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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 708 Documents
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A Low-Cost High Performance Electric Vehicle Design Based on Variable Structure Fuzzy PID Control Shamseldin, Mohamed A.; Araby, Medhat; El-khatib, S.
Journal of Robotics and Control (JRC) Vol. 5 No. 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.22071

Abstract

This paper introduces the design steps and implementation of Electric Vehicle (EV) based on variable structure fuzzy PID control. The role of fuzzy logic is making change in the membership function to tune the fuzzy action according to the error and change of error. The control implementation was executed using a low-cost Arduino mega 2560 and had been programed by MATLAB SIMULINK.  Also, a nonlinear model for the EV was built and validated by the actual performance of the EV experimental setup. The overall EV closed loop implemented on the MATLAB SIMULINK to select the proper control parameters. The proposed variable structure fuzzy PID control had been compared to the traditional PID control to ensure robustness and reliability. The results show that the proposed control technique can deal with the EV disturbances and continuous change in the operating points.
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.
Reinforcement Learning-Based Trajectory Control for Mecanum Robot with Mass Eccentricity Considerations Nguyen, Minh Dong; Ngo, Manh Tien; Quang, Hiep Do; Phuong, Nam Dao
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.v5i5.22148

Abstract

This article presents a robust optimal tracking control approach for a Four Mecanum Wheeled Robot (FMWR) using an online actor-critic reinforcement learning (RL) algorithm to address the challenge of precise trajectory tracking problem in the presence of mass eccentricity and friction uncertainty. In order to handle these obstacles, a detailed dynamics model is derived using Lagrange’s equation, and the Hamilton–Jacobi–Bellman (HJB) equation is solved by iteration algorithm with policy evaluation and improvement. The training laws of optimal control law and value function are proposed after minimizing the modified Hamiltonian function. Moreover, to handle the time-varying property of tracking error model, a transform is given with the addition of time derivative term. Simulation Studies demonstrate the approach’s effectiveness, significantly improving trajectory tracking accuracy and robustness against disturbances. This research contributes to mobile robotics by enhancing control precision and reliability in dynamic environments.
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.
A Model of Proactive-Reactive Job Shop Scheduling to Tackle Uncertain Events with Greedy Randomized Adaptive Search Procedure Nisar, Muhammad Usman; Ma'ruf, Anas; Cakravastia, Andi; Halim, Abdul Hakim
Journal of Robotics and Control (JRC) Vol. 5 No. 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.22208

Abstract

Despite substantial research on job shop scheduling (JSS), there is a gap owing to the lack of a unified framework that considers exact, heuristic, and metaheuristic methods for JSS. This study addressed this gap by presenting a comprehensive approach. The study offered following contributions in this regard: analyzed the exact optimization method for benchmarking, investigated a greedy algorithm (G_r A) for faster solutions, and implemented a novel Greedy Randomized Adaptive Search Procedure (GRASP) to achieve high-quality solutions with computational effectiveness. Additionally, this study considered serious dynamic events (SDE) such as new job arrivals (NJA), rush order (RO), machine failures (MF), and scheduled machine maintenance (SMM), as scheduling disruptions and proposed a proactive-reactive rescheduling strategy, with right-shift (RF) and regeneration (Reg) methods using a hybrid (periodic and event-driven) policy to tackle them. Results showed that the exact methods are optimal but computationally intensive, G_r A are faster but suboptimal, and GRASP strike a balance, delivering high-quality solutions with only a 3.43% gap from exact methods while maintaining computational efficiency. Additionally, RF method effectively handled MF, while Reg efficiently integrated NJA, RO, and SMM. Overall, this study offered a comprehensive approach to JSS, enhancing applicability in manufacturing environments.
Advanced Ensemble Deep Learning Framework for Enhanced River Water Level Detection: Integrating Transfer Learning Tawfeeq, Nisreen; Harbi, Jameelah
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.v5i5.22291

Abstract

The precise monitoring and prediction of river water levels are crucial for effective environmental management, flood prevention, and ensuring water security. This paper introduces an advanced deep learning framework that utilizes an ensemble of state-of-the-art neural networks, namely InceptionV3, VGG16, Xception, MobileNet, and ResNet152, to enhance the accuracy of water level detection from river imagery. The proposed system integrates these models through a robust ensemble methodology that leverages hard voting to improve predictive performance and reliability. Through rigorous preprocessing, including normalization, resizing, and augmentation, alongside strategic transfer learning, the framework achieves an impressive accuracy of 99.5833%, precision of 99.5929%, recall of 99.5762%, and an F1 score of 99.5838%. The ensemble approach not only addresses the variability in image data but also ensures robustness against overfitting and data imbalances. Furthermore, the application of Gradient-weighted Class Activation Mapping (Grad-CAM) enhances the interpretability of the model's decisions, facilitating trust and transparency in its predictions. This study not only demonstrates the potential of ensemble deep learning in hydrological applications but also sets the stage for future enhancements such as real-time processing and integration into comprehensive flood management systems. Future research will explore scalability, the incorporation of additional predictive variables, and the expansion of the model to include real-time monitoring capabilities, aiming to provide a more dynamic tool for disaster readiness and environmental conservation.
Enhancing IoT Security: A Deep Learning and Active Learning Approach to Intrusion Detection Mahdi, Hawraa Fadel; Khadhim, Ban Jawad
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.v%vi%i.22292

Abstract

In response to the escalating demand for robust security solutions in increasingly complex Internet of Things (IoT) networks, this study introduces an advanced Intrusion Detection System (IDS) leveraging both deep learning and active learning techniques. This research addresses the unique challenges posed by IoT environments, such as limited resources and diverse network components, which traditional security measures fail to adequately protect. Employing a BiLSTM model integrated with an active learning strategy, our approach achieved impressive results, including precision, recall, and F1-scores close to 1, and a total accuracy of 0.99. The inclusion of active learning enables the IDS to focus on the most informative data subsets, enhancing processing efficiency and reducing computational demands essential for IoT contexts. This method demonstrates significant promise for detecting sophisticated cyber threats and providing an effective tool for real-world applications. The performance of the proposed model has been rigorously validated on well-established cybersecurity datasets and through simulations in an IoT network environment, confirming its scalability and efficiency. Future work will address potential limitations such as computational demands and adaptability to diverse IoT device architectures, ensuring broader applicability and robustness of the IDS in varied IoT scenarios.
The Role of Occasional Assessment of Sensor Performance for Improved Subsea Search Efficiency Yetkin, Harun
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.v5i5.22298

Abstract

This study addresses the subsea search performance of an autonomous underwater vehicle equipped with a search sensor and an environment characterization sensor. The performance of the search sensor is assumed to be dependent on characteristics of the local environment, and thus sensor performance in some locations can be different than in other locations. For the case that the agent is able to occasionally characterize the environment, and therefore estimate the performance of its search sensor, we describe a method for selecting when and where to characterize the environment and when and where to search in order to maximize overall search effectiveness. Our work accounts for false positives, false negatives and uncertainty in the performance of the search sensor that varies geographically. We show that effort applied to characterizing the environment, and therefore the performance of the search sensor, can improve search performance. We derive a utility function that is used to compute the best path and when to switch between the tasks of search and environmental characterization. The objective of the subsea search mission is to maximize the probability of attaining a desired level of risk reduction, and we terminate the search mission as soon as it is found that the desired risk reduction cannot be attained. To the best of our knowledge, this is the first study that addresses the problem of attaining a desired level of risk and stopping the mission when the desired risk is found to be unachievable. Through numerical illustrations, we show realistic scenarios where the findings of this study can be useful to improve search effectiveness and attain the desired level of risk where the standard exhaustive search techniques will fail to achieve.
Voltage Tracking of Bidirectional DC-DC Converter Using Online Neural Network for Green Energy Application Diana, Nor Farisha; Utomo, Wahyu Mulyo; Abu Bakar, Afarulrazi Bin; Salimin, Suriana; Priyandoko, Gigih; Widjonarko, Widjonarko
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i1.22326

Abstract

In the current era, green energy systems like solar PV, wind energy, and battery storage critically rely on DC-DC converters to manage power flow and voltage conversion efficiently, ensuring optimal performance and reliability. Nevertheless, converters face multiple challenges, including efficiency losses, thermal management concerns, and electromagnetic interference, which can impact these green energy systems' overall performance and reliability. To overcome these challenges, it is necessary to utilize advanced control mechanisms, enhance heat management approaches, and optimize component design. Implementing these improvements will improve the effectiveness and durability of DC-DC converters in renewable energy applications. This research aims to analyze the performance characteristics of a three-phase interleaved half-bridge bidirectional (TPHB-Bi) converter. The research objective involves investigating the effectiveness of the proposed controller by rigorously assessing voltage tracking. This is done through comprehensive assessments of start-up procedures and reference voltage variations using MATLAB/Simulink. The study utilizes a neural network controller with an online learning algorithm based on backpropagation to enhance the converter's operational capabilities, ensuring a stable output voltage and improved transient response. The simulation results highlight the significant advantages of the proposed controller over a conventional PID controller. It exhibits a remarkable reduction in overshoot by 5.29%, considerably shorter rise times ranging from 0.01ms to 0.1ms, and faster settling times of 0.025s. The findings have great importance in promoting sustainable energy development and environmental protection. They demonstrate that implementing advanced control strategies for DC-DC converters can result in more efficient and reliable green energy systems.
Cancer Treatment Precision Strategies Through Optimal Control Theory Abougarair, Ahmed J.; Oun, Abdulhamid A.; Sawan, Salah I.; Abougard, T.; Maghfiroh, H.
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.v5i5.22378

Abstract

Lung cancer is a highly heterogeneous disease, with diverse genetic, molecular, and cellular drivers that can vary significantly between individual patients and even within a single tumor. Though combination therapy is becoming more common in the treatment of cancer, it can be challenging to predict how various treatment modalities will interact and what negative effects they may have on a patient's health, such as increased gastrointestinal toxicities, or neurological problems.   This paper aims to regulate immunity to tumor therapy by utilizing optimal control theory (OCT). This research suggests a malignant tumor model that can be regulated with a combination of immunological, vaccine, and chemotherapeutic therapy. The optimal control variables are employed to support the best possible treatment plan with the fewest potential side effects by reducing the production of new tumor cells and keeping the number of normal cells above the average carrying capacity. Also, the study addresses patient heterogeneity, individual variations in tumor biology, and immune responses for both young and old cancer patients. Finding the right doses for a treatment that works is the main goal. To do this, we conducted a comparative analysis of two optimum control approaches: the Single Network Adaptive Critic (SNAC) approach, which directly applies the notion of reinforcement learning to the essential conditions for optimality and the Linear Quadratic Regulator (LQR) methodology. Although the study's results show the promise of precision treatment plans, a number of significant obstacles must be overcome before these tactics can be successfully applied in clinical settings. It will be necessary to make considerable adjustments to the healthcare system's infrastructure in order to successfully offer personalized treatment regimens. This includes enhanced interdisciplinary care coordination methods, safe data management systems.

Page 57 of 71 | Total Record : 708

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