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Alfian Ma'arif
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ijrcs@ascee.org
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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 15 Documents
 1   2 
Search results for , issue "Vol 2, No 1 (2022)" : 15 Documents clear
Exploration of Applying Lego NXT and Arduino in Situated Engineering Teaching: A Case Study of a Robotics Contest at King Saud University Haykel Marouani
International Journal of Robotics and Control Systems Vol 2, No 1 (2022)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v2i1.508

Abstract

Saudi students in engineering courses suffer from a lack of science, technology, engineering, and mathematics (STEM) knowledge due to the teaching philosophy and programs offered in secondary and intermediate schools. This weakness naturally impacts their motivation, grades, and their relationship with teachers. In this paper, we introduce a new experimental teaching experience in the Applied Mechanical Department of the Applied Engineering College of King Saud University, based on situated learning theory, which emphasizes that knowledge must be learned in constructed situational context). This experience involved introducing Lego NXT and Arduino to enhance the enthusiasm and interest of students through designing and building robots in agreement with the “Introduction to design” course syllabus. Two experimental challenges were associated with: the line-follower problem and the maze problem. These challenges took the form of an internal completion at the end of each semester. The experience was conducted for two consecutive terms (30 students, the academic year 2019-2020), and the results were compared to those in six previous terms (100 students, academic years from 2016 to 2019). The experimental group demonstrated grades improvement (course mean grade rose from 77.1 to 85.3), the progress of academic achievement, and interest that enables them to actively explore and construct knowledge.
An Autonomous Pesticide Sprayer Robot with a Color-based Vision System Mona Tahmasebi; Mohammad Gohari; Alireza Emami
International Journal of Robotics and Control Systems Vol 2, No 1 (2022)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v2i1.480

Abstract

The usage of robots has been due to cost reduction and increasing operation accuracy. Furthermore, employing robots in farming can decrease human tasks in hard and dangerous duties such as plowing, spraying pesticides, etc. Spraying chemicals is a common operation in agriculture crop protection. This operation is essential, but it can create some problems such as human and environmental damages by overdosing using pesticides. Recently, researchers focused on precision agriculture to make this smart. Sensors are employed to detect leaves of plants on the ground and spray them as much as required. Thus, pesticide dose will be under control. The current paper aims to introduce a wheeled robot that is developed to detect plants by color sensor and spray them. This robot can move between planting rows and detect weeds based on the leave color. A microcontroller-based board was used as the main controller, which sends spray commands to the sprayer nozzle. Outdoor and indoor tests were carried out to study the accuracy of this system. Results of experiments showed that this robot could work with acceptable accuracy in identifying weeds in the field. Thus, this robot can be commercialized for applying in the field to spray pesticides.
Pinning Decision in Interconnected Systems with Communication Disruptions under Multi-Agent Distributed Control Topology Samson S. Yu; Tat Kei Chau
International Journal of Robotics and Control Systems Vol 2, No 1 (2022)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v2i1.514

Abstract

In this study, we propose a decision-making strategy for pinning-based distributed multi-agent (PDMA) automatic generation control (AGC) in islanded microgrids against stochastic communication disruptions. The target microgrid is construed as a cyber-physical system, wherein the physical microgrid is modeled as an inverter-interfaced autonomous grid with detailed system dynamic formulation, and the communication network topology is regarded as a cyber-system independent of its physical connection. The primal goal of the proposed method is to decide the minimum number of generators to be pinned and their identities amongst all distributed generators (DGs). The pinning-decisions are made based on complex network theories using the genetic algorithm (GA), for the purpose of synchronizing and regulating the frequencies and voltages of all generator bus-bars in a PDMA control structure, i.e., without resorting to a central AGC agent. Thereafter, the mapping of cyber-system topology and the pinning decision is constructed using deep-learning (DL) technique, so that the pinning-decision can be made nearly instantly upon detecting a new cyber-system topology after stochastic communication disruptions. The proposed decision-making approach is verified using a 10-generator, 38-bus microgrid through time-domain simulation for transient stability analysis. Simulations show that the proposed pinning decision making method can achieve robust frequency control with minimum number of active communication channels.
Methodologies and Applications of Artificial Intelligence in Systems Engineering Awatef K. Ali; MagdiSadek Mostafa Mahmoud
International Journal of Robotics and Control Systems Vol 2, No 1 (2022)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v2i1.532

Abstract

This paper presents an overview of the methodologies and applications of artificially intelligent systems (AIS) in different engineering disciplines with the objective of unifying the basic information and outlining the main features. These are knowledge-based systems (KBS), artificial neural networks (ANN), and fuzzy logic and systems (FLS). To illustrate the concepts, merits, and demerits, a typical application is given from each methodology. The relationship between ANN and FLS is emphasized. Two recent developments are finally presented: one is intelligent and autonomous systems (IAS) with particular emphasis on intelligent vehicle and highway systems, and the other is the very large scale integration (VLSI) systems design, verification, and testing.
Design and Simulation of Synchronous Buck Converter in Comparison with Regular Buck Converter Hossein Zomorodi; Erfan Nazari
International Journal of Robotics and Control Systems Vol 2, No 1 (2022)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v2i1.538

Abstract

In a variety of low-power applications, a step-down dc-dc converter is used to reduce the voltage from a higher level. The two types of dc-dc converters are a regular buck and synchronous buck. The synchronous buck utilizes two switches and one diode, whereas the regular buck uses one switch and one diode. Many converters rely on the power components' switching qualities to work. A second MOSFET is required due to the diode's higher conduction losses. Because of the diode's conduction losses, the converter's efficiency may be reduced. The use of a synchronous buck converter improves efficiency by reducing diode losses. The main goal of this study is to compare and contrast these two low-power step-down converters. The simulation in this work was performed using the LTSPICE program.
Performance Estimation and Control Analysis of AC-DC/DC-DC Hybrid Multi-Port Intelligent Controllers Based Power Flow Optimizing Using STEM Strategy and RPFC Technique C. Nagarajan; B. Tharani; S. Saravanan; R. Prakash
International Journal of Robotics and Control Systems Vol 2, No 1 (2022)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v2i1.562

Abstract

The control system will measure the renewable energy generation power, and if the generated power is equal to the grid power, the generation sources are directly connected to the load system. When the power generation is not at sustaining level, the controller will optimize the source using the DC-DC converter and Battery based Energy management system. The operation of the battery system depends upon the power generation availability of renewable energy resources. During the high power RES, the battery is charging condition. When the RES is low power means the battery is in discharged condition. Also, the fuel cell-based energy compensation will take place when the battery power is low. The Energy router will monitor all the above generation plants based on the threshold values of each power plant, substantial Transformative Energy Management (STEM) Strategy and Resilient Power Flow Control (RPFC) controller takes necessary action like which power plant is connected to the grid power system. The simulation is performed on Mat lab / Simulink simulation platforms, and the results show the effectiveness and reliability of the control strategy for micro-grid interconnection and flexible energy flow correspondence.
Adaptive Neural Networks Based Robust Output Feedback Controllers for Nonlinear Systems Ahmed Jaber Abougarair; Mohamed K. I. Aburakhis; Mohamed M. Edardar
International Journal of Robotics and Control Systems Vol 2, No 1 (2022)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v2i1.523

Abstract

The performance of the nonlinear control system that is subjected to uncertainty, can be enhanced by implementing an adaptive approach by using the robust output-feedback control and the artificial intelligence neural network. This paper seeks to utilize output feedback control for nonlinear system using artificial intelligence employing neural network. The Two Wheel Mobile Robot (TWMR) is treated as a multi-body dynamic system. The nonlinear swing-up problem is handled by designing an adaptive neural network, which is trained using a modified conventional controller called Linear Quadratic Optimal State Estimator with Integral Control (LQOSEIC). In this paper, the nonlinear system TWMR is stabilized utilizing a robust output feedback control called LQOSEIC. This controller allows a linearized model to emulate a model reference for the original nonlinear system. However, it works for a limited range of operations and will fail if the plant characteristics are unknown or uncertain. An adaptive neural network is used to overcome this problem. The adaptive neural controller is trained offline using LQOSEIC to obtain the initial weights of neurons for the network's hidden layers. After finishing the training, the LQOSEIC will be replaced by the adaptive neural controller. The main advantage of a neuro-controller is its ability to update the weights online depending on the error signal. If there are any disturbances or uncertainties that arises within the concerned nonlinear system, the neuro-controller will be able to handle it because of online learning that compensates for the effect of unpredictable conditions. The proposed adaptive neural network improves control performance and ensures the robust stability of the closed-loop control system. Finally, numerical simulations are used to demonstrate the efficacy of the proposed controllers.
Design a New Multiport DC-DC Converter to Charge an Electric Car Amin Abedini Rizi; Ali Rezaei; Mohammadreza Ghorbani Rizi; Mohammadmahdi Aliakbari Rizi
International Journal of Robotics and Control Systems Vol 2, No 1 (2022)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v2i1.566

Abstract

Due to the lack of oil and gas, electric cars have been in high demand in recent years. There are three kinds of electric vehicles, including Hybrid Electric Vehicles (HEV), Battery Electric Vehicle (BEV), and Plug-in Hybrid Electric Vehicle (PHEV).  There is no charging portion for the batteries in the HEV where the batteries are not connected to the power grid, but BEV and PHEV can be charged by a power outlet, and the number of batteries is increased. In order to charge the battery of the Electric Vehicles (EVs), there are two ways, including the power grid and renewable energies. There are already quite a few outages in many countries, and using a power grid for charging the batteries is not suitable. Therefore, the only choice is renewable energy sources such as photovoltaic (PV), fuel-cell (FC), and so on. Furthermore, to use the DC voltage of the renewable sources, two conventional DC-DC converters are required to deliver the energy of the sources to the bank of batteries. To feed the batteries, this paper proposes a two-input one output topology that contains PV, FC, and other components. Simulation results demonstrate that the presented system is improving the system because it is able to feed the batteries with low power losses and low ripples.
Performance Enhancement of a Hybrid Renewable Energy System Accompanied with Energy Storage Unit Using Effective Control System Mahmoud A. Mossa; Olfa Gam; Nicola Bianchi
International Journal of Robotics and Control Systems Vol 2, No 1 (2022)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v2i1.599

Abstract

The current study aims to present a detailed analysis of a hybrid renewable energy system used for standalone operation. The hybrid system consists of a wind-driven synchronous generator, a photovoltaic solar system, and a battery storage system. The modeling of each system component is presented and described in detail. To achieve optimal energy exploitation, the maximum power point tracking algorithm is adopted. The management of synchronous generator operation is achieved through controlling the machine-side converter using a newly formulated predictive control scheme. To visualize the advantages of the proposed control algorithm, its performance is compared with the other two traditional predictive control approaches, mainly the model predictive direct power control and model predictive direct torque control systems. An effective control scheme is also adopted to manage the power storage in the battery using a bi-directional DC/DC converter. To maintain a balanced power flow between the system units, an energy management strategy is presented. Extensive tests are carried out to evaluate the performance of the hybrid system considering variable wind speed, variable sun irradiation, and variable load profiles. The obtained results for the synchronous generator performance visualize the validity and superiority of the proposed control scheme over the other two classic controllers. The results are also validating the effectiveness of the battery storage control system and confirming the validity of the constructed energy management strategy in achieving the energy balance between the system units.
Invariant Ellipsoids Method for Chaos Synchronization in a Class of Chaotic Systems Giuseppe Fedele
International Journal of Robotics and Control Systems Vol 2, No 1 (2022)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v2i1.533

Abstract

This paper presents an invariant sets approach for chaos synchronization in a class of master-slave chaotic systems affected by bounded perturbations. The method provides the optimal state-feedback gain in terms of the minimal ellipsoid that guarantees minimum synchronization error bound. The problem of finding the optimal invariant ellipsoid is formulated in terms of a semi-definite programming problem that can be easily solved using various simulation and calculus tools. The effectiveness of the proposed criterion is illustrated by numerical simulations on the synchronization of Chua's systems.

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