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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 16 Documents
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Search results for , issue "Vol 3, No 2 (2023)" : 16 Documents clear
Ball and Beam Control: Evaluating Type-1 and Interval Type-2 Fuzzy Techniques with Root Locus Optimization Rawiphon Chotikunnan; Phichitphon Chotikunnan; Alfian Ma'arif; Nuntachai Thongpance; Yutthana Pititheeraphab; Anuchart Srisiriwat
International Journal of Robotics and Control Systems Vol 3, No 2 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i2.997

Abstract

This study evaluates the performance of three control systems, namely the root locus method, type-1 Mamdani fuzzy logic system (FLS), and interval type-2 Mamdani FLS, in noise-free and noisy ball and beam systems. The main contribution of this study is enabling improved design and implementation of control systems in real-world applications by offering a comprehensive understanding of each control system's performance. The methodology involves conducting four tests focusing on various input types, including a 0.8-meter step input and sine wave function, and assessing the presence of noise in the system. The performance of each control system is analyzed using parameters such as rise time, setting time, and percentage overshoot, with the interval type-2 Mamdani FLS further examined by varying footprint of uncertainty values. Results from noise-free tests reveal that the root locus method has shorter rise and setting times, but a higher percentage overshoot compared to the type-1 Mamdani FLS and type-2 Mamdani FLS. In noisy environments, the type-2 Mamdani FLS with varying Footprint of Uncertainty values outperforms the type-1 Mamdani FLS with reduced rise time, setting time, and percentage overshoot. The root locus method shows a significantly higher percentage overshoot in noisy conditions compared to the other two control systems. In conclusion, the type-2 Mamdani FLS control system demonstrates superior capability under changing conditions compared to the type-1 Mamdani FLS, with its performance varying based on footprint of uncertainty values. This study highlights the importance of selecting the appropriate control system depending on specific needs and environmental factors.
Design and Application of PLC-based Speed Control for DC Motor Using PID with Identification System and MATLAB Tuner Dodi Saputra; Alfian Ma'arif; Hari Maghfiroh; Phichitphon Chotikunnan; Safinta Nurindra Rahmadhia
International Journal of Robotics and Control Systems Vol 3, No 2 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i2.775

Abstract

Industries use numerous drives and actuators, including DC motors. Due to the wide-ranged and adjustable speed, DC motor is widely used in many industries. However, the DC motor is prone to external disturbance and parameter changes, causing its speed to be unstable. Thus, a DC motor requires an appropriate controller design to obtain a fast and stable speed with a small steady-state error. In this study, a controller was designed based on the PID control method, with the controller gains tuned by trial-and-error and MATLAB Tuner with an identification system. The proposed controller design was implemented using PLC OMRON CP1E NA20DRA in the hardware implementation. Each tuning method was repeated five times so that the system performances could be compared and improved. Based on hardware implementation results, the trial-error method gave acceptable results but had steady-state errors. On the other hand, the use of MATLAB Tuner provided fast system responses with no steady-state error but still had oscillations with high overshoot during the transition. Therefore, the PID controller gains acquired from MATLAB Tuner must be tuned finely to get better system responses.
Proportional Derivative – Type Iterative Learning Algorithm for a Motion Control System Duong Thi Thanh Huyen; Vu Van Hoc; Nguyen Thi Thanh Hoa
International Journal of Robotics and Control Systems Vol 3, No 2 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i2.975

Abstract

In this paper, Iterative Learning Control (ILC) combined with a Proportional Derivative (PD) regulator is proposed to deal with the problem of designing a control signal for motion control systems. The main idea in iterative learning control is to gradually improve the performance of the system by exploiting data from the previous iterations. The learning control algorithm can obtain a better tracking control performance for the next run and hence outperforms conventional control approaches such as Proportional Integral Derivative (PID) controller and feedforward control. The main area of application for ILC is control of industrial robots and CNC machine tool, printing, and other industrial applications. The learning algorithms can also be used in combination with other control techniques. For example, learning feedforward control is designed in the first iteration. Then iterative learning control is applied to improve performance in the subsequent iterations. In addition, the conventional feedback regulator is designed in combination with iterative control to deal with uncertainty. Simulation results demonstrate the potential benefits, sensitivity and robustness of the proposed method.
Dynamic Model of a Robotic Manipulator with One Degree of Freedom with Friction Component Jose A. G. Luz Junior; Jose M. Balthazar; Mauricio A. Ribeiro; Frederic C. Janzen; Angelo Marcelo Tusset
International Journal of Robotics and Control Systems Vol 3, No 2 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i2.984

Abstract

This research aims to develop a dynamic model of a robotic manipulator with one degree of freedom by incorporating the LuGre friction model. The study combines a mathematical model with experimental data analysis, using the Stribeck curve and Non-linear Least Square method for Parameter Identification. The purpose of the study is to improve the accuracy of the model and enhance the performance of robotic manipulators. The LuGre model is chosen for its ability to capture the nonlinear behavior of friction, which is a significant source of error in robot control systems. The effectiveness of the proposed representation is evaluated by comparing the simulation results of the dynamic model with experimental data obtained from a prototype. The results indicate that the model accurately captures the nonlinear behavior of friction, and the proposed approach can be used to develop more accurate models for control purposes.
Forward and Inverse Kinematics Solution of A 3-DOF Articulated Robotic Manipulator Using Artificial Neural Network Abdel-Nasser Sharkawy; Shawkat Sabah Khairullah
International Journal of Robotics and Control Systems Vol 3, No 2 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i2.1017

Abstract

In this research paper, the multilayer feedforward neural network (MLFFNN) is architected and described for solving the forward and inverse kinematics of the 3-DOF articulated robot. When designing the MLFFNN network for forward kinematics, the joints' variables are used as inputs to the network, and the positions and orientations of the robot end-effector are used as outputs. In the case of inverse kinematics, the MLFFNN network is designed using only the positions of the robot end-effector as the inputs, whereas the joints’ variables are the outputs. For both cases, the training of the proposed multilayer network is accomplished by Levenberg Marquardt (LM) method. A sinusoidal type of motion using variable frequencies is commanded to the three joints of the articulated manipulator, and then the data is collected for the training, testing, and validation processes. The experimental simulation results demonstrate that the proposed artificial neural network that is inspired by biological processes is trained very effectively, as indicated by the calculated mean squared error (MSE), which is approximately equal to zero. The resulted in smallest MSE in the case of the forward kinematics is 4.592×10^(-8) in the case of the inverse kinematics, is 9.071×10^(-7). This proves that the proposed MLFFNN artificial network is highly reliable and robust in minimizing error. The proposed method is applied to a 3-DOF manipulator and could be used in more complex types of robots like 6-DOF or 7-DOF robots.
Indonesian Waste Database: Smart Mechatronics System Haris Imam Karim Fathurrahman; Ahmad Azhari; Tole Sutikno; Li-yi Chin; Prasetya Murdaka Putra; Isro Dwian Yunandha; Gralo Yopa Rahmat Pratama; Beni Purnomo
International Journal of Robotics and Control Systems Vol 3, No 2 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i2.999

Abstract

Waste management is an essential component of urban management. As a waste solution, waste management is critical. The goal of this research is to develop a waste management database that is coupled with a mechatronic robot system. Compiling and gathering data on the sorts of garbage found in Indonesia is the starting point for this research. Indonesian waste is classified into six groups: cardboard, paper, metal, plastic, medical, and organic. The total images of the six groups are estimated at 1880 pictures. According to this picture database, Artificial Intelligence (AI) training was used to create the classification system. In the final AI process, the test method was performed using DenseNet121, DenseNet169, and DenseNet201. Testing using artificial intelligence DenseNet201 across 40 epochs yields the best 92,7% accuracy rate. Simultaneously with Artificial Intelligence testing, a mechatronic system is created as a direct implementation of the Artificial Intelligence output model. A four-servo arm robot with dc motor wheel mobility is included in the mechatronic system. According to these findings, the Indonesian waste database can be categorized correctly using Artificial Intelligence and the mechatronics system. This higher accuracy of the artificial intelligence model may be used to create a waste-sorting robot prototype.

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