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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 21 Documents
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Search results for , issue "Vol 4, No 1 (2024)" : 21 Documents clear
The Utilization of Fuzzy Logic Controllers in Steering Control Systems for Electric Ambulance Golf Carts Chotikunnan, Rawiphon; Chotikunnan, Phichitphon; Imura, Pariwat; Pititheeraphab, Yutthana; Thongpance, Nuntachai
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.v4i1.1333

Abstract

This study investigates methods to improve steering control for electric ambulance golf carts by conducting a comparative analysis of fuzzy logic controllers. The research assesses four control systems, PD controller, fuzzy PD controller, fuzzy PD+I controller, and PBC and PD+I type fuzzy logic controller, to determine their effectiveness in enhancing steering control. Simulink simulations are employed to evaluate the performance of these controllers under various conditions. Results indicate that the PBC and PD+I type fuzzy logic controller demonstrates superior performance, showing significant reductions in both rise time and settling time with minimal overshoot compared to other controllers. The findings underscore the potential of fuzzy logic controllers in enhancing steering control for electric vehicles. Future research should explore alternative control strategies and assess controller robustness under diverse operating conditions.
Experimental Validation of the Generation of Direct and Quadratic Reference Currents by Combining the Ant Colony Optimization Algorithm and Sliding Mode Control in PMSM using the Process PIL Najem, Adil; Moutabir, Ahmed; Ouchatti, Abderrahmane; Haissouf, Mohammed El
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.v4i1.1286

Abstract

This article aims to enhance the control efficiency of the Permanent Magnet Synchronous Motor (PMSM) by generating optimal reference currents  and using Ant Colony Optimization (ACO), while ensuring a minimal absorbed current condition to reduce energy consumption and optimize PMSM performance. The ACO algorithm is chosen for its ability to find global solutions and robustness in complex environments, while Sliding Mode Control (SMC) provides advantages in terms of robustness against disturbances and the ability to maintain the system in a desired state. The implementation of the processor-in-the-loop (PIL) technique using MATLAB software with code composer and the LAUNCHXL- F28069M board enables the controller to be implemented in real hardware (LAUNCHXL-F28069M) to test the simulation environment (inverter and PMSM). Our results demonstrate the efficiency of ACO compared to the analytical method (AM) in terms of response time and minimizing absorbed current for different load values. Artificial intelligence (AI) has successfully and efficiently addressed the non-linearity between torque and reference currents, thus reducing energy consumption. This has allowed for the optimization of PMSM performance in a straightforward and efficient manner.
Conceptualization and Topology Optimization of Ampheel: An Integration of Rolling Wheel and Turtle-Inspired Mechanism for Amphibious Mobile Robot Mehta, Vishal; Chauhan, Mihir
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.v4i1.1303

Abstract

The primary distinguishing feature of mobile robots is the ability to traverse various environments, setting them apart in the realm of robotics. The mobility of a robot hinges primarily on its locomotion mechanism, which dictates how it moves. The existing unimodal mobile robots are limited to work within the environment for which they are designed for and hence lack a scope to adapt the change in the terrain especially when they put to work in a mixed environment like land and water. Many applications like land and underwater search/rescue, shore infrastructure inspection, coastal area defence and security, offshore energy harvesting, space exploration, etc. demand a mobile robot that can traverse in both terrestrial and aquatic environments with the help of dual or multimodal locomotion mechanism, something like an amphibious animal. Most of the available amphibious robotic solutions have different appendages for both the environment, need human intervention to changeover the mechanism for transition, require different driving system for land and water locomotion and have fragile structures that limit the manoeuvrability. The proposed conceptual design called “Ampheel” is a novel amphibious locomotion mechanism inspired by the biomechanics of freshwater turtles. Ampheel incorporates a rigid wheel, enabling the robot to move on land, integrating soft actuators within it which emulate the turtle's leg-like extensions and enable the aquatic locomotion. Unlike the existing amphibious robots, the Ampheel utilizes the rotational motion of itself as a common driving system for both the environments. This reduces the need of multiple driving systems and also simplifies the control system. Ampheel is designed for safe travelling on land considering the maximum payload of robot as 20 kg including self-weight. Topology optimization of Ampheel is also carried out using ANSYS software for reduction of weight. Additionally, a unique interfacing shaft is designed that transmits the required torque to Ampheel for rotation and also channelise the compressed air to soft pneumatic actuators for inflation during rotation of Ampheel in aquatic setting. The fabricated Ampheel assembly is experimentally checked for failure under the applicable loading condition and found safe.
Design and Development of ANFIS based Controller for Three Phase Grid Connected System Patra, Rahul; Chaudhary, Priyanka; Shah, Owais Ahmad
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.v4i1.1242

Abstract

A proposal is presented for a low voltage (LV) grid integrated single-stage solar photovoltaic (SPV) system, accompanied by a hybrid control methodology aimed at optimizing system performance. To address prevailing challenges, the hybrid method incorporates the Adaptive Neuro-Fuzzy Inference System (ANFIS). Anticipated benefits of this initiative include efficient power distribution, load connectivity facilitated by the system, and operational functionalities such as mode zero voltage regulation and power factor adjustment. These functionalities collectively enhance energy quality by mitigating harmonic components, compensating for reactive power, and ensuring load balance. The proposed control strategy for a photovoltaic (PV) system interfaced with the grid is designed to exhibit rapid response times in both static and dynamic conditions. Comparative analyses were conducted between the output of our method and that of several competing approaches. The MATLAB/Simulink platform is employed for the purpose of demonstrating the developed system. The results show the extent to which the proposed controller works with reactive power compensation and load balancing to minimize network harmonics and maximize power consumption while keeping power factor functions at unity.
Comparison of Convolutional Neural Networks and Support Vector Machines on Medical Data: A Review Furizal, Furizal; Ma'arif, Alfian; Rifaldi, Dianda; Firdaus, Asno Azzawagama
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.v4i1.1375

Abstract

Medical image processing has become an integral part of disease diagnosis, where technological advancements have brought significant changes to this approach. In this review, a comprehensive comparison between Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) in processing medical images is conducted. Automated medical analysis is becoming increasingly important due to issues of subjectivity in manual diagnosis and potential treatment delays. This research aims to compare the performance of Machine Learning (ML) in medical contexts using MRI, CT scan, and X-ray data. The comparison includes the accuracy rates of CNN and SVM algorithms, sourced from various studies conducted between 2018 and 2022. The results of the comparison show that CNN has higher average accuracy in processing MRI and X-ray data, with average values of 98.05% and 97.27%, respectively. On the other hand, SVM exhibits higher average accuracy for CT scan data, reaching 91.78%. However, overall, CNN achieves an average accuracy of 95.58%, while SVM's average accuracy is at 94.72%. These findings indicate that both algorithms perform well in processing medical data with high accuracy. Although based on these average accuracy rates, CNN demonstrates slightly better capabilities than SVM. Further research and development of more complex models are expected to continue improving the effectiveness of both approaches in disease diagnosis and patient care in the future.
A Technological Review on Fast Chargers for Electric Vehicles: Standards, Architectures, Power Converter Topologies, Fast Charging Techniques, Impacts and Future Research Directions Seleem, Mohamed; Atia, Yousry; Abou-Zalam, Belal; Abd-Elhaleem, Sameh
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.v4i1.1255

Abstract

Within a universe in which concerns about petroleum resources depletion and ecological issues are increasing, the technological evolution of electric mobility has quickened to overcome these concerns. Vehicle electrification technology is seen as a promising and viable substitute for prospective transport systems. Electric vehicles (EVs) offer a solution for reducing the reliance on fossil fuels, improving air quality, integrating easily with renewable energies, and enhancing energy efficiency, especially when smart grids have become omnipresent. However, range anxiety and long charging times remain substantial barriers to the extensive embrace of these vehicles, impeding a seamless shift from conventional vehicles to EVs. Reducing EV recharging time is considered a pivotal element in promoting consumer interest and increasing their market appeal. Thus, EV commercial deployment relies heavily on the presence of an adequate fast-charging infrastructure. Fast-charging infrastructure will decrease drivers' wait times for vehicle charging, providing a refueling experience like that of gasoline vehicles. Hence, a significant portion of research efforts have been dedicated to the advancement of fast chargers (FCs) designed to rapidly recharge EV batteries. It is crucial to opt for the appropriate power electronic interfaces for these chargers to avoid possible grid-related issues and improve overall system efficiency. This review is concentrated on presenting pertinent information regarding EV FCs, including various standards, architectures, power converter topologies, compatible battery chemistries, and fast-charging techniques. Finally, the significant impacts of the integration of fast-charging with both the AC grid and traction batteries are presented in detail. 
Induction Motor Performance Improvement using Super Twisting SMC and Twelve Sector DTC Zahraoui, Yassine; Moutchou, Mohamed; Tayane, Souad; Fahassa, Chaymae; Elbadaoui, Sara
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.v4i1.1090

Abstract

Induction motor (IM) direct torque control (DTC) is prone to a number of weaknesses, including uncertainty, external disturbances, and non-linear dynamics. Hysteresis controllers are used in the inner loops of this control method, whereas traditional proportional-integral (PI) controllers are used in the outer loop. A high-performance torque and speed system is consequently needed to assure a stable and reliable command that can tolerate such unsettled effects. This paper treats the design of a robust sensorless twelve-sector DTC of a three-phase IM. The speed controller is conceived based on high-order super-twisting sliding mode control with integral action (iSTSMC). The goal is to decrease the flux, torque, the current ripples that constitute the major conventional DTC drawbacks. The phase current ripples have been effectively reduced from 76.92% to 45.30% with a difference of 31.62%. A robust adaptive flux and speed observer-based fuzzy logic mechanism are inserted to get rid of the mechanical sensor. Satisfactory results have been got through simulations in MATLAB/Simulink under load disturbance. In comparison to a conventional six-sector DTC, the suggested technique has a higher performance and lower distortion rate.
Optimizing Aircraft Pitch Control Systems: A Novel Approach Integrating Artificial Rabbits Optimizer with PID-F Controller Abualigah, Laith; Izci, Davut; Ekinci, Serdar; Zitar, Raed Abu
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.v4i1.1347

Abstract

The precise control of aircraft pitch angles is critical in aviation for maintaining specific attitudes during flight, including straight and level flight, ascents, and descents. Traditional control strategies face challenges due to the non-linear and uncertain dynamics of flight. To address these issues, this study introduces a novel approach employing the artificial rabbits optimizer (ARO) for tuning a PID controller with a filtering mechanism (PID-F) in aircraft pitch control systems. This combination aims to enhance the stability and performance of the aircraft pitch control system by effectively mitigating the kick effect through the incorporation of a filter coefficient in the derivative gain. The study employs a time-domain-based objective function to guide the optimization process. Simulation results validate the stability and consistency of the proposed ARO/PID-F approach. Comparative analysis with various optimization algorithm-based controllers from the literature demonstrates the effectiveness of the proposed technique. Specifically, the ARO/PID-F controller exhibits a rapid response, zero overshoot, minimal settling time, and precise control during critical phases. The obtained results position the proposed methodology as a promising and innovative solution for optimizing aircraft pitch control systems, offering improved performance and reliability.
Prognostic Real Time Analysis of Induction Motor Kumar, C. S. Subash; Ravikrishna, S.; Rajasekar, P.; Venkatesan, Mani
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.v4i1.1252

Abstract

Variable speed induction motors controlled by variable frequency drives are used for a variety of industrial applications. Monitoring and prognostic occurrence of faults in induction motors is vital for reducing the downtime and accidents. The proposed work focuses on failures in induction motors owing to bearing misalignment and insulation failure in the stator that results in abnormal vibration and temperature rise in the motor. This research intends to improve the dependability and safety of industrial operations by identifying faults in their early stages using advanced methods such as vibration analysis and thermal monitoring. This work focuses on fault prognosis in induction motor through vibration data, which is analyzed using Daubechies orthogonal db10 wavelet transformation. The neural network algorithm optimizes the analyzed results to enable real time fault detection. The temperature of the stator is measured to estimate the expected lifetime of the insulator. The real time vibration and temperature data is measured and transferred to prognostic model build in MATLAB using ATMEGA 32 controller and the results are validated for good, allowable and not permissible conditions of motor based on ISO 10816 vibration levels for Class I motors. The improved accuracy and efficiency of real-time fault detection have the potential to reshape maintenance strategies and enhance the overall reliability of variable speed induction motors.
Selection and Evaluation of Robotic Arm based Conveyor Belts (RACBs) Motions: NARMA(L2)-FO(ANFIS)PD-I based Jaya Optimization Algorithm Fadhil Mohammed, Abdullah; Basil, Noorulden; Abdulmaged, Riyam Bassim; Marhoon, Hamzah M.; Ridha, Hussein Mohammed; Ma'arif, Alfian; Suwarno, Iswanto
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.v4i1.1243

Abstract

Scholars worldwide have shown considerable interest in the industrial sector, mainly due to its abundant resources, which have facilitated the adoption of conveyor belt technologies like Robotic Arm-Based Conveyor Belts (RACBs). RACBs rely on four primary movements: (i.e., joint, motor, gear, and sensor), which can have a significant impact on the overall motions and motion estimation. To optimize these operations, an assistive algorithm has been developed to enhance the effectiveness of motion by achieving favorable gains. However, each motion requires specific criteria for Fractional Order Proportional Integral Derivative (FOPID) controller gains, leading to various challenges. These challenges include the existence of multiple evaluation and selection criteria, the significance of these criteria for each motion, the trade-off between criterion performance for each motion, and determining critical values for the criteria. As a result, the evaluation and selection of the Proposed Jaya optimization algorithm for RACB motion control becomes a complex problem. To address these challenges, this study proposes a novel integrated approach for selecting the Jaya optimization algorithm in different RACB motions. The approach incorporates two evaluation methods: the Nonlinear Autoregressive Moving Average with exogenous inputs (NARMA-L2) controller for Neural Network (NN) weighting of the criteria, and the Adaptive Neuro-Fuzzy Inference System (ANFIS) for selecting the Jaya optimization algorithm. The approach consists of three main phases: RACB-based NARMA-L2 Controller Identification and Pre-processing, Development of NARMA-L2 controller-based NARMA(L2)-FO(ANFIS)PD-I, and Evaluation of FOPID criteria based on JOA. The proposed approach is evaluated based on NARMA(L2)-FO(ANFIS)PD-I that given 0.4074, 0.3156, 0.3724, 0.1898 and 0.2135 for K_p_joint, K_i_motor, K_d_sensor, λ_gear, and µ_N respectively, which verifies the soundness of the proposed methodology.

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