International Journal of Robotics and Control Systems
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.
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<![CDATA[Adaptive Neuro-Fuzzy Self Tuned-PID Controller for Stabilization of Core Power in a Pressurized Water Reactor ]]>
<![CDATA[Hany Abdelfattah]]>;
<![CDATA[Said A. Kotb]]>;
<![CDATA[Mohamed Esmail]]>;
<![CDATA[Mohamed I. Mosaad]]>
<![CDATA[ International Journal of Robotics and Control Systems Vol 3, No 1 (2023) ]]>
Publisher : <![CDATA[Association for Scientific Computing Electronics and Engineering (ASCEE) ]]>
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DOI: 10.31763/ijrcs.v3i1.710
<![CDATA[There has been a lot of interest in generating electricity using nuclear energy recently. This interest is due to the features of such a source of energy. The main part of the nuclear energy system is the reactor core, especially the most widely used Pressurized Water Reactor (PWR). This reactor is the hottest part of the nuclear system; security risks and economic possibilities must be considered. Controlling this reactor can increase the security and efficiency of nuclear power systems. This study presents a dynamic model of the (PWR), including the reactor's core, the plenums of the upper and lower, and the connecting piping between the reactor core and steam generator. In addition, an adaptive neuro-fuzzy (ANFIS) self-tuning PID Controller for the nuclear core reactor is presented. This adaptive controller is used to enhance the performance characteristics of PWR by supporting the profile of the reactor power, the coolant fuel, and hot leg temperatures. The suggested proposed ANFIS self-tuning controller is estimated through a comparison with the conventional PID, neural network, and fuzzy self-tuning controllers. The results showed that the proposed controller is best over traditional PID, neural network, and fuzzy self-tuning controllers. All simulations are throughout by using MATLAB/SIMULINK.]]>
<![CDATA[Deep Learning-based Attack Detector for Bilateral Teleoperation Systems ]]>
<![CDATA[Yousif Ahmed Al-Wajih]]>;
<![CDATA[Mutaz M. Hamdan]]>;
<![CDATA[Turki Bin Mohaya]]>;
<![CDATA[Magdi S. Mahmoud]]>;
<![CDATA[Nezar M. Al-Yazidi]]>
<![CDATA[ International Journal of Robotics and Control Systems Vol 3, No 1 (2023) ]]>
Publisher : <![CDATA[Association for Scientific Computing Electronics and Engineering (ASCEE) ]]>
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DOI: 10.31763/ijrcs.v3i1.822
<![CDATA[A teleoperation system is referred to as a plant that is controlled remotely, and it is often composed of a human operator, a local master manipulator, and a remote slave manipulator, all connected by a communication network. Bilateral teleoperation systems (BTOS) include transmissions in both the forward and backward directions between the master and slave. This paper discusses a class of (BTOS) focusing on the security of the system after modeling the master and slave robots mathematically. The false data injection attack is examined, where the attacker may inject false data into the states that are being exchanged between the master and slave robots. The vulnerability of BTOS, where the attack destabilizes the system, is presented. A deep learning-based detection technique is proposed to detect the presence of false data injection attacks. The deep learning model with convolution neural network structure is trained and tested with considering complex attacks where the attacker has full knowledge of the system and proficiency to emanate and control the target system. The proposed model achieves 96\% validation accuracy, and the efficacy of the proposed deep learning detector is demonstrated and tested into the BTOS.]]>
<![CDATA[Multi-objective Fractional Order PID Controller Optimization for Kid's Rehabilitation Exoskeleton ]]>
<![CDATA[Intissar Zaway]]>;
<![CDATA[Rim Jallouli-Khlif]]>;
<![CDATA[Boutheina Maaleja]]>;
<![CDATA[Hanene Medhaffar]]>;
<![CDATA[Nabil Derbela]]>
<![CDATA[ International Journal of Robotics and Control Systems Vol 3, No 1 (2023) ]]>
Publisher : <![CDATA[Association for Scientific Computing Electronics and Engineering (ASCEE) ]]>
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DOI: 10.31763/ijrcs.v3i1.840
<![CDATA[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.]]>
<![CDATA[Combination of Genetic Algorithm and Neural Network to Select Facial Features in Face Recognition Technique ]]>
<![CDATA[Taraneh Kamyab]]>;
<![CDATA[Haitham Daealhaq]]>;
<![CDATA[Ali Mojarrad Ghahfarokhi]]>;
<![CDATA[Fatemehalsadat Beheshtinejad]]>;
<![CDATA[Ehsan Salajegheh]]>
<![CDATA[ International Journal of Robotics and Control Systems Vol 3, No 1 (2023) ]]>
Publisher : <![CDATA[Association for Scientific Computing Electronics and Engineering (ASCEE) ]]>
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DOI: 10.31763/ijrcs.v3i1.849
<![CDATA[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.]]>
<![CDATA[Development of a 3D Printed Biologically Inspired Monoped Self-Balancing Robot ]]>
<![CDATA[Denis Manolescu]]>;
<![CDATA[Emanuele Lindo Secco]]>
<![CDATA[ International Journal of Robotics and Control Systems Vol 3, No 1 (2023) ]]>
Publisher : <![CDATA[Association for Scientific Computing Electronics and Engineering (ASCEE) ]]>
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DOI: 10.31763/ijrcs.v3i1.841
<![CDATA[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.]]>
<![CDATA[Energy Monitoring and Control of Automatic Transfer Switch between Grid and Solar Panel for Home System ]]>
<![CDATA[Joko Slamet Saputro]]>;
<![CDATA[Hari Maghfiroh]]>;
<![CDATA[Feri Adriyanto]]>;
<![CDATA[Muhammad Renaldy Darmawan]]>;
<![CDATA[Muhammad Hamka Ibrahim]]>;
<![CDATA[Subuh Pramono]]>
<![CDATA[ International Journal of Robotics and Control Systems Vol 3, No 1 (2023) ]]>
Publisher : <![CDATA[Association for Scientific Computing Electronics and Engineering (ASCEE) ]]>
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DOI: 10.31763/ijrcs.v3i1.843
<![CDATA[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.]]>
<![CDATA[Adaptive Droop Control Strategy for Load Sharing in Hybrid Micro Grids ]]>
<![CDATA[Mahmoud Zadehbagheri]]>;
<![CDATA[Alfian Ma'arif]]>;
<![CDATA[Mohammad Javad Kiani]]>;
<![CDATA[Ali Asghar Poorat]]>
<![CDATA[ International Journal of Robotics and Control Systems Vol 3, No 1 (2023) ]]>
Publisher : <![CDATA[Association for Scientific Computing Electronics and Engineering (ASCEE) ]]>
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DOI: 10.31763/ijrcs.v3i1.838
<![CDATA[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.]]>
<![CDATA[Neuro-Fuzzy Decision Support System for Optimization of the Indoor Air Quality in Operation Rooms ]]>
<![CDATA[Najmeh Jamali]]>;
<![CDATA[Mohammad Reza Gharib]]>;
<![CDATA[Behzad Omidi Koma]]>
<![CDATA[ International Journal of Robotics and Control Systems Vol 3, No 1 (2023) ]]>
Publisher : <![CDATA[Association for Scientific Computing Electronics and Engineering (ASCEE) ]]>
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DOI: 10.31763/ijrcs.v3i1.854
<![CDATA[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.]]>
<![CDATA[Controlling Pulse-Like Self-Sustained Oscillators Using Analog Circuits and Microcontrollers ]]>
<![CDATA[Ulrich Simo Domguia]]>;
<![CDATA[Raoul Thepi Siewe]]>
<![CDATA[ International Journal of Robotics and Control Systems Vol 3, No 1 (2023) ]]>
Publisher : <![CDATA[Association for Scientific Computing Electronics and Engineering (ASCEE) ]]>
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DOI: 10.31763/ijrcs.v3i1.802
<![CDATA[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.]]>
<![CDATA[Monte Carlo Simulations on 2D LRF Based People Tracking using Interactive Multiple Model Probabilistic Data Association Filter Tracker ]]>
<![CDATA[Zulkarnain Zainudin]]>;
<![CDATA[Sarath Kodagoda]]>
<![CDATA[ International Journal of Robotics and Control Systems Vol 3, No 1 (2023) ]]>
Publisher : <![CDATA[Association for Scientific Computing Electronics and Engineering (ASCEE) ]]>
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DOI: 10.31763/ijrcs.v3i1.896
<![CDATA[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.]]>
