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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 Novel Fuzzy Identification Approach for Nonlinear Industrial Systems: Eliminating Singularity for Enhanced Control Moreano, Gabriel; Sotelo, Julio Tafur; Andino, Valeria; Villacrés, Sergio; Viscaino, Mayra
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.v6i1.24241

Abstract

The control of nonlinear systems poses significant challenges due to their inherent complexities, limiting the effectiveness of traditional control strategies. This paper presents an improved fuzzy identification and control method for nonlinear industrial systems, using Takagi-Sugeno fuzzy inference to model nonlinear dynamics as an interpolation of multiple linear subsystems. A key improvement of this approach lies in the accurate identification of the nonlinear model, which leads to fewer control system failures. The research contribution is the development of a control strategy that enhances system reliability while simplifying implementation. The method involves minimizing a cost function that optimizes the system’s output error, refining the fuzzy identification process for dynamic adaptation to varying operating conditions. The strategy also enables the design of linear controllers for each subsystem and applies Parallel Distributed Compensation (PDC) to regulate the overall nonlinear system. This approach is validated through experimental testing on an aero-pendulum system. The results show that the PDCbased control scheme not only ensures high performance across a wide operational range but also significantly reduces identification errors compared to traditional methods. Given its improved accuracy, reduced complexity, and adaptability, this approach holds significant potential for practical application in industrial environments, where robust and efficient control of nonlinear systems is crucial for operational success.
Development of a Digital Autotuning PI for First Order Plant Using RLS-PZC Nurcahyo, Sidik; Fitri, Fitri; Sungkono, Sungkono
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.v6i1.24257

Abstract

The fact that a real plant can be estimated as a first order and its parameters vary due to the environment has motivated this article to discuss the development of a digital autotuning PI for a first-order plant using Recursive Least Square (RLS) and Pole Zero Cancellation (PZC). Although the focus is only on first order, the methods discussed here hopefully become a basis for developing higher-order plants. Firstly, formulas for calculating PI parameters are derived using PZC and tested by simulation to verify their effectiveness. Then it is organized serially with the RLS and digital PI to form an autotuning PI algorithm. The RLS periodically reads plant input-output to estimate plant parameters. These resulting parameters are fed to PZC and finally, PZC outputs are used by digital PI to control the plant. This design is verified by Matlab simulation, where the controller is realized as an m-function containing a program code for RLS, PZC, and digital PI algorithm. The test was conducted by varying plant parameters, including DC gain and time constant. Verifying controller parameters and their response shows that RLS-PZC can effectively re-tune the digital PI parameters, proved by its response having zero steady-state error and its settling time is maintained. The proposed algorithm can also ensure that the PI controller output is always within the specified maximum limits hence the actual response does not deviate from the designed response.
IoT-Based Classroom Temperature Monitoring and Missing Data Prediction Using Raspberry Pi and ESP32 Navarrete-Sanchez, M. A.; Olivera-Reyna, Re.; Olivera-Reyna, Ro.; Perez-Chimal, R. J.; Munoz-Minjares, J. U.
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.v6i1.24345

Abstract

This study focuses on accurate temperature monitoring to optimize classroom conditions, enhancing student comfort and performance by providing precise data on temperature dynamics and ensuring reliability through advanced algorithms for handling missing data. Currently, advances in the Internet of Things (IoT) have enabled the development of simple, scalable, and intuitive systems for real-time environmental monitoring. This work presents a novel architecture for monitoring temperature dynamics in an electronic laboratory, leveraging a system of interconnected IoT devices with Wireless Fidelity (WiFi) communication. The system employs an ESP32 microcontroller and DS18B20 temperature sensors placed strategically around the classroom, including near windows and doors, to provide comprehensive data on heat distribution. The ESP32 is a small, low-cost, and powerful electronic chip that acts as the central processor for IoT systems, capable of handling data and connecting to a Wireless Network trough WiFi. While the DS18B20 can be defined as a digital sensor that accurately measures temperature and transmits the data electronically to a connected device. Therefore, the ESP32 microcontroller acts as the central processor, receiving temperature data from the DS18B20 sensors, which are configured to detect and transmit measurements. So, this data is then sent over a secure local WiFi network for real-time monitoring and analysis. The proposed system offers several advantages over existing solutions, including cost effective deployment, ease of integration, and real time monitoring. By using a secure local network for communication, it ensures reliable and uninterrupted data transmission. Furthermore, the I-UFIR algorithm was implemented to estimate missing temperature data points, significantly improving the accuracy of temperature readings and providing smoother, more reliable estimations. This system not only demonstrates the feasibility of IoT-based temperature monitoring in educational settings, but also highlights its potential to improve learning environments by optimizing classroom conditions.
Mathematical Modeling of a Unicycle Robot and Use of Advanced Control Methodologies for Multi-Paths Tracking Taking into Account Surface Friction Factors Basal, Mohamed Abdelhakim; Ahmed, Mohammed Fadhil
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.v6i1.24361

Abstract

The research aims to design robust controllers that achieve the stability of a single-wheeled robot under the presence of friction factors and to track different parameters to verify robust stability. This paper presents a new study of the unicycle robot system that is controlled using advanced control methodologies. The paper aims to improve the work of the unicycle robot system, due to its effective impact on improving the performance of driving the robot, which is reflected in the smoothness of the vehicle speed change, ensuring the stability of the robot and the safety of the investor in the uncertain work environment. The main goal is to achieve high dynamic performance for the unicycle robot system. The studied system is non-linear and is subject to the restrictions of the friction factor change with the speed change of the unicycle robot. What increases the difficulty of controlling this type of control system is the uncertainty of some parameters of the control system, such as friction factors. In this paper, two advanced control methodologies were proposed: the optimal controller and the optimal parametric controller. The research results showed that both the optimal and optimal parametric controllers succeeded in achieving stability despite the uncertainty of the parameters and multiple friction factors, but with a relative superiority of the optimal parametric controller. Previous research has discussed many controllers such as classical and advanced controllers such as sliding control and fuzzy control, but it has not previously dealt with the optimal parametric controller that will be discussed in this research.
HyVADSVM: Hybrid VADER-SVM and GridSearchCV Optimization for Enhancing Cyberbullying Detection Ernawati, Siti; Frieyadie, Frieyadie; Yulia, Eka Rini
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.v6i1.24385

Abstract

Cyberbullying detection is becoming increasingly crucial in today’s digital era, as many individuals suffer from online harassment. The main challenge lies in accurately identifying patterns of harassment in social media texts, which often use informal languages, slang, and sarcasm. Existing methods struggle to capture emotional context owing to the vast amount of data and rapid digital interactions. This study aims to improve the detection accuracy by combining advanced sentiment analysis using VADER and parameter tuning with GridSearchCV. Data were collected from Instagram, Twitter, and YouTube, with TF-IDF employed for feature extraction. Multiple machine-learning classifiers (SVM, K-NN, NB, LR, DT, and RF) were tested to determine the best-performing model. VADER was selected for its reliability in processing social media texts rich in informal contexts, effectively capturing emotional nuances, such as sarcasm and varying sentiment intensities. This makes it well suited for complex language patterns typical of cyberbullying scenarios, enhancing data labeling and analysis accuracy. Using 10-fold cross-validation for reliable testing, performance metrics (accuracy, precision, recall, and F1-Score) were evaluated using a confusion matrix. The findings highlight SVM as the most effective model when optimized with GridSearchCV, achieving accuracy (98.83%), precision (98.78%), recall (98.83%), and F1-Score (98.62%) with kernel =linear, C=1, and gamma=scale. This optimized model, HyVADSVM model has significant potential in cyberbullying detection, contributing to academic research and serving as an effective tool to prevent online harassment. Future work could integrate this model into real-time systems, improve user safety, and support digital policymaking.
Optimal Synergetic and Feedback Linearization Controllers Design for Magnetic Levitation Systems: A Comparative Study Al-Ani, Fatin R.; Lutfy, Omar F.; Al-Khazraji, Huthaifa
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.v6i1.24452

Abstract

In this paper, the stabilization and trajectory tracking of the magnetic levitation (Maglev) system using optimal nonlinear controllers are investigated. Firstly, the overall structure and physical principle represented by the nonlinear differential equations of the Maglev system are established. Then, two nonlinear controllers, namely synergetic control (SC) and feedback linearization based state feedback controller (FL-SFC), are proposed to force the ball's position using the voltage control input in the Maglev system to track a desired trajectory. For the SC design, the Lyapunov function is employed to guarantee an exponential convergence of the tracking error to zero. In the FL-SFC approach, an equivalent transformation is used to convert the nonlinear system into a linear form, and then the state feedback controller (SFC) method is utilized to track the ball to the desired position. The swarm bipolar algorithm (SBA) based on the integral time absolute error (ITAE) cost function is employed to determine the gains of the controllers to achieve the desired response. Computer simulations are conducted to evaluate the performance of the proposed methodology. The results indicate that in normal conditions, the SC controller is more effective than the FL-SFC controller in controlling the Maglev system. Both controllers achieve zero maximum overshoot and zero steady-state error, but SC responds faster, with a settling time of 0.35 seconds compared to FL-SFC's 1.2 seconds. This highlights SC's superior dynamic performance in speed and accuracy. Additionally, when the Maglev system experiences external disturbances, SC shows better resilience, recovering in just 0.1 seconds, while FL-SFC takes 0.65 seconds. The SC exhibits better performance than that of the FL-SFC in terms of reducing the ITAE index and improving the transient response, even when external disturbances are applied.
Design and Implementation of a Backstepping Time Varying Sliding Mode Control for the Angular Velocity Control of a Hydraulic Rotary Actuator Abdullah, Aws M.; Al-Samarraie, Shibly A.; Ali, Hasan H.; Al-Qassar, Arif A.
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.v6i1.24472

Abstract

The Backstepping Sliding Mode Control is a control technique used for controlling nonlinear systems. In this paper, the performance of the backstepping sliding mode controller schemes for the angular velocity control for a rotary actuator of an angular velocity control system that utilizes a novel hydraulic flow control method called inlet throttling was investigated. For the angular velocity dynamic, a linear state feedback with suitable high gain is designed as the virtual controller, where steady state error can be made arbitrarily small according to the gain value. A time varying sliding variable is then selected based on the designed virtual controller. The resulting control design is robust, and the maximum error of the angular velocity with respect to the desired value is derived via Lyapunov Function where its value can be controlled via suitable selections of the control parameters. The simulation results have been obtained based on the MATLAB software tools, which are system transient response, the performance and the robustness of the proposed control in forcing the angular velocity to track the reference value in spite of the uncertainty and disturbances in the system parameters were studied. The SMC is a more comprehensive solution for ensuring the best robustness of stability and performance for the model. The simulation results were generated using MATLAB software tools., which are system transient response, the proposed control performance and the robustness in forcing the angular velocity to track the reference value (100-2000 RPM) in spite of the uncertainty (+10%) and disturbances (5-30 N.m) in the system parameters are studied.
Hybrid Fuzzy-Expert System Control for Robotic Manipulator Applications Chotikunnan, Phichitphon; Roongprasert, Kittipan; Chotikunnan, Rawiphon; Pititheeraphab, Yutthana; Puttasakul, Tasawan; Wongkamhang, Anantasak; Thongpance, Nuntachai
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.v6i1.24956

Abstract

This research examines a hybrid fuzzy-expert system for the control of robotic manipulators, integrating the flexibility of fuzzy logic with the analytical decision-making capabilities of expert systems. The hybrid system switches dynamically between triangle membership functions, which facilitate smooth transitions, and trapezoidal membership functions, which efficiently manage sudden step changes. This adaptive technique mitigates the shortcomings of independent fuzzy logic controllers, particularly their inconsistency across varied setpoints. Simulation outcomes demonstrate substantial decreases in overshoot and settling time, as well as enhanced adaptability and flexibility in dynamic settings. A comparison test shows that the hybrid system is better than separate triangular and trapezoidal fuzzy controllers because it chooses the best control strategy based on the setpoint attributes in real time. Although there are occasional compromises in accuracy (IAE and RMSE), the hybrid controller provides balanced performance appropriate for various robotic applications. The results confirm its viability as a dependable option for industrial and medical robots, particularly in applications necessitating precision control and adaptability.
Image Denoising Using Generative Adversarial Network by Recursive Residual Group Naser, Maysaa A. Ulkareem; Al-Asadi, Abbas H. Hassin
Journal of Robotics and Control (JRC) Vol. 6 No. 2 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i2.24302

Abstract

Cardiac magnetic resonance imaging (CMR) is a vital tool for noninvasively assessing heart shape and function, offering exceptional spatial and temporal resolution alongside superior soft tissue contrast. However, CMR images often suffer from noise and artifacts due to cardiac and respiratory motion or patient movement impacting diagnostic accuracy. While real-time noise suppression can mitigate these issues, it comes at a high computational and financial cost. This paper introduces a method that includes a complete way to clean up medical images by using a new Denoising Generative Adversarial Network (D-GAN). The D-GAN architecture incorporates a recursive residual group-based generator and a discriminator inspired by PatchGAN.The recursive residual group-based generator and the Selective Kernel Feature Fusion (SKFF) mechanism are part of a new D-GAN architecture that makes denoising work better. A PatchGAN-based discriminator designed to improve adversarial training dynamics and texture modeling for medical images. These innovations offer improved feature refinement and texture modeling, enhancing the denoising of cardiac MRI images. allows the model to get a doubling context of local and global, informational, and hierarchical developed features located in the generator. Our technique outperforms other methods in terms of PSNR and SSIM. With scores of 0.837, 0.911, and 0.971 for noise levels of 0.3, 0.2, and 0.1, and PSNR scores of 29.48 dB, 32.58 dB, and 37.85 dB, the results show that the D-GAN method is better than other methods.
Enhanced RRT* with APF and Halton Sequence for Robot Path Planning Hameed, Mohammed T.; Raheem, Firas A.; Nasser, Ahmed R.
Journal of Robotics and Control (JRC) Vol. 6 No. 2 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i2.24921

Abstract

This paper presents a new path planning method (APF-IRRT*-HS), which relies on the optimization process of the conventional RRT* algorithm and combined with the APF method where the sampling process of the RRT* algorithm is improved using the Halton sequence, which is known to be deterministic and repeatable and provides more efficient coverage than other low discrepancy sequences with the modified goal-based method which provides a probabilistic approach to decide whether to sample from a point directly at the target or to choose a random point from the Halton sequence based on the current distance. We implemented the proposed method in two cases of mass point and two-link robots. The proposed method compares path length with the conventional RRT* algorithm and APF-RRT*, as well as time efficiency and number of iterations. The technique proves effective in various dynamic environments. Specifically, the APF-IRRT*-HS algorithm achieved an improvement of approximately 21.88% and 7.5% in path length, 79.75% and 49.2% in computation time, and 57.39% and 40% in the number of iterations compared with the RRT* and RRT*-APF algorithms, respectively. We can use this method in everyday applications such as robotic arms, drones, self-driving cars, etc. More advanced methods, such as multi-link robots and real-time constraints, can be used in the future.

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