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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 361 Documents
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Analysis and Performance Validation of CRONE Controllers for Speed Control of a DC Motor Velmurugan, V.; Venkatesan, M.; Praboo, N. N
International Journal of Robotics and Control Systems Vol 4, No 2 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i2.1343

Abstract

In recent decades controllers play a major role for an efficient control and reliable operation of an industrial process. Hence in this paper, a special kind of Commande Robuste d’Ordre Non Entier (CRONE) controller is designed for controlling the speed of DC Motor (DCM). The proper design procedure of two generations CRONE control strategies named as First-Generation CRONE (FGC) and Second-Generation CRONE (SGC) controllers are implemented through the transfer function of armature-controlled DCM. Simulations are performed on MATLAB software in order to investigate the servo responses of two designed CRONE controllers, besides the results are presented in terms of settling time(ts), rise time(tr), Integral Square Error (ISE) and Integral Absolute Error (IAE). In addition, the relay PI controller is designed and the simulation results are presented for comparison purpose. It is evident from the comparing results that the SGC controller is superior for effective process control.
Multi-Objective Particle Swarm Optimization for Enhancing Chiller Plant Efficiency and Energy Savings Bhardwaj, Yogesh; Shah, Owais Ahmad; Kumar, Rakesh
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1501

Abstract

This study aims to enhance operational efficiency in chiller plants by implementing the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. The primary objectives are to simultaneously reduce energy consumption and increase cooling efficiency, addressing the challenges posed by variable environmental and operational conditions. Employing the MOPSO algorithm, this research conducts a detailed analysis using real-time environmental data and operational parameters. This approach facilitates a dynamic adaptation to changes in ambient temperature and electricity pricing, ensuring that the algorithm's application remains effective under fluctuating conditions. The application of MOPSO has resulted in significant reductions in energy use and improvements in cooling efficiency. These results demonstrate the algorithm's capacity to optimize chiller plant operations dynamically, adapting to changes in environmental conditions and operational demands. The study finds that MOPSO's adaptability to dynamic operational conditions enables robust energy management in chiller plants. This adaptability is crucial for maintaining efficiency and cost-effectiveness in industrial applications, especially under varying environmental impacts. The paper contributes to the field by enhancing the understanding of how advanced optimization algorithms like MOPSO can be effectively integrated into energy management systems for chiller plants. A novel aspect of this research is the integration of real-time data analytics into the optimization process, which significantly improves the sustainability and operational efficiency of HVAC systems. Furthermore, the study outlines the potential for similar research applications in large-scale HVAC systems, where such algorithmic improvements can extend practical benefits. The findings underscore the importance of considering a broad range of environmental and operational factors in the optimization process and suggest that MOPSO's flexibility and robustness make it a valuable tool for achieving sustainable and cost-effective energy management in industrial settings.
Novel Leak Detector Based on DWT an Experimental Study Meftah, Sabir; Bentoumi, Miloud; Burhanuddin, Dirman Hanafi; Bakhti, Haddi; Chabira, Chaima
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1458

Abstract

We always face water leakage problems in underground distribution water networks (DWNs). Existing leak detectors suffer from false alarms due to poor leak signal quality affected by external noise, often collected by acoustic or vibratory sensors. This paper introduces a novel Discrete Wavelet Transform Detector (DWTD) that leverages precise pressure signals non-influenced by environmental noise. Using a prototype of a 100m PEHD pipeline and a diameter of 40mm, Data from two pressure transmitters were collected using a dSPACE MicroLabBox unit. The main idea is to apply the Discrete Wavelet Transform (DWT) with a DONOHO threshold law to cancel noises due to water turbulence fluctuations, ensuring high-quality signals for accurate leak detection and localization. As benchmarks to assess the quality of denoising signals three parameters were calculated, Signal to Noise Ratio (SNR 26.6763 dB), Normalized Cross-Correlation (NCC≈1), and Mean Square Error (0.20573 MSE 48.4761). The denoised temporal signals are obtained from the Inverse Discrete Wavelet Transform (IDWT). A Cross-correlation is employed to these signals to determine the leak’s location. The experimental validation involves positioning the first and second transmitters at specific distances on both sides of the leak position. This allows for comparison between the actual leak position in advance known and calculated positions at various points and leak sizes. With only a few exceptions where the maximum error rate reached 5 meters from the actual leak position, the detector's effectiveness was proven across tests involving four different leak sizes.
Comparative Analysis of Path Planning Algorithms for Multi-UAV Systems in Dynamic and Cluttered Environments: A Focus on Efficiency, Smoothness, and Collision Avoidance Sukwadi, Ronald; Airlangga, Gregorius; Basuki, Widodo Widjaja; Kristian, Yoel; Rahmananta, Radyan; Sugianto, Lai Ferry; Nugroho, Oskar Ika Adi
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i4.1555

Abstract

This study evaluates the performance of various path planning algorithms for multi-UAV systems in dynamic and cluttered environments, focusing on critical metrics such as path length, path smoothness, collision avoidance, and computational efficiency. We examined several algorithms, including A*, Genetic Algorithm, Modified A*, and Particle Swarm Optimization (PSO), using comprehensive simulations that reflect realistic operational conditions. Key evaluation metrics were quantified using standardized methods, ensuring the reproducibility and clarity of the findings. The A* Path Planner demonstrated efficiency by producing the shortest and smoothest paths, albeit with minor collision avoidance limitations. The Genetic Algorithm emerged as the most robust, balancing path length, smoothness, and collision avoidance, with zero violations and high feasibility. Modified A* also performed well but exhibited slightly less smooth paths. In contrast, algorithms like Cuckoo Search and Artificial Immune System faced significant performance challenges, especially in adapting to dynamic environments. Our findings highlight the superior performance of the Genetic Algorithm and Modified A* under these specific conditions. We also discuss the potential for hybrid approaches that combine the strengths of these algorithms for even better performance. This study's insights are critical for practitioners looking to optimize multi-UAV systems in challenging scenarios.
Cucumber Disease Image Classification with A Model Combining LBP and VGG-16 Features Arifin, Miftahol; Yuniarti, Anny; Suciati, Nanik
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1529

Abstract

Cucumber (Cucumis sativus) is a significant horticultural crop worldwide, highly valued for both fresh consumption and processing. However, cucumber cultivation faces challenges due to diseases that can substantially reduce yield and quality. Diseases like leaf spots, stem wilt, and fruit rot are caused by pathogens including viruses, bacteria, and fungi. Traditionally, disease detection in cucumbers is performed manually, which is time-consuming and inefficient. Therefore, developing machine vision-based models using Deep Learning (DL) and Machine Learning (ML) for early disease detection through image analysis is crucial for assisting farmers. While many studies on plant disease classification using various DL and ML models show optimal results, research on cucumbers has mostly focused on leaf diseases. This study aims to optimize cucumber disease image classification by developing a model that combines Local Binary Pattern (LBP) texture features and VGG-16 convolutional features. The dataset used, Cucumber Disease Recognition Dataset consists of 8 classes of cucumber plant disease images covering leaves, stems, and fruits. This study classifies cucumber plant disease images using Random Forest (RF) combined with LBP texture features and VGG-16 visual features and compares its performance with models using VGG-16, LBP+RF, and VGG-16+RF on the same dataset. The results show that the proposed model achieved a precision of 84.7%, recall of 84%, F1-Score of 83.8%, and accuracy of 84%. These results outperform the comparative models, demonstrating the effectiveness of the combined approach in classifying cucumber plant diseases.
Design and Implementation of Smell Agent Optimizer for Parameters Estimation of Single and Double Diode in PV System: A Comparative Analysis Elnaggar, Mohamed F.
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1490

Abstract

One of the most important and desirable options for moving toward clean electric energy sources is solar energy. Therefore, a PV system's characteristics play a significant role in determining how effective it is across a range of temperature and radiation scenarios. One can consider the PV model's parameter estimation to be a nonlinear optimization situation. This work makes use of a novel application of the smell agent optimizer (SAO) created to forecast the undefined parameters of the PV model's single- and two-diode equivalent circuits.  The goal of this effort is to create an accurate photovoltaic model that can accurately represent its performance under variable operating conditions. The square of the mean squared error between the actual measured curve and the current-voltage curve derived from the model defines the intended objective function. The suggested system is constructed and tested experimentally in a range of temperature and light conditions. Next, the MATLAB software is used to create the simulated PV model integrated with the SAO. The PV parameters are then predicted by comparing the experimental data with the convergence of the SAO based on the PV model. Based on the observed properties, the suggested approach for determining the parameters of an actual solar cell has been put into practice and contrasted with eight other optimization techniques. The outstanding efficacy of the method utilized compared with alternate methods is demonstrated by the statistical comparison of the ideal objective function resulting from the difference in the current-voltage curve produced from the optimized circuit model and the measurement.
Investigating the Influence of Temperature on UAV Signal Quality Abbas, Ahmed Hussein; Abdulsadda, Ahmad Taha; Hameed, Hassanain Ghani
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1353

Abstract

Advancements in drone technology make them important in many areas. military, industry, and disaster The efficacy of a drone's communication systems can be greatly impacted by temperature fluctuations, either from environmental conditions or mechanical problems in the drone's construction. This study gives an analysis and computational model of the impact of temperature on the performance of drone communication. Utilizing a one-dimensional convolutional neural network, we aim to forecast the signal-to-noise ratio (SNR) and received signal strength indicator (RSSI). Following the initial stage of dataset creation in the drone laboratory, proceed to reprocess the dataset and divide it into a 70% training set and a 30% testing set. Subsequently, a graphical user interface (GUI) was developed using MATLAB App Designer to enhance user friendliness. The outcome suggests that the efficiency of the drone communication system  declines with rising temperatures.  Using 1DCNN is our contribution to this work; other studies depend only on simulation to assess performance. One benefit of 1DCNN is that the impact may be evaluated by automatically extracting important features from the input dataset. Using 1DCNN is our special addition to this project; other research evaluate the UAV communication system's effectiveness only through simulation. We propose in this work to optimize system characteristics for improved performance, including power transfer, by adding a feedback loop between the CNN result and the communication system. Furthermore, we investigate how different weather conditions, such wind and rain, affect UAV communication systems.
Design and Implementation of Voltage Source Inverter Using Sinusoidal Pulse Width Modulation Technique to Drive A Single-Phase Induction Motor Shneen, Salam Waley; Abdullah, Zainab B.; Dakheel, Hashmia S.
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1541

Abstract

A study is underway under the title, Design and implementation of voltage source inverter using sinusoidal pulse width modulation technique to drive a single-phase induction motor. The objectives of the study can be achieved by building a simulation model for a single-phase full-wave inverter consisting of four IGBT transistors. The inverter converts a direct voltage of 220 volts from the power source connected to the inverter input to an alternating voltage of 220 volts RMS. A 10-ohm resistive load is fed to the inverter output. In the first test, a square wave is generated as a result of operating the inverter in the first mode, as a result of activating two electronic switches that give the value of the voltage wave to the load, while the second mode gives the negative voltage with an interval of ten milliseconds for each mode, i.e., at a frequency of 50 Hz for twenty milliseconds for the square wave generated at the inverter output. The other model uses sinusoidal pulse width modulation technique to remove harmonics and control the inverter output by opening and closing electronic switches, which leads to removing some harmonics. The third model depends on adding a filter to obtain the basic wave and get rid of the rest of the harmonics, which results in generating a sine wave. After obtaining an inverter model that converts 220 volts direct voltage to 220 volts alternating voltage RMS as a first stage, the second stage is to feed a single-phase induction motor and operate it under test conditions that include a no-load condition, i.e., zero torque, a constant load condition, i.e., 1 Newton-meter torque, and finally a variable load condition, which is similar to many applications such as a fan, pump, etc. From the simulation results, we can say that the system is effective in operating the induction motor at the specified speed (1430 rpm) after providing the specified electrical quantities, a frequency of 50 Hz, and a voltage of 220 volts alternating voltage RMS.
Photovoltaic Energy Anomaly Detection using Transformer Based Machine Learning Wirawan, I Made; Wibawa, Aji Prasetya; Widiyanintyas, Triyanna
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1260

Abstract

This study uses the Anomaly Transformer model to find anomalies in photovoltaic energy generation in Malang, Indonesia. The main background of this study is the lack of satellite monitoring in this region and the importance of annual data for electricity generation forecasting. Temperature scattered direct solar radiation, and hourly electricity production are all part of the dataset used which is only available since 2019. Anomalies were detected at 05.00 and 16.00 WIB, indicating instability in the energy supply due to high temperatures in the morning and heavy rain in the afternoon. Detection of these anomalies is important to improve the efficiency and reliability of photovoltaic systems, reduce operational costs, and reduce the risk of system failure. Indonesia has many challenges for photovoltaic energy generation due to its unique location, with many islands located close to the equator. The use of the Anomaly Transformer algorithm improves the accuracy of anomaly detection over conventional methods. This algorithm helps to find complex patterns in very large time series. The results show that the anomaly transformer model can effectively detect anomalous patterns. It offers ideas to improve the stability and efficiency of photovoltaic systems in Malang and other areas with comparable environmental conditions. Improved energy efficiency and environmental sustainability are the results of anomaly pattern detection.
Optimized Fault Detector Based Pattern Recognition Technique to Classify and Localize Electrical Faults in Modern Distribution Systems Mishra, Chandra Sekhar; Jena, Ranjan Kumar; Sinha, Pampa; Paul, Kaushik; Mahmoud, Mohamed Metwally; Elnaggar, Mohamed F.; Hussein, Mahmoud M.; Anwer, Noha Mohammed
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1474

Abstract

This research presents a method that integrates artificial neural networks (ANN) and discrete wavelet transform (DWT) to identify and classify faults in large power networks, as well as to pinpoint the zones where these faults occur. The objective is to enhance reliability and safety by accurately detecting and categorizing electrical faults. To manage the computational demands of processing the extensive and complex data from the power system, the network is divided into optimal zones, each made visible for fault detection. Niche Binary particle swarm optimization (NBPSO) is employed to place the fault detectors (FD) in each zone. This allows for precise measurement of fault voltage and current phasors without significant cost. The ANN module is tasked with identifying the fault area and locating the exact fault within that zone, as well as classifying the specific type of fault. Discrete Wavelet Transform is used for feature extraction, and a phase locked loop (PLL) is used for load angle computation. The proposed method's validity has been tested on the IEEE-33 bus distribution network.

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