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All Journal Bulletin of Electrical Engineering and Informatics Emerging Science Journal Buletin Ilmiah Sarjana Teknik Elektro Journal of Robotics and Control (JRC) Journal of Soft Computing Exploration International Journal of Robotics and Control Systems Signal and Image Processing Letters
Suwarno, Iswanto
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering

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Optimizing Solar Energy Production in Partially Shaded PV Systems with PSO-INC Hybrid Control Abboud, Sarah; Loulijat, Azeddine; Boulal, Abdellah; Semma, El Alami; Habachi, Rachid; Chojaa, Hamid; Ma'arif, Alfian; Suwarno, Iswanto; Mossa, Mahmoud A.
Journal of Robotics and Control (JRC) Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i2.20896

Abstract

Partial shading, from obstacles such as buildings or trees, is a major challenge for photovoltaic systems, causing unpredictable fluctuations in solar energy production and underlining the need for advanced energy management strategies. In this paper, we propose an innovative approach that combines hybrid metaheuristic optimization with maximum power point tracking control (MPPT), using particle swarm optimization (PSO) in conjunction with the incremental conductance (IC) algorithm. We compare the proposed method with the conventional Perturb and Observation (PO) algorithm. The choice of PO as a comparison method is due to its simplicity, its familiarity with the scientific literature, its low cost of implementation. The main objective of swarm optimization combined with the IC algorithm lies in its ability to overcome the challenges posed by partial shading, ensuring accurate and efficient tracking of the point of maximum power, thanks to dynamic adaptation to variations in solar irradiation, thus enhancing the performance and resilience of the photovoltaic system. This approach  is of crucial importance, offering considerable potential for solving the complex challenges associated with partial shading. Our results show that this hybrid MPPT algorithm offers superior tracking efficiency 98% , faster convergence 500ms , better stability and increased robustness compared to traditional MPPT approaches. The system is composed of a PV and a boost converter that connects the input to the resistive load. The algorithms were implemented with MATLAB/Simulink as the simulation tool. These results not only reinforce the viability of sustainable energy solutions, but also open the way for the development of more sustainable energy solutions.The perspectives of this work are oriented towards a practical and extended integration of the proposed hybrid approach in real photovoltaic systems, with a particular emphasis on experimental validation.
Design of Multivariate PID Controller for Power Networks Using GEA and PSO Zadehbagheri, Mahmoud; Ma'arif, Alfian; Ildarabadi, Rahim; Ansarifard, Mehdi; Suwarno, Iswanto
Journal of Robotics and Control (JRC) Vol 4, No 1 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i1.15682

Abstract

The issue of proper modeling and control for industrial systems is one of the challenging issues in the industry. In addition, in recent years, PID controller design for linear systems has been widely considered. The topic discussed in some of the articles is mostly speed control in the field of electric machines, where various algorithms have been used to optimize the considered controller, and always one of the most important challenges in this field is designing a controller with a high degree of freedom. In these researches, the focus is more on searching for an algorithm with more optimal results than others in order to estimate the parameters in a more appropriate way. There are many techniques for designing a PID controller. Among these methods, meta-innovative methods have been widely studied. In addition, the effectiveness of these methods in controlling systems has been proven. In this paper, a new method for grid control is discussed. In this method, the PID controller is used to control the power systems, which can be controlled more effectively, so that this controller has four parameters, and to determine these parameters, the optimization method and evolutionary algorithms of genetics (EGA) and PSO are used.  One of the most important advantages of these algorithms is their high speed and accuracy. In this article, these algorithms have been tested on a single-machine system, so that the single-machine system model is presented first, then the PID controller components will be examined. In the following, according to the transformation function matrix and the relative gain matrix, suitable inputs for each of the outputs are determined. At the end, an algorithm for designing PID controller for multivariable MIMO systems is presented. To show the effectiveness of the proposed controller, a simulation was performed in the MATLAB environment and the results of the simulations show the effectiveness of the proposed controller.
Analysis and Performance Comparison of Fuzzy Inference Systems in Handling Uncertainty: A Review Furizal, Furizal; Ma'arif, Alfian; Wijaya, Setiawan Ardi; Murni, Murni; Suwarno, Iswanto
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.22123

Abstract

Uncertainty is an inevitable characteristic in human life and systems, posing challenges in decision-making and data analysis. Fuzzy theory emerges to address this uncertainty by describing variables with vague or uncertain values, one of which is the Fuzzy Inference System (FIS). This research analyzes and compares the performance of FIS from previous studies as a solution to manage uncertainty. FIS allows for flexible and responsive representations of truth levels using human-like linguistic rules. Common FIS methods include FIS-M, FIS-T, and FIS-S, each with different inference and defuzzification approaches. The findings of this research review, referencing previous studies, indicate that the application of FIS in various contexts such as prediction, medical diagnosis, and financial decision-making, yields very high accuracy levels up to 99%. However, accuracy comparisons show variations, with FIS-M tending to achieve more stable accuracy based on the referenced studies. The accuracy difference among FIS-M studies is not significantly different, only around 7.55%. Meanwhile, FIS-S has a wider accuracy range, from 81.48% to 99% (17.52%). FIS-S performs best if it can determine influencing factors well, such as determining constant values in its fuzzy rules. Additionally, the performance comparison of FIS can also be influenced by other factors such as data complexity, variables, domain, membership functions (curves), fuzzy rules, and defuzzification methods used in the study. Therefore, it is important to consider these factors and select the most suitable FIS method to manage uncertainty in the given situation.
Deployment of STATCOM with Fuzzy Logic Control for Improving the Performance of Power System under Different Faults Conditions Fawzy, Ibram Y.; Mossa, Mahmoud A.; Elsawy, Ahmed M.; Suwarno, Iswanto; Diab, Ahmed A. Zaki
Journal of Robotics and Control (JRC) Vol 5, No 3 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i3.21558

Abstract

This paper purposes to demonstrate the effectiveness of fuzzy logic controller (FLC) over proportional integral (PI) controller for reducing the fault current and maintaining the voltage profile at different faults conditions using Static Synchronous Compensator (STATCOM) which is considered an effective FACTS (Flexible Alternating Current Transmission System) device. The study evaluates the performance of a power system equipped with STATCOM which is connected in shunt with bus B1 under various faults conditions, including single-phase and three-phase faults. The performance of the STATCOM is evaluated by using two different controllers: PI controllers and FLCs. A comparative analysis is done between the performances of the two different controllers using Matlab/Simulink software package. The results obtained conclude that the presented system gives better performance with STATCOM as compared to not using it under several faults conditions besides, the STATCOM gives better response with FLC as compared to PI controller. It is demonstrated that STATCOM with FLC can reduce the positive sequence fault current at bus B1 ‎to 96.49% of its value without ‎using STATCOM under line to ground fault and 98.17% under three line to ‎ground fault whereas STATCOM with PI controller can reduce it ‎to (99.57, 99.05%), respectively. Also, the bus voltage B1 is improved to 102.19% by using STATCOM with fuzzy controller under line to ground fault and 101.86% under three line to ‎ground fault whereas STATCOM with PI controller can improve it ‎to (100.21, 100.93%), respectively.
Obstacle Avoidance Based on Stereo Vision Navigation System for Omni-directional Robot Umam, Faikul; Fuad, Muhammad; Suwarno, Iswanto; Ma'arif, Alfian; Caesarendra, Wahyu
Journal of Robotics and Control (JRC) Vol 4, No 2 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i2.17977

Abstract

This paper addresses the problem of obstacle avoidance in mobile robot navigation systems. The navigation system is considered very important because the robot must be able to be controlled from its initial position to its destination without experiencing a collision. The robot must be able to avoid obstacles and arrive at its destination. Several previous studies have focused more on predetermined stationary obstacles. This has resulted in research results being difficult to apply in real environmental conditions, whereas in real conditions, obstacles can be stationary or moving caused by changes in the walking environment. The objective of this study is to address the robot’s navigation behaviors to avoid obstacles. In dealing with complex problems as previously described, a control system is designed using Neuro-Fuzzy so that the robot can avoid obstacles when the robot moves toward the destination. This paper uses ANFIS for obstacle avoidance control. The learning model used is offline learning. Mapping the input and output data is used in the initial step. Then the data is trained to produce a very small error. To support the movement of the robot so that it is more flexible and smoother in avoiding obstacles and can identify objects in real-time, a three wheels omnidirectional robot is used equipped with a stereo vision sensor. The contribution is to advance state of the art in obstacle avoidance for robot navigation systems by exploiting ANFIS with target-and-obstacles detection based on stereo vision sensors. This study tested the proposed control method by using 15 experiments with different obstacle setup positions. These scenarios were chosen to test the ability to avoid moving obstacles that may come from the front, the right, or the left of the robot. The robot moved to the left or right of the obstacles depending on the given Vy speed. After several tests with different obstacle positions, the robot managed to avoid the obstacle when the obstacle distance ranged from 173 – 150 cm with an average speed of Vy 274 mm/s. In the process of avoiding obstacles, the robot still calculates the direction in which the robot is facing the target until the target angle is 0.
Current Trends in Incubator Control for Premature Infants with Artificial Intelligence Based on Fuzzy Logic Control: Systematic Literature Review Maharani Raharja, Nia; Suwarno, Iswanto; Sugiyarta, Sugiyarta
Journal of Robotics and Control (JRC) Vol 3, No 6 (2022): November
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v3i6.13341

Abstract

Incubator Control for Premature Babies has benefited greatly from the development of creative methods and uses of artificial intelligence. Due to the immaturity of the epidermis, premature infants lose fluid and heat early in life, which causes hyperosmolar dehydration and hypothermia. Water loss through the epidermis. Therefore, in order to maintain the baby's healthy temperature, an incubator is required. As a result, it is anticipated that the baby will maintain the same temperature as in the mother's womb. A temperature regulation system with good measurement and regulation quality is necessary due to the necessity of Incubator Control for Premature Infants with Artificial Intelligence Based on Fuzzy Logic in treating premature infants. The purpose of this research is to assess current trends in artificial intelligence-based fuzzy logic incubator control for preterm infants. The Preferred Reporting Items for Systematic Review (PRISMA) were used in this study's systematic literature review. 188 suitable articles that fit the inclusion requirements were found after the articles were screened and chosen. The outcomes demonstrated that the Incubator Control for Premature Infants offered the best environment for newborns with growth or disease-related issues (premature babies). An incubator is a sealed space free of dust and bacteria with the ability to regulate temperature, humidity, and oxygen to maintain a stable environment.
Stock Price Forecasting with Multivariate Time Series Long Short-Term Memory: A Deep Learning Approach Furizal, Furizal; Ritonga, Asdelina; Ma’arif, Alfian; Suwarno, Iswanto
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22460

Abstract

Stocks with their inherent complexity and dynamic nature influenced by a multitude of external and internal factors, play a crucial role in investment analysis and trend prediction. As financial instruments representing ownership in a company, stocks not only reflect the company's performance but are also affected by external factors such as economic conditions, political climates, and social changes. In a rapidly changing environment, investors and analysts continuously develop models and algorithms to aid in making timely and effective investment decisions. This study applies a Sequential model to predict stock data using a LSTM neural network. The model consists of a single hidden LSTM layer with 200 units. The LSTM layer, the core element of this model, enables it to capture temporal patterns and long-term relationships within the data. The training and testing data were divided into 80% for training and 20% for testing. The Adam optimizer was chosen to optimize the model's learning process, with a learning rate of 0.001. Dropout techniques were applied to reduce overfitting, with a dropout rate of 0.4, along with batch normalization and ReLU activation functions to enhance model performance. Additionally, callback mechanisms, including ReduceLROnPlateau and EarlyStopping, were used to optimize the training process and prevent overfitting. The model was evaluated using MAE and MSE metrics on training, testing, and future prediction data. The results indicate that the model achieved high accuracy, with an MAE of 0.0142 on the test data. However, future predictions showed higher MAE values, suggesting room for improvement in long-term forecasting. The model's ability to accurately predict future stock closing prices can assist investors in making informed investment decisions.
Capability of Hybrid Long Short-Term Memory in Stock Price Prediction: A Comprehensive Literature Review Furizal, Furizal; Ma'arif, Alfian; Firdaus, Asno Azzawagama; Suwarno, Iswanto
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1489

Abstract

Stocks are financial instruments representing ownership in a company. They provide holders with rights to a portion of the company's assets and earnings. The stock market serves as a means for companies to raise capital. By selling shares to the public, companies can obtain funds needed for expansion, research and development, as well as various other investments. Though significant, predicting stock prices poses a challenge for investors due to their unpredictable nature. Stock price prediction is also an intriguing topic in finance and economics due to its potential for significant financial gains. However, manually predicting stock prices is complex and requires in-depth analysis of various factors influencing stock price movements. Moreover, human limitations in processing and interpreting information quickly can lead to prediction errors, while psychological factors such as bias and emotion can also affect investment decisions, reducing prediction objectivity and accuracy. Therefore, machine processing methods become an alternative to expedite and reduce errors in processing large amounts of data. This study attempts to review one of the commonly used prediction algorithms in time series forecasting, namely hybrid LSTM. This approach combines the LSTM model with other methods such as optimization algorithms, statistical techniques, or feature processing to enhance the accuracy of stock price prediction. The results of this literature review indicate that the hybrid LSTM method in stock price prediction shows promise in improving prediction accuracy. The use of optimization algorithms such as GA, AGA, and APSO has successfully produced models with low RMSE values, indicating minimal prediction errors. However, some methods such as LSTM-EMD and LSTM-RNN-LSTM still require further development to improve their performance.
Long Short-Term Memory vs Gated Recurrent Unit: A Literature Review on the Performance of Deep Learning Methods in Temperature Time Series Forecasting Furizal, Furizal; Fawait, Aldi Bastiatul; Maghfiroh, Hari; Ma’arif, Alfian; Firdaus, Asno Azzawagama; Suwarno, Iswanto
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1546

Abstract

Temperature forecasting is a crucial aspect of meteorology and climate change studies, but challenges arise due to the complexity of time series data involving seasonal patterns and long-term trends. Traditional methods often fall short in handling this variability, necessitating more advanced solutions to enhance prediction accuracy. LSTM and GRU models have emerged as promising alternatives for modeling temperature data. This study is a literature review comparing the effectiveness of LSTM and GRU based on previous research in temperature forecasting. The goal of this review is to evaluate the performance of both models using various evaluation metrics such as MSE, RMSE, and MAE to identify gaps in previous research and suggest improvements for future studies. The method involves a comprehensive analysis of previous studies using LSTM and GRU for temperature forecasting. Assessment is based on RMSE values and other metrics to compare the accuracy and consistency of both models across different conditions and temperature datasets. The analysis results show that LSTM has an RMSE range of 0.37 to 2.28. While LSTM demonstrates good performance in handling long-term dependencies, GRU provides more stable and accurate performance with an RMSE range of 0.03 to 2.00. This review underscores the importance of selecting the appropriate model based on data characteristics to improve the reliability of temperature forecasting.
Performance Optimization of a DFIG-based Variable Speed Wind Turbines by IVC-ANFIS Controller Ouhssain, Said; Chojaa, Hamid; Aljarhizi, Yahya; Al Ibrahmi, Elmehdi; Hadoune, Aziz; Maarif, Alfian; Suwarno, Iswanto; Mossa, Mahmoud A.
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22118

Abstract

An improved indirect vector control (IVC) method for a wind energy conversion system (WECS) is presented in this research. Field-oriented control or indirect vector control as it is sometimes called is a very important element of contemporary WECS that employs DFIGs. This control strategy is pivotal for achieving high performance and efficiency of DFIG-based wind turbines because it offers direct control on the torque and power ratings of the generator. A doubly fed induction generator (DFIG) is used by the WECS to inject power to the grid. An adaptive network-based fuzzy inference system (ANFIS), which is proposed to replace traditional methods like linear PI controllers, is the basis for this IVC. In this paper we chose ANFIS controller over traditional linear Proportional-Integral (PI) controllers due to its ability to adapt and learn from the system, leading to improved performance. The rotor voltage is controlled by the proposed IVC in order to regulate the exchanged active and reactive power between the stator and the grid. In order to verify the proposed control in terms of performance and robustness, a comparative analysis between the proposed ANFIS and linear PI controllers for the WECS-DFIG system is performed by a simulation study in a MATLAB/Simulink environment. This analysis covers both the transient and steady states of operation. As a result, the proposed ANFIS controller shows improved efficiency and robustness compared to the linear PI controllers. This superiority stems from its ability to integrate the flexibility and effectiveness inherent in diverse artificial intelligence controllers, specifically the synergistic use of Neural Network (NN) and Fuzzy Logic (FL) algorithms. The ANFIS controller's adaptability to diverse operating conditions and its capability to learn and optimize its performance play pivotal roles in enhancing its control capabilities within the WECS-DFIG system.
Co-Authors Abboud, Sarah Abdulmaged, Riyam Bassim Al Ibrahmi, Elmehdi Al Madani Kurniawan, Aqsha Aldi Bastiatul Fawait Fawait Alfian Ma’arif Aljarhizi, Yahya Ansarifard, Mehdi Asno Azzawagama Firdaus Atashgah, Mohammad Ali Amiri Azlan, Norsinnira Zainul Baballe, Muhammad Ahmad Basil, Noorulden Boulal, Abdellah Cakan, Abdullah Chojaa, Hamid Diab, Ahmed A. Zaki Dwi Ana Ratna Wati Elahian, Samaneh Elsawy, Ahmed M. Fadhil Mohammed, Abdullah Faikul Umam Fawzy, Ibram Y. Furizal Furizal, Furizal Habachi, Rachid Hadoune, Aziz Kamarudzaman, Nohaslinda Lora Khaula Amifia Loulijat, Azeddine Maghfiroh, Hari Maharani Raharja, Nia Mahmoud Zadehbagheri Mahmoud, Magdi S. Marhoon, Hamzah M. Meiyanto Eko Sulistyo Moch. Iskandar Riansyah Mossa, Mahmoud A. Muhammad Amin Muhammad Fuad Murni Murni Nia Maharani Raharja Osman, Mohamad Afwan Ouhssain, Said Puriyanto, Riky D. Rahim Ildarabadi Ridha, Hussein Mohammed Ritonga, Asdelina Samewoi, Abdul Rahman Semma, El Alami Sugiyarta Sugiyarta Wahyu Caesarendra Wijaya, Setiawan Ardi Yamaura, Hiroshi Yunandha, Isro D.