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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 361 Documents
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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.
Pulse Width Modulation Analysis of Five-Level Inverter- Fed Permanent Magnet Synchronous Motors for Electric Vehicle Applications Omokhafe J. Tola; Edwin A. Umoh; Enesi A. Yahaya
International Journal of Robotics and Control Systems Vol 1, No 4 (2021)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v1i4.483

Abstract

In recent times, intense research has been focused on the performance enhancement of permanent magnet synchronous motors (PMSM) for electric vehicle (EV) applications to reduce their torque and current ripples. Permanent magnet synchronous motors are widely used in electric vehicle systems due to their high efficiency and high torque density. To have a good dynamic and transient response, an appropriate inverter topology is required. In this paper, a five-level inverter fed PMSM for electric vehicle applications, realized via co-simulation in an electromagnetic suite environment with a reduced stator winding current of PMSM via the use of in-phase disposition (PD) pulse width modulation (PWM) techniques as the control strategy is presented. The proposed topology minimizes the total harmonic distortion (THD) in the inverter circuit and the motor fed and also improves the torque ripples and the steady-state flux when compared to conventional PWM techniques. A good dynamic response was achieved with less than 10A stator winding current, zero percent overshoot, and 0.02 second settling time synchronization. Thus, the stator currents are relatively low when compared to the conventional PWM. This topology contribution to the open problem of evolving strategies that can enhance the performance of electric drive systems used in unmanned aerial vehicles (UAV), mechatronics, and robotic systems.
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.
Stabilizing of Inverted Pendulum System Using Robust Sliding Mode Control Magdi S. Mahmoud; Radhwan A. A. Saleh; Alfian Ma’arif
International Journal of Robotics and Control Systems Vol 2, No 2 (2022)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v2i2.594

Abstract

The Inverted Pendulum is a highly nonlinear, unstable, and fast dynamic system. These characteristics make it a popular benchmark for building and testing novel controllers. Therefore, in this study, a sliding mode controller is proposed and tested on the inverted pendulum system. According to the results of the simulation experiments with a sine signal as a reference, the proposed controller can stabilize the system well and has so fast response. Moreover, we have tuned the parameters of the proposed sliding mode controller in order to eliminate the chattering effect, the overshoot, and the steadystate error.
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.
Spiking PID Control Applied in the Van de Vusse Reaction Carlos Antonio Márquez-Vera; Zaineb Yakoub; Marco Antonio Márquez Vera; Alfian Ma'arif
International Journal of Robotics and Control Systems Vol 1, No 4 (2021)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v1i4.490

Abstract

Artificial neural networks (ANN) can approximate signals and give interesting results in pattern recognition; some works use neural networks for control applications. However, biological neurons do not generate similar signals to the obtained by ANN.  The spiking neurons are an interesting topic since they simulate the real behavior depicted by biological neurons. This paper employed a spiking neuron to compute a PID control, which is further applied to the Van de Vusse reaction. This reaction, as the inverse pendulum, is a benchmark used to work with systems that has inverse response producing the output to undershoot. One problem is how to code information that the neuron can interpret and decode the peak generated by the neuron to interpret the neuron's behavior. In this work, a spiking neuron is used to compute a PID control by coding in time the peaks generated by the neuron. The neuron has as synaptic weights the PID gains, and the peak observed in the axon is the coded control signal. The neuron adaptation tries to obtain the necessary weights to generate the peak instant necessary to control the chemical reaction. The simulation results show the possibility of using this kind of neuron for control issues and the possibility of using a spiking neural network to overcome the undershoot obtained due to the inverse response of the chemical reaction.
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.
An Intelligent Color Image Recognition and Mobile Control System for Robotic Arm Albert Wen Long Yao; H. C. Chen
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.v2i1.557

Abstract

The aim of this study is to develop intelligent color recognition, mobile control, and monitoring system for a pick-and-place robotic arm for manufacturing systems. The demand for smart manufacturing factories with real-time control of fabricating processes and traceability of production information is increasing urgently. Generally speaking, a smart manufacturing facility is usually composed of sensing, computing, control, and communication technologies together. In this study, the three-tier architecture of the Internet of things (IoT) was adopted as a guideline to design mobile devices to control and monitor a color image recognition and alarm monitoring system by using Raspberry Pi and a web page database. The practical results and contributions of this study are as follows:  With integrating the techniques of advanced BR PLC, mobile devices and APP, color image recognition, Raspberry Pi microcomputer, and MySQL database technologies together, (1) the mobile control and monitoring system is able to supervise a real-time manufacturing plant anywhere and anytime with mobile devices easily; (2) the color identification system can identify and classify different color work-piece precisely, and the identification results are recorded for remote database platform; (3) the collected data are analyzed and displayed on mobile devices through the web database for field operators and engineers promptly.  It provides a very successful practical paradigm to promote conventional factories to meet industry 4.0.

Page 5 of 37 | Total Record : 361

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