the collection efficiency. This paper seeks to understand the necessity of shifting from conventional energy sources and why issues like scarcity of fossil fuel, and pollution are some of the hurdles toward achieving sustainable energy. Solar power, in particular, is one of the lights at the end of this tunnel since it pioneers a shift towards the usage of clean energy in the world. The subject of interests of the study is on how tracking systems help in maximizing energy collection from solar systems by interchanging it with the movement of sun’s path. It discusses the method that was followed, which involves selecting component, designing circuit and developing software together with presenting empirical data that was obtained from a three-day, Twenty-four-hour experiment. Outcomes show that there is an improvement on voltage stability, the level of solar irradiation and temperature regulation when the system is applied as compared to static system and its applicability for the enhancement of the renewable energy harnessing methods by using the solar tracking technology. Finally, it outlines the future research directions to continue exploring the proposed methods and its wider impact on renewable energy generation.
Visual Slam and Visual Odometry Based on RGB-D Images Using Deep Learning: A Survey Le, Van-Hung
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.22061

Abstract

Visual simultaneous localization and mapping (Visual SLAM) based on RGB-D images includes two main tasks: building an environment map and simultaneously tracking the location/motion trajectory of the image sensor, or called visual odometry (VO). Visual SLAM and VO are used in many applications as robot systems, autonomous mobile robots, supporting systems for the blind, human-machine interaction, industry, etc. With the strong development of deep learning (DL), it has been applied and brought impressive results when building Visual SLAM and VO from image sensor data (RGB-D images). To get the overall picture of the development of DL applied to building Visual SLAM and VO systems. At the same time, the results, challenges, and advantages of DL models to solve Visual SLAM and VO problems. In this paper, we proposed the taxonomy to conduct a complete survey based on three methods from RGB-D images: (1) using DL for the modules (depth estimation, optical flow estimation, visual odometry, mapping, and loop closure detection) of the Visual SLAM and VO framework; (2) using DL modules to supplement (feature extraction, semantic segmentation, pose estimation, map construction, loop closure detection, others module) to Visual SLAM and VO framework; (3) using end-toend DL to build Visual SLAM and VO systems. The studies were surveyed based on the order of methods, datasets, and evaluation measures, the detailed results according to datasets are also presented. In particular, the challenges of studies using DL to build Visual SLAM and VO systems are also analyzed and some of our further studies are also introduced.
Advanced Estimation of Brain Age Using Pre-trained 2D Convolutional Neural Networks on a Public Dataset Al-kubaisi, Mohannad; Ahmed, Ali Saadoon; Al-Barzinji, Shokhan M.; Khaleel, Arshad M.
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.22006

Abstract

This work introduces a brand-new approach to be employed for correctly assessing healthy person’s brain aging, masking what constitutes the most serious challenge in the fight against age-related cognitive decline. An approach is serviced by 2D CNNs, a simpler technology to state-of-the-art 3D model, to yield close to accurate forecast. Our algorithm improves telling in two respects. By virtue of this, we will utilize well-known ImageNet-pre-trained classifiers to suggest initial brain age predictions. This process sets the tone of the core of our business model in terms of dependability and reliability. Next, we improve the networks’ performance through progressively expanding their capacity via fine-tuning on pre-segmentation tasks using the neuroimaging datasets which are openly available. This stage increases the predictive accuracy in addition to ensuring that there is transparency and flexibility because it utilizes open datasets. Our research's strength is that it encompasses all techniques and fields necessary for brain age estimation and adopts justifiable evaluation metrics. As a result, this conduct adds to the literature. Our study not only points out deficiencies in private datasets but reels out the validity of our approach by using the public data instead to give the results another direction of accessibility and reproducibility.
Developing an Advanced Control System to Enhance Precision in Uncertain Conditions for Five-Bar Parallel Robot Through a Combination of Robust Adaptive Tracking Control Using CMAC Le, Tong Tan Hoa; Ngo, Thanh Quyen; Tran, Thanh Hai
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.22188

Abstract

Parallel robot systems have become increasingly applied due to significant advantages such as fast operating speed and high accuracy. Researchers are currently focusing on developing advanced control methods to increase the accuracy of this system. However, these advances face many challenges, including system dynamics and uncertain components in impact factors. Therefore, achieving a high level of accuracy remains a challenging problem and requires continued effort and careful research. This study proposes to use the Cerebellar Model Articulation Controller (CMAC) to estimate the nonlinear components of the system. By applying Lyapunov theory, this method focuses on adapting CMAC's online learning rules while ensuring stability and convergence. Besides using CMAC, the paper proposes a new signed distance method instead of sliding mode control (SMC) to handle input errors. This method aims to increase flexibility and adaptability and overcome the chattering of SMC in nonlinear systems. In particular, the research also adds a robust controller to ensure stability using Lyapunov to improve the system's accuracy. These recommendations increase the flexibility and accuracy of the control system, helping the system respond more quickly to changes and uncertainties in the operating environment. Finally, to demonstrate the effectiveness of the proposed controller, a five-bar parallel robot was chosen to conduct experiments in case situations. The results show that the proposed controller combined with signed distance achieves higher accuracy than other algorithms and is more stable in all cases mentioned in the research.
Cooperative Control of Bimanual Continuum Robots for Automated Knot-Tying in Robot-Assisted Surgical Suturing Quaicoe, Enoch; Nada, Ayman; Ishii, Hiroyuki; El-Hussieny, Haitham
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.21617

Abstract

Knot-tying, a crucial yet intricate surgical task, remains a challenge in Robot-assisted Minimally Invasive Surgery (RAMIS) performed under teleoperation. While existing studies on automated knot-tying mostly focus on rigid-link robots, whose dexterity, adaptability, and inherent safety in RAMIS are outperformed by continuum robots, this research takes a novel approach by developing a unique cooperative control scheme for bimanual continuum robots, specifically designed for automated knot-tying tasks in RAMIS. We meticulously plan two effective knot-tying trajectory scenarios and develop the cooperative control scheme for the bimanual continuum robots, leveraging the well-known Jacobian transpose kinematic algorithms to ensure their precise and collaborative knot-tying trajectory tracking performance. The control scheme incorporates a switching mechanism to guarantee the robots’ collaboration and synchronous operation during the knot-tying trajectory tracking process. The effectiveness of our cooperative control scheme is illustrated through simulation studies using MATLAB/Simulink in terms of trajectory tracking performance. Meanwhile, ten Monte Carlo simulations are conducted to analyze the system’s robustness against pulse disturbances that could occur in surgical settings. All ten simulations returned similar error values despite the increasing disturbance levels applied. The results not only demonstrate the seamless collaboration and synchronous operation of the bimanual continuum robots in precisely tracking the pre-planned knot-tying trajectories with errors less than 0.0017 m but also highlight the stability, effective tuning and robustness of our cooperative control system against pulse disturbances. This study demonstrates precision, robustness, and autonomy in bimanual continuum robotic knottying in RAMIS, promising safe robot-patient interaction and reduced surgeon workload and surgery time.
Parameter Extraction of Triple-diode Photovoltaic Model via RIME Optimizer with Neighborhood Centroid Opposite Solution Izci, Davut; Ekinci, Serdar; Ma'arif, Alfian
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.22256

Abstract

In this investigation, a novel application of the RIME optimizer with neighborhood centroid opposite solution is introduced to robustly estimate parameter values for an accurate photovoltaic triple-diode model. The suggested optimizer's performance is rigorously evaluated in comparison to other well-documented methods. The evaluation of the proposed optimizer is conducted using real data from the RTC France solar cell, and the results are assessed through various evaluation metrics, including root mean square error and statistical analyses for multiple independent runs. Specifically, the proposed optimizer demonstrates superior performance by achieving the lowest objective function values compared to other algorithms. Through a comprehensive quantitative and qualitative assessment, it can be inferred that the estimated parameters of the triple-diode model obtained using the proposed optimizer surpass the accuracy of those acquired through other optimization algorithms under consideration.
Enhancing Multilevel Inverter Performance: A Novel Dung Beetle Optimizer-based Selective Harmonic Elimination Approach Taha, Taha A.; Neamah, Muthanna Ibrahim; Ahmed, Saadaldeen Rashid; Taha, Faris Hassan; Bektaş, Yasin; Desa, Hazry; Yassin, Khalil Farhan; Ibrahim, Marwa; Hashim, Abdulghafor Mohammed
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.21722

Abstract

This paper introduces a novel approach for enhancing the performance of multilevel inverters by applying a dung beetle optimizer (DBO)-based Selective Harmonic Elimination (SHE) technique. Focusing on a 3-phase multilevel inverter (MLI) with a non-H-bridge structure, the proposed method offers advantages such as cost-effective hardware implementation and eliminating the traditional H-bridge inverter requirement. To assess its efficacy, we compare the presented DBO-based SHE technique (DBOSHE) with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), evaluating their ability to determine optimal switching angles for achieving low-distorted load voltage. Unlike methods reliant on time-consuming calculations or fixed solutions, DBO provides a flexible approach, considering multiple possibilities to yield accurate switching angles. Using Simulink, harmonic component values and Total Harmonic Distortion (THD) are obtained for each optimization technique, specifically emphasizing on 9-level and 11-level MLI topologies. Our study aims to identify the most effective optimization technique for achieving lower THD and THDe values while eliminating odd-order harmonics from the 3-phase load voltage. Finally, we demonstrate the effectiveness of employing DBO for THD and THDe optimization within the SHE technique.
Modeling and Designing of Active Vibration Isolation Platform Mikhailov, Valery; Kopylov, Alexey; Kazakov, Alexander
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.22103

Abstract

The most important task of ensuring the quality of operation of technological and research equipment is its effective protection from external vibration effects in the area of low frequencies, at which resonance phenomena are manifested. For this purpose, various types of vibration isolation systems are used, which are divided into passive and active. Passive systems effectively suppress vibrations at frequencies above 40-50 Hz, at lower frequencies such systems are ineffective as they cannot compensate for resonance phenomena. In this case, active vibration isolation systems are used. The active dampers and platform based on magnetorheological (MR) elastomers presented in the paper demonstrated higher vibration isolation efficiency in the frequency range of 0.3-3 Hz compared to the piezoelectric system and in the frequency range of 0.3-20 Hz compared to the platform based on electromagnetic power drive. At these frequencies, the vibration displacement amplitude transfer coefficient was less than 0.075.  The use of MR effect makes it possible to regulate the stiffness and deformation of the elastic active element made of MR elastomer by changing the magnitude of magnetic induction. In this case it is possible to control dynamic and precision characteristics of the active damper, as well as to increase the efficiency of vibration isolation.  During dynamic modelling of the active MP damper the differential equations and transfer functions of its elements were determined. Modelling in Simulink MATLAB software environment allowed to determine the transient process of damper movement under the influence of harmonic oscillations, select the type of regulator and calculate its tuning parameters. Experimental studies were carried out on a vibration test bench and confirmed the modelling results at frequencies of 0.3-50 Hz. At frequencies higher than 50 Hz passive vibration isolation begins to prevail due to absorption of vibration energy in the MR elastomer, which is not taken into account in the modelling. The experimental amplitude-frequency response of the platform showed high vibration isolation efficiency at frequencies of 0.3-100 Hz with vibration displacement amplitude transfer coefficient less than 0.075.
Securing Communication in Internet of Vehicles using Collaborative Cryptography and Intelligent Reflecting Surfaces Ahmed, Aljumaili; Trabelsi, Hafedh; Jerbi, Wassim; Hazim, Rafal
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.21813

Abstract

The Internet of Vehicles (IoV) is revolutionizing transportation systems by enabling seamless communication and collaboration among vehicles, roadside units (RSUs), and cloud servers. However, the dynamic and diverse nature of IoV environments raises significant concerns regarding security vulnerabilities and operational efficiency. In response to these challenges, this study proposes an innovative approach that integrates collaborative cryptographic techniques with intelligent reflecting surfaces (IRS). Our approach leverages advanced encryption methods, such as the Advanced Encryption Standard (AES), to ensure secure data transmission, while intelligent reflecting surfaces dynamically adjust their reflective properties to enhance signal propagation and reception. We present a comprehensive network model and algorithmic framework for implementing our proposed strategy, with a specific emphasis on cryptographic protocols and the role of intelligent reflecting surfaces in enhancing both communication security and efficiency. Through theoretical analysis and discussion, we highlight the potential advantages of integrating intelligent reflecting surfaces into secure physical layer (PL), IoV networks, including expanded network coverage, reduced communication overhead, and enhanced energy efficiency. Moreover, we address security threats and vulnerabilities in IoV environments, including potential attacks such as eavesdropping, data tampering, and denial of service. We discuss strategies for mitigating these security risks through the combined use of cryptographic techniques and intelligent reflecting surfaces, thereby bolstering the resilience and robustness of IoV systems.

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