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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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Wireless Sensor Networks Fault Detection and Identification Rastko R. Selmic; Jake Scoggin; Stephen Oonk; Francisco Maldonado
International Journal of Robotics and Control Systems Vol 3, No 4 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i4.1136

Abstract

We have developed and experimentally tested a set of models for the detection and identification of sensor faults that commonly occur in wireless sensor networks. Considered faults include outlier, spike, variance, high-frequency noise, offset, gain, and drift faults. These faults affect the system operations and can endanger operators, final users, and the general public. The fault detection models are divided into two classes: data-centric models, which only analyze a single data stream, and system-centric models, which consider the overall system. For data-centric models, we use the magnitude, the gradient, and the variance of raw sensor data to model faults. For system-centric models, we introduce variogram-based techniques that allow faults to be detected by comparing readings from multiple sensors that measure related phenomena. For data-centric and system-centric sensor fault detection, we show how a few model parameters affect the sensitivity of wireless sensor network fault models. We present simulation and experimental results that illustrate the fault detection and identification models. The system is intended for health monitoring applications of the NASA Stennis Space Center (SSC) test stands and widely distributed support systems, including pressurized gas lines, propellant delivery systems, and water coolant lines. The testbed consists of Coremicro® reconfigurable embedded smart sensor nodes [29] capable of wireless communication, a network-capable application processor, a wireless base station, the software that supports sensor and actuator health monitoring, a database server, and a smartphone running a health monitoring Android application.
A Review on Energy Management of Community Microgrid with the use of Adaptable Renewable Energy Sources Tamosree Saha; Abrarul Haque; Md Abdul Halim; Md Momin Hossain
International Journal of Robotics and Control Systems Vol 3, No 4 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i4.1009

Abstract

The main objective of this paper is to review the energy management of a community microgrid using adaptable renewable energy sources. Community microgrids have grown up as a viable strategy to successfully integrate renewable energy sources (RES) into local energy distribution networks in response to the growing worldwide need for sustainable and dependable energy solutions. This study presents an in-depth examination of the energy management tactics employed in community microgrids using adaptive RES, covering power generation, storage, and consumption. Energy communities are an innovative yet successful prosumer idea for the development of local energy systems. It is based on decentralized energy sources and the flexibility of electrical users in the community. Local energy communities serve as testing grounds for innovative energy practices such as cooperative microgrids, energy independence, and a variety of other exciting experiments as they seek the most efficient ways to interact both internally and with the external energy system. We discuss several energy management tactics utilized in community microgrids with flexible RES, Which include various renewable energy sources (wind, solar power, mechanical vibration energy) and storage devices. Various energy harvesting techniques have also been discussed in this paper. It also includes information on various power producing technology. Given the social, environmental, and economic benefits of a particular site for such a community, this paper proposes an integrated technique for constructing and efficiently managing community microgrids with an internal market. The report also discusses the obstacles that community microgrids confront and proposed methods for overcoming them. This paper analyzes future developments in community microgrids with adaptive RES. The study discusses potential developments in community microgrids with flexible energy trading systems.
Intelligent Controller Based on Artificial Neural Network and INC Based MPPT for Grid Integrated Solar PV System Anil Kumar; Priyanka Chaudhary; Owais Ahmad Shah
International Journal of Robotics and Control Systems Vol 3, No 4 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i4.1150

Abstract

Solar photovoltaic (PV) systems have become an integral part of today's advanced energy infrastructure due to its low kinetic energy, its abundance availability, and its freedom from human interference. Solar PV systems have the potential to greatly reduce our reliance on fossil fuels, but their intermittent nature means they cannot provide a constant source of electricity. The system's security should be well thought out, and it should be able to withstand a lot of abuse. The current energy system faces a significant difficulty in ensuring continuous supply. In this study, a three-phase, two-stage photovoltaic system that is managed by artificial neural networks (ANN). A DC-DC boost converter with maximum power point tracking (MPPT) based on the incremental conductance (INC) method is incorporated in the first stage. In the next step, an ANN-based controller optimizes the performance of a three-phase switching PWM inverter that is connected to the grid by controlling currents along the d-q axis. Comprehensive simulations were carried out using MATLAB or Simulink to evaluate the system's performance under various illumination and temperature conditions. Results show that the suggested approach outperforms the baseline in a number of areas. Better dynamic reactions, accurate tracking of reference currents within permissible bounds, and quick settling periods after startup are all displayed by it. These findings show that our method has the potential to greatly improve the efficiency and dependability of solar PV systems. The results of this study have implications for renewable energy in general and present a viable path toward enhancing the resilience and sustainability of energy infrastructure.
Real-Time Obstacle Detection for Unmanned Surface Vehicle Maneuver Anik Nur Handayani; Ferina Ayu Pusparani; Dyah Lestari; I Made Wirawan; Aji Prasetya Wibawa; Osamu Fukuda
International Journal of Robotics and Control Systems Vol 3, No 4 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i4.1147

Abstract

The rapid advancement and increasing demand for Unmanned Surface Vehicle (USV) technology have drawn considerable attention in various sectors, including commercial, research, and military, particularly in marine and shallow water applications. USVs have the potential to revolutionize monitoring systems in remote areas while reducing labor costs. One critical requirement for USVs is their ability to autonomously integrate Guidance, Navigation, and Control (GNC) technology, enabling self-reliant operation without constant human oversight. However, current study for USV shown the use of traditional method using color detection which is inadequate to detect object with unstable lighting condition. This study addresses the challenge of enabling Autonomous Surface Vehicles (ASVs) to operate with minimal human intervention by enhancing their object detection and classification capabilities. In dynamic environments, such as water surfaces, accurate and rapid object recognition is essential. To achieve this, we focus on the implementation of deep learning algorithms, including the YOLO algorithm, to empower USVs with informed navigation decision-making capabilities. Our research contributes to the field of robotics by designing an affordable USV prototype capable of independent operation characterized by precise object detection and classification. By bridging the gap between advanced visualization techniques and autonomous USV technology, we envision practical applications in remote monitoring and marine operations with object detection. This paper presents the initial phase of our research, emphasizing significance of deep learning algorithms for enhancing USV navigation and decision-making in dynamic environmental conditions, resulting in mAP of 99.51%, IoU of 87.80%, error value of the YOLOv4-tiny image processing algorithm is 0.1542.
Performance Investigation of Low-Cost Dual-Axis Solar Tracker using Light Dependent Resistor Chivorn Keo; Sarot Srang; Rattana Seng
International Journal of Robotics and Control Systems Vol 3, No 4 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i4.1137

Abstract

To generate power, the solar tracker mechanism can be mounted on a stationary or mobile platform. Moving platforms include boats, ground vehicles, and aerial vehicles. The solar tracker must be a mechanism that can keep the solar panel perpendicular to the direction of the sun at an appropriate level of precision in order to be more effective. Therefore, this research is going to investigate the performance of a low-cost dual-axis solar tracker (parallel mechanism) installed on a moving platform. This work describes the simulation and experiment of a dual-axis solar tracker that is mounted on a rotating support plate with rotational axis. This simulation uses the method of controlling linear actuators to adjust the solar panel perpendicular to the direction of sunlight. Both actuators were controlled by proportional and integral controllers (PI), which will make the system have a faster response time. The tracker is equipped with a type of low-cost sunlight sensor to provide the information for determining the orientation of the sunlight vector with respect to the solar panel. The sunlight sensor was designed and fabricated on our own by adding four light-dependent resistors in the four different quadrants. For the purpose of tracking the sun, the mathematical models of the tracker mechanism, sun sensor, and control architecture are defined. The results of simulation and experiment demonstrate that the tracker control system can follow the sun with some tracking error (about 2 degrees) at its final alignment. In real-time applications, solar trackers can be used on vehicles or boats to adjust solar panels on their surfaces and increase their exposure to sunlight and electrical output.
Study on Viral Transmission Impact on Human Population Using Fractional Order Zika Virus Model Dhanalakshmi Palanisami; Shrilekha Elango
International Journal of Robotics and Control Systems Vol 3, No 4 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i4.1105

Abstract

This work comprises the spread of Zika virus between humans and mosquitoes as a mathematical simulation under fractional order, which also incorporates the asymptotically infected human population. For determining the solution of the model the fuzzy Laplace transform technique is utilized. By combining fuzzy logic with the Laplace transform, we can analyze systems even when we lack precise information. Further, the sensitivity analysis is performed to validate the model. On top of that the population dynamics of both human and mosquito populations are discussed using numerical data and the graphical result of the model is presented. The main objective of this work is to study the dynamics of the Zika virus and to examine the effect of virus on humans when the transmission occurs between humans and from mosquitoes, under fractional order. The outcome of these comparisons suggests that even by reducing a minute fractional part of transmission through mosquitoes results in a greater reduction of Zika exposed population. The comparisons improve the understanding of fractional level transmission resulting in more effective drug administration to patients. The Hyers-Ulam stability method is a mathematical technique used to study the stability of functional equations. Eventually, Ulam Hyers and Ulam Hyers Rassias stability are employed to assess the stability of the proposed model.
Using Active Filter Controlled by Imperialist Competitive Algorithm ICA for Harmonic Mitigation in Grid-Connected PV Systems Hadi, Husam Ali; Kassem, Abdallah; Amoud, Hassan; Nadweh, Safwan; Ghazaly, Nouby M.; Moubayed, Nazih
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.v4i2.1365

Abstract

Solar energy has been gaining momentum recently, with a focus on maximizing its investment potential due to its reputation as the most sustainable and efficient energy source. This shift towards solar power could potentially reduce the reliance on oil-based fuels in the future. As a result of the integration of photovoltaic (PV) energy sources into the grid, the reliability of power distribution and maintaining its quality in these systems has become increasingly important. The presence of non-linear loads in these grids causes distortion of both voltage and current waves on the grid side, so it is necessary to implement effective reduction techniques to reduce the distortions in these waves. The research contribution is TO introduce the integration of an active filter on the dc side of grid-connected PV systems, along with a control circuit for the filter switches. The control switches were operated using a Sinusoidal Pulse Width Modulation (SPWM) control scheme, while the controller parameters were tuned using the Imperialist Competitive Algorithm (ICA). The proposed system was simulated in the MATLAB/Simulink environment with variations in solar radiation and temperature. The simulation results demonstrated a reduction in the total harmonic distortion factor (THD) for voltage and current waveforms on the grid side, which are within the permissible limits. This confirms the effectiveness of the proposed filter and the efficiency of the control strategy and algorithm for parameter adjustment.
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.
Deep Learning-Based Automated Approach for Classifying Bacterial Images Abougarair, Ahmed Jaber; Oun, Abdulhamid A.; Sawan, Salah I.; Ma’arif, Alfian
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.v4i2.1423

Abstract

Identifying and classifying bacterial species from microscopic images is crucial for medical applications like prevention, diagnosis, and treatment. However, because of their diversity and variability in appearance, manually classifying bacteria is difficult and time-consuming. This work suggests employing deep learning architecture to automatically categorize bacterial species in order to overcome these difficulties and raise the accuracy of bacterial species recognition. We have evaluated our suggested approach using the Digital Images of Bacteria Species (DIBaS), a publicly accessible resource of photographs of tiny bacteria.  This work uses a dataset that differs in terms of bacterial morphology, staining methods, and imaging circumstances. This paper aims to enhance the accuracy and reduce the computational requirements for Convolutional Neural Networks (CNN) based classification of bacterial species using GoogLeNet and AlexNet to train the models. This paper focuses on employing transfer learning to retrain pre-trained CNN models using a dataset consisting of 2000 images encompassing 12 distinct bacteria species known to be harmful to human health.  The concept of transfer learning was utilized to expedite the network's training process and enhance its categorization performance.  The results are promising, with the method achieving an accuracy of 98.7% precision, recall of 99.50%, and an F1-score of 99.45%   with classifier speed. Furthermore, the proposed bacteria classification approach demonstrated strong performance, irrespective of the size of the training data used.  This paper contributes by automating bacterial classification to facilitate faster and more accurate identification of bacterial species, which facilitates the treatment of infections and related diseases, in addition to monitoring public health, and promoting the wise use of antimicrobial drugs. To improve outcomes in the future, researchers can also integrate deep learning techniques with other machine learning methods.

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