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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 30 Documents
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Search results for , issue "Vol 5, No 5 (2024)" : 30 Documents clear
Speed Control for Linear Induction Motor Based on Intelligent PI-Fuzzy Logic Ahmed, Ahmed H.; Yahya, Ahmed S.; Ali, Ahmed J.
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22203

Abstract

Nowadays, linear induction motors (LIM) are most used in applications such as transportation, liquid metal pumping, material handling, etc. These applications require large forces and high constant speed under changes in load. The LIM suffers from change in speed as a result of the force loads applied to it instantaneously, which causes high ripple in the force response and not constant speed. This research proposes solutions to these problems by designing an intelligent controller to improve the response variable-speed with different forces. LIM was represented by d-q model using MATLAB/Simulink based-on equivalent circuit equations for LIM and study dynamic performance of this machine. The motor was operated at different speeds and loads; the speed change was observed when the load changed. a PI-controller was designed to control velocity of the machine and keeping its velocity constant at load changes. the values of gains (Kp, Ki) was taken manually by using Ziegler method and this requires a long time as tuning the gain values at every reference speed. An intelligent self-tuning fuzzy-PI controller was prepared to select best values of gains and compared with PI-controller. The simulation outcomes display that fuzzy-PI controller has improved speed and force moving performances machine than PI-controller since we obtained least ripple in the force response. The results obtained in the simulation are interesting, given that the Fuzzy-PI controller designed has nonlinear behavior that achieves wide range of speeds operation for the machine at variable forces compared with traditional PI-controller, and this gave clear improvement in the engine’s performance.
Research Trends and Knowledge Taxonomy of Artificial Intelligence Applications in Supply Chain Management, Logistics, and Transportation: A Systematic Literature Review and Bibliometric Analysis Kriouich, Mohamed; Sarir, Hicham; Louah, Soulaiman
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.21859

Abstract

Due to industrialization and globalization, supply chains (SC) have become more and more in need of artificial intelligence (AI), which has sparked conversations on how to use it to improve SC performance globally. Using both quantitative and qualitative methodologies, this study provides a thorough examination of the trends, gaps, and knowledge structure in the literature on AI in SC. Scientific mapping was used to summarize 140 important publications published between 1998 and 2022. Publication years, attribution, journal co-citations, partnerships between countries and institutions, significant papers, related keywords, and historical study groups were all included in the bibliographic analysis. A thematic categorization of the data produced 22 sub-branches of AI application in SC that are covered in five domains: environment, planning and risk management, SC areas, technology, logistics and transportation, and planning and environment. The study identifies current knowledge gaps and recommends future research directions due to limited international cooperation and inadequate platforms for advancing technology research. these findings aid academics and practitioners by providing a coherent intellectual outlook on AI's involvement in SC.
Synergetic Control Design Based Sparrow Search Optimization for Tracking Control of Driven-Pendulum System Al-Khazraji, Huthaifa; Al-Badri, Kareem; Al-Majeez, Rawaa; Humaidi, Amjad J
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22893

Abstract

This study investigates the performance of designing a Synergetic Control (SC) approach for angular position tracking control of driven-pendulum systems. SC is one of the popular nonlinear control techniques that contributed in a variety of control design applications. This research shows a unique application of the SC for angular position tracking control of driven-pendulum systems. Initially, the equations of motion of the system are developed. Subsequently, the control law of the SC is established. For the stability analysis of the closed loop control system, the Lyapunov Function (L.F) is used. To guarantee optimal performance, a Sparrow Search Optimization (SSO) based approach is presented in order to search for the optimum designing parameters of the controller. For performance comparison, the classical Sliding Mode Control (SMC) is introduced. The simulation's outcomes of the study have been confirmed that the proposed control algorithm is addressed the tracking problem of the angular position of the system successfully. Besides, when an external disturbance is inherited in the simulation, the SC exhibits a robustness performance. Moreover, the performance of the SC is slightly similar as SMC. However, the distinct difference in the performance is that the control signal of the SMC exhibits chattering problem, while this phenomenon is absent in the SC. All computer simulations are carried out using MATLAB software.
Errors Detection Based on SDWT and BNN Applied for Position, Velocity and Acceleration Signals of a Wheeled Mobile Robot Saeed, Saad Zaghlul; Alobaidy, Muhamad Azhar Abdilatef; Yosif, Zead Mohammed
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.21424

Abstract

Accurate error detection in mobile robots is crucial for reliable operation and prevention of mechanical or electrical failures. Mechanical defects on the wheels of mobile robot make real path deviate from the desired path and trajectory. From the kinematics equations, error in the angular velocity of wheel affects the position, velocity, and acceleration. Each of these signals is fed to the Symelet discrete wavelet transform (SDWT) for the purpose of error's feature detection and extraction. The SDWT with 5-level for each component of the signal produce 10 inputs for the Bayesian Neural network (BNN). The BNN with single layer of 18 neurons classifies the inputs either no error case or specify the wheel(s) at which error had been happened. Straight line and circular paths were tested in the presence of errors in single wheel or both wheels. Two different path's time durations are tested to investigate robustness of the proposed methodology. The simulation’s results of two wheels mobile robot showed that acceleration's signal for a straight-line path has accuracy of 100%, MSE 3.05×10-23 and 9.81×10-17, training iterations are 15 and 23 for 4- and 2-seconds durations; respectively. While for a circular path, displacement's signal gave high accuracy 100%, MSE 8.86·10-16 and 3.76×10-18, training iteration 17 and 13 for 4- and 2-seconds durations; respectively. Acceleration signal can be used for detecting errors in real time by using accelerometer. Limitations such as amount of data besides to the sensor noise affects the proposed methodology.
Neuro-Fuzzy Controller for a Non-Linear Power Electronic DC-DC Boost Converters Al-Dabbagh, Zainab Ameer; Shneen, Salam Waley
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22690

Abstract

The current paper aims to explore the possibility of improving the performance of one of the important systems (boost DC-DC converter) in the field of electrical energy by contributing to the use of electronic power converters to provide the scheduled voltage to the loads with changing operating conditions using traditional (PID control) and expert (Neuro-fuzzy logic control) methods. Test cases are proposed to verify the possibility of improvement and the effectiveness of the system through approved measurement criteria such as improving stability, response time, efficiency, or a performance measure for overshoot and undershoot rates and rise time in addition to steady-state error through which comparison can be made to know the best between the methodology used to evaluate the performance of PID controllers and ANFIS (Adaptive Neuro-fuzzy Inference System). The current paper deals with a study of the operation of non-linear DC-DC Converters with a Neuro-Fuzzy Controller. To verify the system's effectiveness, proposed tests are conducted to simulate operation in real-time. The assumptions adopted are that the input voltage value is available from a direct current source with a voltage of (12) volts, and what is required to supply a load with a voltage ranging between (22-120) depending on the load change. The necessary calculations were made to calculate the converter parameters. The required inductance value was (160μH) and the capacitance value was (276μF). The simulation test was conducted using a model consisting of a resistive load and a step-up converter in addition to the supply source in both the open-loop and closed-loop system states. System tests were also conducted in the presence of the proposed controllers to verify the system's effectiveness.
Comparative Analysis of CryptoGAN: Evaluating Quality Metrics and Security in GAN-based Image Encryption Bhat, Ranjith; Nanjundegowda, Raghu
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.23096

Abstract

Balancing security with image quality is a critical challenge in image encryption, particularly for applications like medical imaging that require high visual fidelity. Traditional encryption methods often fail to preserve image integrity and are vulnerable to advanced attacks. This paper introduces CryptoGAN, a novel GAN-based model designed for image encryption. CryptoGAN employs an architecture to effectively encrypt a dataset of 2000 butterfly images with a resolution of 256x256 pixels, integrating Generative Adversarial Networks (GANs) with symmetric key encryption. Using a U-Net Generator and a PatchGAN Discriminator, CryptoGAN optimizes for key metrics including Structural Similarity Index (SSIM), entropy, and correlation measures. CryptoGAN's performance is comprehensively compared against state-of-the-art models such as Cycle GAN-based Image Steganography, EncryptGAN, and DeepEDN. Our evaluation, based on metrics like SSIM, entropy, and PSNR, demonstrates CryptoGAN's superior ability to enhance encryption robustness while maintaining high image quality. Extensive experimental results confirm that CryptoGAN effectively balances security and visual fidelity, making it a promising solution for secure image transmission and storage. This study is supported by a literature survey and detailed analysis of the model architecture, underscoring CryptoGAN's significant contributions to the field of image encryption.
Performance Optimization of a DFIG-based Variable Speed Wind Turbines by IVC-ANFIS Controller Ouhssain, Said; Chojaa, Hamid; Aljarhizi, Yahya; Al Ibrahmi, Elmehdi; Hadoune, Aziz; Maarif, Alfian; Suwarno, Iswanto; Mossa, Mahmoud A.
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22118

Abstract

An improved indirect vector control (IVC) method for a wind energy conversion system (WECS) is presented in this research. Field-oriented control or indirect vector control as it is sometimes called is a very important element of contemporary WECS that employs DFIGs. This control strategy is pivotal for achieving high performance and efficiency of DFIG-based wind turbines because it offers direct control on the torque and power ratings of the generator. A doubly fed induction generator (DFIG) is used by the WECS to inject power to the grid. An adaptive network-based fuzzy inference system (ANFIS), which is proposed to replace traditional methods like linear PI controllers, is the basis for this IVC. In this paper we chose ANFIS controller over traditional linear Proportional-Integral (PI) controllers due to its ability to adapt and learn from the system, leading to improved performance. The rotor voltage is controlled by the proposed IVC in order to regulate the exchanged active and reactive power between the stator and the grid. In order to verify the proposed control in terms of performance and robustness, a comparative analysis between the proposed ANFIS and linear PI controllers for the WECS-DFIG system is performed by a simulation study in a MATLAB/Simulink environment. This analysis covers both the transient and steady states of operation. As a result, the proposed ANFIS controller shows improved efficiency and robustness compared to the linear PI controllers. This superiority stems from its ability to integrate the flexibility and effectiveness inherent in diverse artificial intelligence controllers, specifically the synergistic use of Neural Network (NN) and Fuzzy Logic (FL) algorithms. The ANFIS controller's adaptability to diverse operating conditions and its capability to learn and optimize its performance play pivotal roles in enhancing its control capabilities within the WECS-DFIG system.
Momentum-Based Push Recovery Control of Bipedal Robots Using a New Variable Power Reaching Law for Sliding Mode Control Al-Tameemi, Ibrahim; Doan, Duc; Patanwala, Abizer; Agheli, Mahdi
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.23379

Abstract

A significant challenge in deploying bipedal robots for human-oriented real-world applications is their ability to maintain balance when externally disturbed. Current momentum-based balance control strategies often exhibit inadequate robustness to disturbances due to reliance on simple proportional controllers and imprecise incorporation of desired angular momentum changes. Furthermore, the sequential activation of momentum and posture correction controllers compromises system stability when confronted with consecutive disturbances. This paper proposes and validates a new Variable Power Reaching Law for Sliding Mode Control (SMC) to enhance the regulation of linear momentum against disturbances. The proposed reaching law adjusts dynamically to the system's errors, ensuring fast convergence and minimal chattering. In this paper, we precisely define the desired angular momentum change in relation to the Center of Pressure (CoP), a crucial stability metric, as well as the desired linear momentum and ground reaction forces.  The null-space method, which allows for simultaneous task execution by using unused degrees of freedom, is employed to ensure effective balance and upright posture without interference. The posture correction control is projected onto the null-space of momentum control. Simulation results confirm that the proposed control system effectively stabilizes the robot against external disturbances, regulating momentum and restoring upright posture. The null-space method proves effective in maintaining balance under multiple disturbances by simultaneously controlling momentum and posture. Comparative evaluations show that our approach outperforms traditional momentum-based controls and nonadaptive reaching laws, reducing CoP fluctuations, managing disturbances up to 117 N, and minimizing chattering and steady-state error. These advancements underscore the potential for deploying bipedal robots in dynamic environments.
Optimizing Network Security with Machine Learning and Multi-Factor Authentication for Enhanced Intrusion Detection Mahmood, Rafah Kareem; Mahameed, Ans Ibrahim; Lateef, Noor Q.; Jasim, Hasanain M.; Radhi, Ahmed Dheyaa; Ahmed, Saadaldeen Rashid; Tupe-Waghmare, Priyanka
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22508

Abstract

This study examines the utilization of machine learning methodologies and multi-factor authentication (MFA) to bolster network security, specifically targeting network intrusion detection. We analyze the way in which the integration of these technologies effectively tackles existing security concerns and constraints. The research highlights the importance of incorporating energy conservation and environmental impact reduction into security solutions, in addition to traditional cryptography and biometric methods. In addition, we tackle the limitations of centralized systems, such as vulnerabilities to security breaches and instances of system failures. The study examines different security models, encompassing categories, frameworks, consensus protocols, applications, services, and deployment goals in order to determine their impact on network security. In addition, we offer a detailed comparison of seven machine learning models, showcasing their effectiveness in enhancing network intrusion detection and overall security. The objective of this study is to provide in-depth understanding and actionable suggestions for utilizing machine learning with MFA (Multi-Factor Authentication) to enhance network defensive tactics.
Integrated Deep Hybrid Learning Model Upon Spinach Leaf Classification and Prediction with Pristine Accuracy Elumalai, Meganathan; Fernandez, Terrance Frederick
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22546

Abstract

Over the years, Agriculture has been a mainstay of life for Indians and about half the working population of Tamil Nadu. Spinach is an integral part of everyone’s meal and its nutrient content is higher than other veggies. The nutrients are unique for varied varieties so there is a dire need to classify them and thus to predict them. Furthermore, exactitude prediction leads to easy detection of spinach leaves. In this work, we selected 5 varieties of spinach leaves populated under a huge dataset. We implemented the same employing a Deep Hybrid approach which is a fusion of conventional Machine Learning with state-of-the-art Deep Learning using Orange toolkit. Out of the plethora of these AI Domaine approaches, four classifiers, such as Support Vector Machine (SVM), k- Nearest Neighbour(kNN), Random Forest (RF), and Neural Network (NN) were chosen and implemented. Existing methods using these algorithms have achieved promising results, with individual accuracies of 98.80% (RF), 98.20% (KNN), 99.9% (NN), and 99.60% (SVM). However, the IDHLM aims to surpass these individual performances by integrating them into a cohesive framework. This approach leverages each algorithm's complementary strengths to achieve even higher classification accuracy. The abstract concludes by highlighting the potential of the IDHLM for achieving pristine accuracy in spinach leaf classification.

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