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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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Smart Indoor Plantation System Using Soil Moisture Sensor and Light Dependent Resistor Sensor N. S. Abu; W. M. Bukhari; M. A. Firdaus; N. M. Sukri
International Journal of Robotics and Control Systems Vol 2, No 4 (2022)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v2i4.845

Abstract

Plantation methods, including hydroponics, have been extensively used in agriculture. They also employed a time-based irrigation system for the plant. The goal of this project was to create a self-sustaining indoor plantation system that uses soil moisture sensor data to control the flow of water when the sensor detects that the soil is almost dry. Soil conditions are monitored, and crops are irrigated more efficiently with the help of this new technology. Water is conserved by just watering the plants when they absolutely need it, rather than watering them continually all the time as the traditional method would require. Light-dependent resistors are used to measure the brightness of the surroundings in this project. As a result, the grow light will be activated when the ambient light level drops. With the help of a soil moisture sensor and a light-dependent resistor (LDR), one can create a system that automatically waters and lights plants. Finally, the soil moisture sensor collects data for the sprinkler system and displays it on the LCD screen, and then the appropriate measures are taken. When the soil's humidity level is high, the water that flows will be stopped.
Multi-objective Fractional Order PID Controller Optimization for Kid's Rehabilitation Exoskeleton Intissar Zaway; Rim Jallouli-Khlif; Boutheina Maaleja; Hanene Medhaffar; Nabil Derbela
International Journal of Robotics and Control Systems Vol 3, No 1 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i1.840

Abstract

Fractional order Controllers have been used in several industrial cases to achieve better performance of the systems. This paper proposes a Fractional Order Proportional Integral Derivative (FOPID) controller. It is synthesized using Oustaloup approximation, and its parameters are tuned using the Genetic Algorithm (GA) optimization method. The aim is to minimize the error, the energy and the startup torques using two objective functions to improve the control performances and the robustness. The validity of the proposed controller is shown via simulation by controlling a two-link exoskeleton for children's gait rehabilitation, and the results are compared to an Integer order PID (IOPID) controller. Simulation results clearly indicate the superiority of the optimized FOPID in terms of trajectory tracking and the used torques. Moreover, the FOPID controller is tested with parameter uncertainties. Its robustness is proven against thigh and shank masses variation. Both controllers are simulated under the same frequency conditions using Simulink MATLAB R2018a.
Combination of Genetic Algorithm and Neural Network to Select Facial Features in Face Recognition Technique Taraneh Kamyab; Haitham Daealhaq; Ali Mojarrad Ghahfarokhi; Fatemehalsadat Beheshtinejad; Ehsan Salajegheh
International Journal of Robotics and Control Systems Vol 3, No 1 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i1.849

Abstract

Face recognition methods are computational algorithms that follow aim to identify a person's image according to the bank of images they have of different people. So far, various methods have been proposed for face recognition, which can generally be divided into two categories based on face structure and based on facial features. Based on this, many algorithms have been introduced and used for face recognition. Genetic algorithm has been one of the successful algorithms for face recognition. In this article, we first briefly explained the genetic algorithm and then used the combination of neural network and genetic algorithm to select and classify facial features The presented method has been evaluated using individual features and combined features of the face region. Composite features perform better than face region features in experimental tests. Also, a comprehensive comparison with other facial recognition techniques available in the FERET database is included in this paper. The proposed method has produced a classification accuracy of 94%, which is a significant improvement and the best classification accuracy among the results established in other studies.
Development of a 3D Printed Biologically Inspired Monoped Self-Balancing Robot Denis Manolescu; Emanuele Lindo Secco
International Journal of Robotics and Control Systems Vol 3, No 1 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i1.841

Abstract

The development of lightweight, agile robots built with a high degree of efficiency and precision is a complex and particularly challenging task. In Robotics, the perspectives of such systems have the potential to reimagine and reevaluate space and planetary explorations while enhancing the human ability to reach and analyze different environments. This research evaluates ways to build a single-leg robot capable of self-balancing on any terrain with the prospect of omnidirectional saltation steering. This report will go through the process of designing a 1-degree of freedom, balanced robot while evaluating various hardware options in motion control and data feedback. Based on these research findings, this paper will also discuss the difficulties and issues encountered and the approach to solving them.
Energy Monitoring and Control of Automatic Transfer Switch between Grid and Solar Panel for Home System Joko Slamet Saputro; Hari Maghfiroh; Feri Adriyanto; Muhammad Renaldy Darmawan; Muhammad Hamka Ibrahim; Subuh Pramono
International Journal of Robotics and Control Systems Vol 3, No 1 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i1.843

Abstract

In this digital age, most aspects of life require an electric supply. The availability of electrical power is very critical to maintaining the continuity of the system in various applications. To maintain system continuity, the Energy Monitoring and Control of an Automatic Transfer Switch (ATS) between the Grid and Solar Panel is proposed. The system consists of an Automatic Transfer Switch, Home Solar Power Plant, and Automatic Charging. The function of the monitoring system is to monitor the voltage, current, and power across the device, the operation mode of the ATS, and the State of Charge (SoC) of the battery. The control system is to control the operation mode of ATS according to the energy source selected by the user on the Internet of Things (IoT) interface. The results show that the system can successfully monitor solar panel conditions, AC output, and battery's State of Charge through Blynk IoT. The ATS works automatically with a switching delay of 20ms to 26ms, while on the user's command, the average switching delay is 303.33ms to activate the relay and 185ms to deactivate the relay.
Adaptive Droop Control Strategy for Load Sharing in Hybrid Micro Grids Mahmoud Zadehbagheri; Alfian Ma'arif; Mohammad Javad Kiani; Ali Asghar Poorat
International Journal of Robotics and Control Systems Vol 3, No 1 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i1.838

Abstract

Energy management becomes essential when distributed energy sources such as solar, wind, and fuel cell are connected in a micro-grid. In this paper, using a combination of two powerful neural network tools and fuzzy logic, intelligentization, and adaptation of droop control along with voltage and current control as one of the most common methods of decentralized control is done. One of the essential features of this method is its fast performance and the need for telecommunication infrastructure. In this paper, we provide a comprehensive control system that enables proper operation in both upstream and island network modes for both AC and DC microgrids. The proposed method is simulated to evaluate its effectiveness. The proposed structure, by adapting the control system by ANFIS structure, can properly distribute power between distributed products in a brilliant way and without the need for operators and costly telecommunications infrastructure in the face of severe disturbances such as change. The state between the connection and the island or fault occurrence ensures the stability of both the AC and DC parts of the microgrid. The results show the effectiveness of the proposed method.
Neuro-Fuzzy Decision Support System for Optimization of the Indoor Air Quality in Operation Rooms Najmeh Jamali; Mohammad Reza Gharib; Behzad Omidi Koma
International Journal of Robotics and Control Systems Vol 3, No 1 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i1.854

Abstract

In order to minimize surgical site infections, indoor air quality in hospital operating rooms is a major concern. A wide range of literature on the relevant issue has shown that air contamination diminution can be attained by applying a more efficient set of monitoring and controlling systems that improve and optimize the indoor air status level. This paper discusses a fuzzy inference system (FIS) and the integrated model neuro-fuzzy inference system (ANFIS) focusing on the control of contamination via proper airflow distribution in an operating room, which is essential to guarantee the accuracy of the surgical procedure. A deep learning estimation approach is proposed to predict incidence in the presence of airborne contamination. The project's goal is to reduce airborne contamination to improve the surgical environment and reduce the predicted incidence during surgeries. The neuro-fuzzy deep learning model was trained with a neural network structure and tested by considering 3 important parameters that affected the air quality introducing the specialization of the system to control the model’s target. Finally, the proposed approach has been put into practice by making use of data collected by sensors placed within a real operating room in a hospital in Mashhad, Iran. The proposed model attains 97.3% and 95% validation accuracy for estimating the relative humidity and particles, respectively. The efficacy of the proposed neuro-fuzzy indicates that the system significantly lowers risk values and enhances indoor air quality.
Understanding of Convolutional Neural Network (CNN): A Review Purwono, Purwono; Ma'arif, Alfian; Rahmaniar, Wahyu; Fathurrahman, Haris Imam Karim; Frisky, Aufaclav Zatu Kusuma; Haq, Qazi Mazhar ul
International Journal of Robotics and Control Systems Vol 2, No 4 (2022)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v2i4.888

Abstract

The application of deep learning technology has increased rapidly in recent years. Technologies in deep learning increasingly emulate natural human abilities, such as knowledge learning, problem-solving, and decision-making. In general, deep learning can carry out self-training without repetitive programming by humans. Convolutional neural networks (CNNs) are deep learning algorithms commonly used in wide applications. CNN is often used for image classification, segmentation, object detection, video processing, natural language processing, and speech recognition. CNN has four layers: convolution layer, pooling layer, fully connected layer, and non-linear layer. The convolutional layer uses kernel filters to calculate the convolution of the input image by extracting the fundamental features. The pooling layer combines two successive convolutional layers. The third layer is the fully connected layer, commonly called the convolutional output layer. The activation function defines the output of a neural network, such as 'yes' or 'no'. The most common and popular CNN activation functions are Sigmoid, Tanh, ReLU, Leaky ReLU, Noisy ReLU, and Parametric Linear Units. The organization and function of the visual cortex greatly influence CNN architecture because it is designed to resemble the neuronal connections in the human brain. Some of the popular CNN architectures are LeNet, AlexNet and VGGNet.
Controlling Pulse-Like Self-Sustained Oscillators Using Analog Circuits and Microcontrollers Ulrich Simo Domguia; Raoul Thepi Siewe
International Journal of Robotics and Control Systems Vol 3, No 1 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i1.802

Abstract

Using simulation from analog electronic circuits and from a microcontroller, this paper considers the control or synchronization of pulse-like self-sustained oscillators described by the equations derived from the chemical system known as Brusselator. The attention is focused on the effect of proportional control when the Brusselator is subjected to disturbances such as pulse-like oscillations and square signals. The analog electronic circuits simulation is based on Multisim, while the microcontroller simulation uses mikroC software and PIC 18F4550. In order to determine the intervals for which the synchronization is effective, the equations of the Brusselator are solved numerically using the fourth-order Runge-Kutta method. As software used for conducting numerical simulations, FORTRAN 95 version PLATO is used for numerical simulation and MATLAB for plotting curves using the data generated from FORTRAN simulations. It has been shown that the control is effective for some values of the proportional control parameter. A good qualitative and quantitative agreement is found from the results of the numerical simulation and those obtained from the analog electronic circuits as well as those delivered by the microcontroller. Since the oscillations delivered by the heart are pulsed oscillations, this study gives an idea of how to control the heart frequency of an individual whose heart is subject to certain disturbances related to stress or illness, to name just a few examples.
Monte Carlo Simulations on 2D LRF Based People Tracking using Interactive Multiple Model Probabilistic Data Association Filter Tracker Zulkarnain Zainudin; Sarath Kodagoda
International Journal of Robotics and Control Systems Vol 3, No 1 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i1.896

Abstract

Consistency of tracking filter such as Interactive Multiple Model Probabilistic Data Association Filter (IMMPDAF) is the most important factor in targets tracking. Inaccurate tracking capability will lead to poor tracking performance when dealing with multiple people's interactions and occlusions. In order to validate the consistency, Normalized Estimation Error Squared (NEES) and Normalized Innovation Squared (NIS) were evaluated and tested using Monte Carlo experiments for 50 runs. These simulations has proven that the tracker is conditionally consistent on targets tracking despite the fact that it has difficulties on handling occlusions and maneuvering people. NEES requires ground truth of tracking data and predicted data, whereas NIS requires observation and predicted data for Monte Carlo simulations. In NEES simulations, the result emphasizes that state estimation errors of IMMPDAF tracker are inconsistent with filter-calculated covariances especially when dealing with sudden turns in zig-zag motion where quite a large number of points fall outside 95\% probability region. In NIS simulations, IMMPDAF tracker is confirmed to have difficulties to handle multiple targets with a short period of occlusion although a small number of points falls outside of 95\% probability region. Filter tracker is considered mismatched when dealing with zig-zag motion; however, it deemed to be optimistic when dealing with occlusions. As a result, the IMMPDAF tracker has limited capability in monitoring sharp turns under occlusion conditions, although it is acceptable when dealing with occlusions only.

Page 10 of 37 | Total Record : 361

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