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Alfian Ma'arif
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International Journal of Robotics and Control Systems
Published by Association for Scientific Computing Electronics and Engineering
ISSN : -     EISSN : 27752658     DOI : https://doi.org/10.31763/ijrcs
Core Subject : Engineering,
Control & Systems Engineering Electrical & Electronics Engineering
International Journal of Robotics and Control Systems is open access and peer-reviewed international journal that invited academicians (students and lecturers), researchers, scientists, and engineers to exchange and disseminate their work, development, and contribution in the area of robotics and control technology systems experts. Its scope includes Industrial Robots, Humanoid Robot, Flying Robot, Mobile Robot, Proportional-Integral-Derivative (PID) Controller, Feedback Control, Linear Control (Compensator, State Feedback, Servo State Feedback, Observer, etc.), Nonlinear Control (Feedback Linearization, Sliding Mode Controller, Backstepping, etc.), Robust Control, Adaptive Control (Model Reference Adaptive Control, etc.), Geometry Control, Intelligent Control (Fuzzy Logic Controller (FLC), Neural Network Control), Power Electronic Control, Artificial Intelligence, Embedded Systems, Internet of Things (IoT) in Control and Robot, Network Control System, Controller Optimization (Linear Quadratic Regulator (LQR), Coefficient Diagram Method, Metaheuristic Algorithm, etc.), Modelling and Identification System.
Arjuna Subject : Teknik (semua kategori) - Teknik Kontrol dan Sistem
Articles 21 Documents
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Search results for , issue "Vol 4, No 1 (2024)" : 21 Documents clear
Exploring the Role of Deep Learning in Forecasting for Sustainable Development Goals: A Systematic Literature Review Utama, Agung Bella Putra; Wibawa, Aji Prasetya; Handayani, Anik Nur; Chuttur, Mohammad Yasser
International Journal of Robotics and Control Systems Vol 4, No 1 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i1.1328

Abstract

This paper aims to explore the relationship between deep learning and forecasting within the context of the Sustainable Development Goals (SDGs). The primary objective is to systematically review 38 articles published between 2019 and 2023, following PRISMA guidelines, to understand the current landscape of deep learning forecasting for SDGs. Using data from 2019-2023 allows capturing the latest developments in deep learning forecasting for Sustainable Development Goals (SDGs), while excluding data before 2019 and after 2023 is based on the desire to avoid including potentially less relevant or unpublished research and to maintain focus on the most current and contextually relevant literature. The methodological approach involves analyzing the application of deep learning methods for forecasting within various SDG fields and identifying trends, challenges, and opportunities. The literature review results reveal the popularity of LSTM models, challenges related to data availability, and the interconnected nature of SDGs. Additionally, the study demonstrates that deep learning models enhance forecast accuracy and computational performance, as measured by Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and R-squared (R2). The findings underscore the importance of advanced data preparation techniques and the integration of deep learning with SDGs to improve forecasting outcomes. The novelty of this research lies in its comprehensive overview of the current landscape and its valuable insights for researchers, policymakers, and stakeholders interested in advancing sustainable development goals through deep learning forecasting. Finally, the paper suggests future research directions, including exploring the potential of hybrid forecasting models and investigating the impact of emerging technologies on SDG forecasting methodologies. Innovative methods for imputing missing values in deep learning forecasting models could be further explored to enhance predictive accuracy and robustness.
Radial Basis Function Network Based Self-Adaptive PID Controller for Quadcopter: Through Diverse Conditions Sahrir, Nur Hayati; Basri, Mohd Ariffanan Mohd
International Journal of Robotics and Control Systems Vol 4, No 1 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i1.1261

Abstract

A quadcopter is an underactuated and nonlinear system which requires a robust controller to aid in maneuvering the quadcopter during flight. A Proportional-Integral-Derivative (PID) controller is easy and suitable to implement, and its efficiency is proved in quadcopter control. However, a PID controller with fixed parameters is inadequate enough to control a quadcopter system with different inputs or perturbations. This paper proposes the development of a self-adaptive PID controller assisted by Radial Basis Function (RBF) Network, to improve the function of the PID controller and help a quadcopter to better adapt towards different inputs and situations, independently.  This work contributes to introducing RBF-PID controller to adaptively fly the underactuated quadcopter through different trajectory and perturbations using simulation. By using the hidden Gaussian function to train the current input, estimate the suitable output and update the Jacobian Information during system control, the PID gains can change adaptively during flight, additionally with the help of Gradient Descent Method (GDM). The proposed method is compared to the traditional PID controller tuned using the PID Tuner App in Simulink. Different inputs are given to test the altitude, attitudes, and position tracking such as step, multistep, sine wave, circular and lemniscate trajectory. The simulated results proved the robustness of RBF-PID in enhancing the disturbance rejection capacity by 13% to 25% in the presence of perturbations (sine wave and wind gust) compared to PID controller. The proposed controller can ensure quadcopter’s flight stability through perturbations that is within the quadcopter’s limitations.
Enhancing the Performance of Power System under Abnormal Conditions Using Three Different FACTS Devices Ibram Y. Fawzy; Mahmoud A. Mossa; Ahmed M. Elsawy; Ahmed A. Zaki Diab
International Journal of Robotics and Control Systems Vol 4, No 1 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i1.1229

Abstract

In this paper, a comparison between Flexible Alternating Current Transmission System (FACTS) devices including Static Synchronous Compensator (STATCOM), Static Synchronous Series Compensator (SSSC) and Unified Power Flow Controller (UPFC) for providing a better adaptation to changing operating conditions and improving the usage of current systems. The power system using FACTS devices is presented under different conditions such as single phase fault and three phase fault. A digital simulation using Matlab/Simulink software package is carried out to demonstrate the better performance including the voltage and the current of the presented system using FACTS that located between buses B1 and B2 under different faults types. The results obtained investigate that the presented system gives better response with FACTS as compared to not using them under abnormal conditions besides, the UPFC gives better performance of power system under several faults as compared to STATCOM or SSSC as It can absorb reactive power in a manner which significantly reduced the fault current. It is demonstrated that UPFC can reduce the peak fault current at bus B1 ‎to 63.85% of its value without ‎using FACTS devices under line to ground fault and 79.18% under three line to ‎ground fault whereas STATCOM and SSSC reduce it ‎to (75.21, 94.35%) and (75.40, 94.68%), respectively.
Analysis of Drone Wireless Communication System Performance Affected by Vibration based on 1DCNN Abbas, Ahmed Hussein; Hameed, Hassanain Ghani; Abdulsadda, Ahmad Taha
International Journal of Robotics and Control Systems Vol 4, No 1 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i1.1315

Abstract

Developments in drone technology have made them crucial in various fields. Vibrations caused by external conditions or mechanical failures in a drone's design can significantly affect the efficiency of the drone's communication systems. The drone's antenna generates phase noise, which can degrade the performance of drone communications systems. This work presents an analysis and computational model of how drone vibration affects system performance. by using two steps. The first one uses the simulation Monte-Carlo in MATLAB when the iteration algorithm processes with various variable values as the frequency carriers and the order of the quadrature-amplitude-modulation (M-QAM) system and evaluates the performance of the communication system by measuring the symbol error rate. The second step uses the one-dimensional convolutional neural network to predict the symbol error rate. After creating the dataset in the first stage, reprocess it and split it into 70% training and 30% testing. Then, by MATLAB App Designer created a graphical user interface (GUI) for friendly use. The result appears to be that the performance of the drone communication system decreased when frequency carriers and modulation order for M-QAM increased due to the impact of a vibrating antenna. Our contribution to this work is using 1DCNN, unlike other works that only use simulation to evaluate the performance, because 1DCNN can automatically extract useful features from the input dataset to evaluate the effect. This study provides a valuable method to evaluate the efficiency of a communication system on the UAV, which is particularly important for drone wireless system planning. In our next work, we propose investigating other factors affecting UAV communication systems, including humidity and temperature.
Research on Indoor 3D Reconstruction Technology Based on Semantic Visual Simultaneous Localization and Mapping Liang, Yu; Lijia, Cao; Changyou, Fu
International Journal of Robotics and Control Systems Vol 4, No 1 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i1.1266

Abstract

In response to the challenge that traditional visual simultaneous localization and mapping (SLAM) systems, based on the assumption of a static environment, struggle to achieve real-time indoor 3D reconstruction in complex dynamic scenes, this paper proposes a real-time indoor 3D reconstruction algorithm based on semantic visual SLAM. By leveraging object detection to obtain 2D semantic information and providing prior information for geometric methods, the fusion of the two effectively suppresses dynamic features, reduces reliance on deep learning methods, and ensures the algorithm's real-time performance. Experimental results on dynamic scenes in the TUM RGB-D dataset show that our algorithm maintains nearly unchanged real-time performance while achieving an average performance improvement of approximately 97.56% and 97.31% on the TUM dataset and Bonn dataset, respectively, compared to the ORB-SLAM2 system. Moreover, our algorithm can reconstruct more intuitive indoor global Octo-map and semantic metric maps compared to sparse point cloud maps, effectively enhancing the scene perception capability of mobile robots and laying the foundation for performing advanced tasks. Furthermore, our algorithm demonstrates a 3.5-10.5 times improvement in real-time performance compared to other mainstream semantic SLAM systems. Experimental results on the NVIDIA Jetson AGX Xavier confirm that our algorithm can run in real time on low-power platforms such as mobile robots or drones. However, the drawbacks of our algorithm include lower reconstruction accuracy in low-texture and large-scale scenes and ineffective suppression of dynamic features in low-dynamic scenes. Future work will consider replacing and improving deep learning methods and integrating IMU and other sensors to enhance system usability.
Quadrotor Modeling Approaches and Trajectory Tracking Control Algorithms: A Review Abitha M.A.; Abdul Saleem
International Journal of Robotics and Control Systems Vol 4, No 1 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i1.1324

Abstract

Quadrotor unmanned aerial vehicles are utilized in basically every sector of society, including the business, civil, and military industries. Popular applications include delivery, agriculture, target-acquisition, surveying, surveillance, and rescue. They are widely used due to their exceptional features such as accuracy, capability to perform swift inspections, simplicity in deploying perilous and uncertain missions, and additional praiseworthy attributes. This article presents a comprehensive analysis of the theoretical frameworks that have been proposed for the purpose of quadrotor modelling and control. Detailed examinations are conducted on every methodology that underpins the control algorithms, spanning from traditional linear to modern. The analysis looks at hybrid control technique models, which incorporate adaptive components across multiple controllers to improve overall performance and resilience by addressing individual algorithm shortcomings. This analysis also delves deeper into potential future research avenues. These include the development of learning-based or hybrid methodologies that employ machine learning and artificial intelligence to optimize performance and adaptability. For instance, model reference adaptive control systems can learn adaptation laws through machine learning techniques, as opposed to depending on predefined adaptation laws. By training neural networks or fuzzy logic controllers to forecast optimal adaptation parameters based on sensor data, the quadrotor can adjust to fluctuating conditions more effectively. A comparison table is provided to elaborate on the advantages, disadvantages, and hybrid versions of each control algorithm. This will serve as a concise guide that will promote innovation, facilitate the selection and integration of appropriate control algorithms, and enhance the functionality of quadrotor control systems.
Synthesis of Adaptive Sliding Mode Control for Twin Rotor MIMO System with Mass Uncertainty based on Synergetic Control Theory Nguyen Xuan Chiem; Bui Xuan Hai; T. C. Phan
International Journal of Robotics and Control Systems Vol 4, No 1 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i1.1307

Abstract

In this paper, the authors present a new method to synthesize an adaptive sliding controller for Twin Rotor MIMO System (TRMS) based on Synergetic Control Theory (SCT). This system represents a prototype of a helicopter with two degrees of freedom and is widely used in automatic control laboratories. The complexity of the control problem is due to the nonlinear cross-coupling between the main and tail rotors. Uncertainty in system parameters further increases the complexity of the control problem. In Synergetic Control Theory, manifolds are designed for each channel. The control law is found based on sequential manifolds and the Analytical Design of Aggregated Regulators (ADAR) method. The adaptive law when the parameters are uncertain is given based on the analysis of system stability thanks to the Lyapunov function of the first manifold. Finally, the effectiveness of the proposed controller is demonstrated by numerical simulation results and comparison with conventional Sliding Mode Control (SMC).
Predictive Modeling of Energy Consumption in the Steel Industry Using CatBoost Regression: A Data-Driven Approach for Sustainable Energy Management Karthick, K.; Dharmaprakash, R.; Sathya, S.
International Journal of Robotics and Control Systems Vol 4, No 1 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i1.1234

Abstract

This article presents a machine learning model for predicting energy consumption in the steel industry, which aids in energy management, cost reduction, environmental regulation compliance, informed decision-making for future energy investments, and contributes to sustainability. The dataset used for the prediction model comprises 11 attributes and 35,040 instances. The CatBoost prediction algorithm was employed for energy consumption prediction, and hyperparameter optimization was performed using GridSearchCV with 5-fold cross-validation. The developed model has undergone a comparative analysis based on both Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) metrics, demonstrating its promise for accurate energy consumption prediction on both the training and test sets. The proposed model accurately predicts energy consumption for different load types, achieving impressive results on both the training set (RMSE=0.382, R2=0.999, MAPE=1.139) and the test set (RMSE=1.073, R2=0.998, MAPE=1.142). These findings highlight the potential of CatBoost as a valuable tool for energy management and conservation, enabling organizations to make informed decisions, optimize resource allocation, and promote sustainability.
Oil Pipeline Leak Detection in Iraqi Oil Fields based on 1DCNN Mustafa Raad Al-Khalidi; Ahmad Taha Abdulsadda; Mudafeer Sadaq Al Zuhryi
International Journal of Robotics and Control Systems Vol 4, No 1 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i1.1319

Abstract

The oil industry plays a crucial role in Iraq's economy. There's a growing need for technologies that can quickly detect leaks in oil pipelines because leaks can have serious ramifications, including monetary losses, endangerment to public safety, environmental degradation, and resource waste. Advances in technology and software have made it possible to detect leaks. Current approaches often require manual extraction of features, which can be slow and inefficient. This paper presents a new method that proposes using convolutional neural networks (CNNs) for automatic feature extraction. The Iraqi Ministry of Oil, specifically the Basra Oil Company, provided the dataset, such as total distance (km), pressure (bar), and flow rate (STB/d). We split the data into training (70%) and testing (30%) sets. then we calculate metrics such as confusion matrices, accuracy, precision, recall, and F-score to evaluate performance and calculate errors from the regression analysis (root mean square error, root mean absolute error, and relative error). Our contribution to this work is to use 1DCNN to identify leaks, pinpoint their location, and even predict the amount of spilled oil, unlike other research that only uses it to evaluate the presence or absence of a leak only. Additionally, we've created a user-friendly interface for the system. Finally, compare the proposed approach with conventional and alternative methods to show its efficiency. In the future, we plan to expand the system to assess pipeline corrosion and predict its remaining lifespan.
Industry 4.0 Readiness Trends: A Bibliometric and Visualization Analysis Solikhah, Efa Wakhidatus; Asih, Hayati Mukti; Astuti, Fatma Hermining; Ghazali, Ihwan; Mohammad, Effendi Bin
International Journal of Robotics and Control Systems Vol 4, No 1 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i1.1247

Abstract

The Industrial Revolution 4.0 signifies a pivotal change in industrial paradigms, integrating advanced technologies like the Internet of Things (IoT), artificial intelligence (AI), robotics, and big data into production processes. This research aims to analyze the growth and readiness in industry for these changes through a detailed bibliometric analysis. It quantitatively tracks the expansion of Industry 4.0 readiness research, including publication counts, citation trends, and thematic shifts, reflecting heightened academic and industrial interests. A clear definition of Industry 4.0 readiness is provided, focusing on metrics and criteria used for assessment. The paper identifies key contributions and novel insights of the research, emphasizing its practical implications for industry and academia. It examines elements influencing Industry 4.0 readiness, such as infrastructure, policy, and workforce preparedness, offering a comprehensive overview of challenges. The practical implications of our findings are presented, suggesting actionable strategies for stakeholders. This research also highlights the gaps in the current literature, which offers a thorough and multidimensional understanding of Industry 4.0 readiness, its influences, and its impact on the global industrial system.

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