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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
Predicting SI Engine Performance Using Deep Learning with CNNs on Time-Series Data Hofny, Mohamed S.; Ghazaly, Nouby M.; Shmroukh, Ahmed N.; Abouelsoud, Mostafa
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.v5i5.22558

Abstract

In this study, deep learning (DL) model is used to predict brake power (BP) of GX35-OHC 4-stroke, air-cooled, single-cylinder gasoline engine. The engine uses E15 (85% gasoline + 15% ethanol) as a fuel due to its high performance and low emissions. A convolutional neural networks (CNN) model is used on time-series data due to their ability to capture temporal patterns and relationships in sequential data, such as engine BP. While studying the performance of the network, it is found that the root mean squared error (RMSE) is 0.0007, explained variance score (EVS) is 0.9999, and mean absolute percentage error (MAPE) is 0.22%. Compared to traditional machine leaning methods, these metrics demonstrate the high accuracy and reliability of the model, confirming its effectiveness in predicting BP. Various performance curves are plotted such as comparing target and predicted values, regression plots (to indicate the generalization capability),  learning curve (to demonstrate the model's effective training progress and convergence), Bland-Altman plot (to show the convergence between the actual and predicted values), histogram and density plot (to show a close fit between predicted and actual values), density plot of actual and predicted outputs, and residual plot (to show randomly distributed errors). This high accuracy and reliability of this DL model help in effective real-time engine performance monitoring, and reducing emission levels, especially for the adoption and use of renewable fuels like E15.
Three-Dimensional Coordination Control of Multi-UAV for Partially Observable Multi-Target Tracking Maynad, Vincentius Charles; Nugraha, Yurid Eka; Alkaff, Abdullah
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.v5i5.22560

Abstract

This research deals with multi-UAV systems to track partially observable multi-targets in noisy three-dimensional environments, which are commonly encountered in defense and surveillance systems. It is a far extension from previous research which focused mainly on two-dimensional, fully observable, and/or perfect measurement settings. The targets are modeled as linear time-invariant systems with Gaussian noise and the pursuers UAV are represented in a standard six-degree-of-freedom model. Necessary equations to describe the relationship between observations regarding the target and the pursuers states are derived and represented as the Gauss-Markov model. Partially observable targets require the pursuers to maintain belief values for target positions. In the presence of a noisy environment, an extended Kalman filter is used to estimate and update those beliefs. A Decentralized Multi-Agent Reinforcement Learning (MARL) algorithm known as soft Double Q-Learning is proposed to learn the coordination control among the pursuers. The algorithm is enriched with an entropy regulation to train a certain stochastic policy and enable interactions among pursuers to foster cooperative behavior. The enrichment encourages the algorithm to explore wider and unknown search areas which is important for multi-target tracking systems. The algorithm was trained before it was deployed to complete several scenarios. The experiments using various sensor capabilities showed that the proposed algorithm had higher success rates compared to the baseline algorithm. A description of the many distinctions between two-dimensional and three-dimensional settings is also provided.
Techno-Economic and Environmental Analysis of an On-Grid and Off-Grid Renewable Energy Hybrid System in an Energy-Rich Rural Area: A Case in Indonesia Umam, Faikul; Wahyu, Fiki Milatul; Efendi, Mochamad Yusuf; Amir, Nizar; Gozan, Misri; Asmara, Yuli Panca
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.v5i5.22633

Abstract

Developing a dedicated renewable energy hybrid system is a viable option for extending access to electrical energy in energy-rich rural areas. This study conducted a feasibility analysis of using a hybrid energy system, combining solar photovoltaic, wind, and biogas, to generate electricity and meet the energy needs of the rural area. West Waru Village is selected as the case study area for this research because it has abundant renewable energy sources. The Hybrid Optimization of Multiple Energy Resources (HOMER) tools is employed for modeling and optimizing the hybrid energy system, offering a comprehensive analysis encompassing technical, economic, and environmental aspects. Furthermore, the study's findings were further analyzed through a sensitivity analysis, considering unpredictable factors such as village load consumption, solar radiation, wind speed, and biomass availability. Additionally, the study’s results reveals that the renewable energy hybrid system can meet nearly 80% of the rural area's electrical energy requirements at a cost of $0.16 per kWh, resulting in the reduction of 8.4 million kg of carbon dioxide emissions. These findings can serve as a baseline for stakeholders in developing renewable energy systems in rural areas.
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.
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.
Soft Tissue Compliance Detection in Minimally Invasive Surgery: Dynamic Measurement with Piezoelectric Sensor Based on Vibration Absorber Concept Hashem, Radwa; El-Hussieny, Haitham; Umezu, Shinjiro; El-Bab, Ahmed M. R. Fath
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.v5i5.22895

Abstract

Recent research in the medical field has increasingly focused on tissue repair, tumor detection, and associated therapeutic techniques. A significant challenge in Minimally Invasive Surgery (MIS) is the loss of direct tactile sensation by surgeons, as they cannot physically feel the organs they operate on. Tactile feedback enhances patient safety by tissue differentiation and reducing inadvertent damage risks. Addressing this challenge, this study introduces a novel tactile sensor designed for compliance detection to enhance tactile feedback in MIS. The sensor operates on a 2-Degree-of-Freedom (2-DOF) vibration absorber system, utilizing a piezoelectric actuator with a calibrated stiffness of 188 N/m. It interprets tissue stiffness regarding a spring constant, Ko, and measures changes in soft tissue stiffness by analyzing variations in the vibration absorber frequency, specifically at the frequency which causes the first mass to exhibit zero amplitude. The effectiveness of this sensor was evaluated through tests on polydimethylsiloxane (PDMS) specimens, which were engineered to replicate varying stiffness found in human organ tissues. Young's modulus of these specimens was determined using a universal testing machine, showing a range from 10.12 to 226.89 kPa. Additionally, the sensor was applied to measure the stiffness of various chicken tissues – liver, heart, breast, and gizzard with respective Young's moduli being 1.97, 9.47, 19.55, and 96.36 kPa. This sensor successfully differentiated between tissue types non-invasively, without requiring substantial deformation or penetration of the tissues. Given its piezoelectric nature, the sensor also holds significant potential for miniaturization through Micro-Electro-Mechanical Systems technology (MEMS), broadening its applicability in surgical environments.
Dynamic Motion Control of Two-Link Robots with Adaptive Synergetic Algorithms Abbas, Aya Khudhair; Kadhim, Saleem Khalefa
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.v5i5.22985

Abstract

Robotics is advancing to assist with daily tasks by developing human-like robotic limbs, which involves challenges in integrating software, control systems, electronics, and mechanical designs. To address these challenges, Classic Synergetic Controller (CSC) and Adaptive Synergetic Controller (ASC) algorithms were created using mathematical equations to regulate the robot arm's joint angle position and achieve precise tracking. A comparison with Adaptive Sliding Mode Control (ASMC) and Classical Sliding Mode Control (CSMC) demonstrated that CSC and ASC outperform in efficiency and robustness. ASC improved by 63%, providing smoother angular position tracking and faster response times. CSC reached the desired position angle in 1.5 seconds with oscillations, while ASC achieved it in 2.4 seconds without oscillations and eliminated chattering. CSC's Root Mean Square (RMS) was 1.57 rad, whereas ASC had no RMS value. The improvement rate of ASC over CSC was 100%, ensuring seamless motion, better rise time, and eliminating oscillations, thus providing robust control against disturbances and parameter variations.
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.
Towards Resilient Machine Learning Models: Addressing Adversarial Attacks in Wireless Sensor Network Shihab, Mustafa Abdmajeed; Marhoon, Haydar Abdulameer; Ahmed, Saadaldeen Rashid; Radhi, Ahmed Dheyaa; Sekhar, Ravi
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.v5i5.23214

Abstract

Adversarial attacks represent a substantial threat to the security and reliability of machine learning models employed in wireless sensor networks (WSNs). This study tries to solve this difficulty by evaluating the efficiency of different defensive mechanisms in minimizing the effects of evasion assaults, which try to mislead ML models into misclassification. We employ the Edge-IIoTset dataset, a comprehensive cybersecurity dataset particularly built for IoT and IIoT applications, to train and assess our models. Our study reveals that employing adversarial training, robust optimization, and feature transformations dramatically enhances the resistance of machine learning models against evasion attempts. Specifically, our defensive model obtains a significant accuracy boost of 12% compared to baseline models. Furthermore, we study the possibilities of combining alternative generative adversarial networks (GANs), random forest ensembles, and hybrid techniques to further boost model resilience against a broader spectrum of adversarial assaults. This study underlines the need for proactive methods in preserving machine learning systems in real-world WSN contexts and stresses the need for continued research and development in this quickly expanding area.
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.

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