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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 2 (2024)" : 24 Documents clear
Design of PID, IMC and IMC based PID Controller for Hydro Turbine Power System of Non-minimum Phase Dynamics Bhuran, Supriya Y.; Jadhav, Sharad P.
Journal of Robotics and Control (JRC) Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i2.21342

Abstract

The primary objective of this paper is to design and assess the performance of conventional Proportional Integral Derivative (PID), Internal Model Controller (IMC), and IMCbased PID controllers tailored for Hydro Turbine Power Systems (HTPS) exhibiting Non-Minimum Phase (NMP) dynamics. The focus is on overcoming the limitations of existing approaches in handling such complex system dynamics. Existing literature underscores the difficulty of crafting controllers for such systems. The current study represents a sincere endeavour to design and evaluate the performance of conventional Proportional Integral and Derivative (PID), Internal Model Controller (IMC), and IMCbased PID controllers tailored for HTPS characterized by NMP behaviour. The design case study and simulations were conducted using MATLAB and Simulink. The closed-loop responses of HTPS with PID, IMC, and IMC-PID are presented, and the controller performances are scrutinized in both time and frequency domains. To validate the effectiveness of the controllers, performance indices such as Integrated Squared Error (ISE), Integrated Absolute Error (IAE), Integrated Time-weighted Absolute Error (ITAE), Integrated Time Squared Error (ITSE) are calculated, as well as control efforts are calculated using 2-norm and infinity-norms. These performance indices and control effort norms offer a comprehensive evaluation of the controllers’ performance in terms of minimizing error, handling system dynamics, and optimizing control effort across different time scales. Analysing these metrics aids in selecting and refining controllers for optimal performance in HTPS with NMP behaviour. Our findings illustrate that IMCbased PID controllers exhibit superior performance compared to conventional PID controllers in effectively handling the NonMinimum Phase (NMP) dynamics of Hydro Turbine Power Systems (HTPS). This superiority is substantiated by enhanced performance indices, including reductions in ISE, IAE, ITSE, and ITAE.
A Systematic Literature Review of Performance Hospital Supply Chain Management Louah, Soulaiman; Sarir, Hicham; Kriouich, Mohamed
Journal of Robotics and Control (JRC) Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i2.21541

Abstract

Over the last few decades, globalization has driven up the demand for hospital Supply Chain Management (SCM) with the goal of bio-medical development and improving performance. This review aims to offer both a qualitative and quantitative comprehension of the hospital SCM re-search field's overall developmental trend. By using the methodology science mapping approach are visualize the organization of academic knowledge, 87 significant papers, that were published between 2002 and 2023 in total due to their importance in recent years, were located, expanded upon, and summarized. Bibliographic analysis for under-standing the global research state and academic develop-ment was performed on visualized statistics can help identi-fy trends in data about co-occurring keywords, interna-tional cooperation, journal allocation/co-citation, and view clusters of study subjects based on this five categorization, 22 sub-branches in total of hospital SCM identification and topical discussion of knowledge were conducted, namely (i) technologies; (ii) planning; (iii) supply chain field in hospi-tals; (iv) logistics and (v) environmental. Lastly, suggestions for future study directions and current knowledge gaps were made due to constraints of international cooperation and insufficient platforms to quickly advance innovation technology research. The results contribute to a methodical intellectual representation of the current state of hospital SCM research. Furthermore, it offers heuristic ideas to practitioners and researchers to control the quality of de-veloped healthcare and logistics services.
Ophthalmic Diseases Classification Based on YOLOv8 Khalaf, Ahmed Tuama; Abdulateef, Salwa Khalid
Journal of Robotics and Control (JRC) Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i2.21208

Abstract

With the rising prevalence of retinal diseases, identifying eye diseases at an early stage is crucial for effective treatment and prevention of irreversible blindness. But Ophthalmologists face challenges in detecting subtle symptoms that may indicate the presence of a disease before it progresses to an advanced stage Among these challenges, eye diseases can present with a wide range of symptoms, and some conditions may share similar signs. To solve these difficulties, in the research proposed YOLOV8(You Only Look Once) Lightweight Self-Attention model to classify seven different retinal diseases. In this regard, the dataset that have been used in this study contains 5787 images from three different sources (Roboflow, Kaggle and Medical Clinics) were included in the seven classes of Glaucoma, Age-related Macular Degeneration (AMD), Cataract, Diabetic retinopathy (DR), and Retinal Vein Occlusion, which comprises of Branch Retinal Vein Occlusion (BRVO) and Central Retinal Occlusion (CRVO) and normal.  As a results, the model has proven excellent performance in its classification ability. Boasting an average classification accuracy of 94% across the seven disease with precsition 96.2%, recall 96.6%and f1 score was 96.3% At the time of training it was 0.6 Houres(H). When compaired with Resnet50, VGG16 results underscore the model’s superior performance in precision and computational efficiency compared. The algorithm's evaluation reveals its superiority when compared to earlier pertinent research, making it a trustworthy method for classifying retinal illnesses.
Adaptive Vector Field Histogram Plus (VFH+) Algorithm using Fuzzy Logic in Motion Planning for Quadcopter Mohammed, Khitam; Aliedani, Ali; Al-Ibadi, Alaa
Journal of Robotics and Control (JRC) Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i2.21540

Abstract

This work introduces the adaptive version of the vector field histogram plus (VFH+) motion planning algorithm, which is designed for unmanned aerial vehicles, particularly quadcopters, to enhance its performance in navigation tasks. The method suggests incorporating fuzzy control to adaptively modify the VFH+ look-ahead distance parameter by analysis continuous environmental and motion conditions. Simulation tests were completed using different scenarios that varied in obstacle quantity, density, distribution, and size and waypoint quantity. Simulation results showed the successful outcomes of this strategy in enhancing quadcopter motion performance in various contexts. The results indicated notable enhancements in obstacle avoidance, smoother motion trajectories, and decreased travel time compared to the traditional VFH+ method. One of the most important aspects of creating real-time motion planning systems is handling uncertainty. This is accomplished by incorporating a fuzzy system knowledge base for automatic algorithmic modification into the planning process and employing advanced motion-planning techniques. The adaptive algorithm improves the quadcopter's ability to deal with high uncertainty levels by incorporating fuzzy logic for dynamic parameter adjustment, allowing for accurate and efficient navigation in various environments, even in uncertain conditions.
Optimizing Solar Energy Production in Partially Shaded PV Systems with PSO-INC Hybrid Control Abboud, Sarah; Loulijat, Azeddine; Boulal, Abdellah; Semma, El Alami; Habachi, Rachid; Chojaa, Hamid; Ma'arif, Alfian; Suwarno, Iswanto; Mossa, Mahmoud A.
Journal of Robotics and Control (JRC) Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i2.20896

Abstract

Partial shading, from obstacles such as buildings or trees, is a major challenge for photovoltaic systems, causing unpredictable fluctuations in solar energy production and underlining the need for advanced energy management strategies. In this paper, we propose an innovative approach that combines hybrid metaheuristic optimization with maximum power point tracking control (MPPT), using particle swarm optimization (PSO) in conjunction with the incremental conductance (IC) algorithm. We compare the proposed method with the conventional Perturb and Observation (PO) algorithm. The choice of PO as a comparison method is due to its simplicity, its familiarity with the scientific literature, its low cost of implementation. The main objective of swarm optimization combined with the IC algorithm lies in its ability to overcome the challenges posed by partial shading, ensuring accurate and efficient tracking of the point of maximum power, thanks to dynamic adaptation to variations in solar irradiation, thus enhancing the performance and resilience of the photovoltaic system. This approach  is of crucial importance, offering considerable potential for solving the complex challenges associated with partial shading. Our results show that this hybrid MPPT algorithm offers superior tracking efficiency 98% , faster convergence 500ms , better stability and increased robustness compared to traditional MPPT approaches. The system is composed of a PV and a boost converter that connects the input to the resistive load. The algorithms were implemented with MATLAB/Simulink as the simulation tool. These results not only reinforce the viability of sustainable energy solutions, but also open the way for the development of more sustainable energy solutions.The perspectives of this work are oriented towards a practical and extended integration of the proposed hybrid approach in real photovoltaic systems, with a particular emphasis on experimental validation.
Robust Adaptive Trajectory Tracking Sliding Mode Control for Industrial Robot Manipulator using Fuzzy Neural Network Xuan, Quynh Nguyen; Cong, Cuong Nguyen; Ba, Nghien Nguyen
Journal of Robotics and Control (JRC) Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i2.20722

Abstract

This paper presents a control method for a two-link industrial robot manipulator system that uses Fuzzy Neural Networks (FNNs) based on Sliding Mode Control (SMC) to investigate joint position control for periodic motion and predefined trajectory tracking control. The proposed control scheme addresses the challenges of designing a suitable control system that can achieve the required approximation errors while ensuring the stability and robustness of the control system in the face of joint friction forces, parameter variations, and external disturbances. The control scheme uses four layers of FNNs to approximate nonlinear robot dynamics and remove chattering control efforts in the SMC system. The adaptive turning algorithms of network parameters are derived using a projection algorithm and the Lyapunov stability theorem. The proposed control scheme guarantees global stability and robustness of the control system, and position is proven. Simulation and experiment results from a two-link IRM in an electric power substation are presented in comparison to PID and AF control to demonstrate the superior tracking precision and robustness of the proposed intelligent control scheme.
Design and Analysis of IO and FO Controllers to Investigate the Effects of Process Parameter Perturbations on Lag-Dominant Time Delay Systems Patil, Diptee; Jadhav, Sharad
Journal of Robotics and Control (JRC) Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i2.21101

Abstract

This paper focuses on the design, analysis and implementation of Integer-order (IO) and Fractional-order (FO) controllers for systems characterized by lag-dominant time delays. The existing literature has been examined to analyze the methodology employed in tuning IO and FO controllers for first-order time delay system for perturbations in process parameters. It is observed that there is scope to investigate better controllers for lag-dominant time delay systems. The five different structures of controllers are chosen. The paper proposes IO and FO controllers tailored for a test group comprising 16 first-order systems with time delays. These IO and FO controllers are designed to fulfil design specifications: phase margin, peak overshoot, IAE, ITAE and ISE using Modified Bode’s Ideal Loop Transfer Function with delay method. For comparison conventional IO tuning method, Gain-Phase Margin Tester (GPMT) and Fractional Ms Constrained Integral Gain Optimization Method (F-MIGO) is used. The simulation results and performance evaluation for both IO and FO controllers are obtained for a range of values of relative dead time of the system represented by τ. The τ value is obtained by varying conditions of delay (L) and time constant (T). Two scenarios are taken into account: the first involves varying L while keeping T constant, and the second involves keeping L constant while varying T. The main objective of the paper is to analyze IO and FO controllers based on time and frequency domain parameters, performance error indices, disturbance rejection, gain variations, Total Variation (TV) and control efforts for perturbations in process parameters. The simulation results indicate that FO controllers show superior tolerance to perturbations in L and T when compared to IO counterparts. This observation was noted during the analysis of the control system by varying values of L and T to obtain a consistent value of τ . Thus, the extensive simulation studies demonstrate that the FO controller tailored for lag-dominant time delay systems outperforms its IO counterpart in terms of robustness, closed-loop stability and error performance metrics.
Road Object Detection using SSD-MobileNet Algorithm: Case Study for Real-Time ADAS Applications Bouazizi, Omar; Azroumahli, Chaimae; El Mourabit, Aimad; Oussouaddi, Mustapha
Journal of Robotics and Control (JRC) Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i2.21145

Abstract

Object detection has played a crucial role in Advanced Driver Assistance Systems (ADAS) applications, particularly with integrating deep learning techniques. These advancements have improved ADAS applications by enabling more precise object identification, thereby enhancing real-time decision-making. Object detection models can be categorized into two main groups: two-stage and one-stage models. While prior studies reveal that one-stage detectors generally achieve higher frames per second (FPS) at the expense of some accuracy, they remain better suited for real-time ADAS applications. Our study aims to analyze the performance of an object detection model created using SSD-MobileNet, a one-stage detector approach. We focused on identifying road-related objects such as vehicles, and traffic signs. The contribution of our work lies in developing an object detection model using a pre-trained SSD-MobileNet and employing transfer learning. This process involves introducing a new fully connected layer tailored for the specific identification of objects in road scenes. The retraining of the SSD-MobileNet model is executed through GPU-accelerated transfer learning on the MS COCO dataset, incorporating appropriate pre-processing to exclusively include road-related objects. Our findings indicate promising results for the retrained SSD-MobileNet model, achieving an F1 score of 0.801, and a Mean Average Precision (mAP) of 65.41 at 71 FPS. A comparative analysis with other one-stage and two-stage detectors demonstrates the model's performance, surpassing some existing works in the literature related to road object detection. Notably, our model exhibits improved mAP while maintaining a higher FPS, rendering it more apt for ADAS applications.
AI-based Bubbles Detection in the Conformal Coating for Enhanced Quality Control in Electronics Manufacturing Zouhri, Nizar; Mourabit, Aimad El; Abidine, Alaoui Ismaili Zine El
Journal of Robotics and Control (JRC) Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i2.20441

Abstract

This research pioneers the application of artificial intelligence (AI) methodologies—machine learning, deep learning, hybrid models, transfer learning, and edge AI deployment—in enhancing bubble detection within conformal coatings, a critical as- pect of electronics manufacturing quality control. By addressing the limitations of traditional detection methods, our work offers a novel approach that significantly improves automation, accuracy, and speed, thereby ensuring the reliability of electronic assemblies and contributing to economic and safety benefits. We navigate through the challenges of creating diverse datasets, system robustness, and the imperative for industry-wide standardization, proposing strategies for overcoming these obstacles. Our findings highlight the transformative impact of AI on quality control processes, demonstrating substantial advancements in detection capabilities. Furthermore, we advocate for future research, development, and collaboration to extend these AI-driven improvements across the manufacturing spectrum. This study underscores the potential of AI to revolutionize electronics manufacturing, emphasizing the need for continued innovation and standardization to realize safer, more efficient, and cost-effective production methodologies.
Application of Software Robots Using Artificial Intelligence Technologies in the Educational Process of the University Yeslyamov, Serik
Journal of Robotics and Control (JRC) Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i2.21083

Abstract

The use of artificial intelligence (AI) in education has gained interest due to its increasing application in various fields. This study explores the potential of AI-based software robots in higher education and their ability to revolutionize educational methodologies. The research purpose is to examine the positive impact of the use of software robots in educational settings. The study focuses on evaluating the prospects of expanding the use of AI-based software robots in higher education. The research uses a combination of observational techniques and practical case studies. It includes an experimental investigation of the basic principles of developing an AI-based robot teacher, with the aim of eventually implementing it in educational processes. The research findings indicate that integrating AI-driven software robots into university education can provide substantial benefits and significant improvements over traditional teaching models. These robots can enhance the educational process and address various developmental challenges. The study highlights the transformative impact of AI-based software robots in modernizing university education. The findings demonstrate the potential of these technologies to reshape the current higher education system.

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