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Journal of Robotics and Control (JRC)
Published by Universitas Muhammadiyah Yogyakarta
ISSN : 27155056     EISSN : 27155072     DOI : https://doi.org/10.18196/jrc
Core Subject : Science, Engineering,
Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering
Journal of Robotics and Control (JRC) is an international open-access journal published by Universitas Muhammadiyah Yogyakarta. The journal invites students, researchers, and engineers to contribute to the development of theoretical and practice-oriented theories of Robotics and Control. Its scope includes (but not limited) to the following: Manipulator Robot, Mobile Robot, Flying Robot, Autonomous Robot, Automation Control, Programmable Logic Controller (PLC), SCADA, DCS, Wonderware, Industrial Robot, Robot Controller, Classical Control, Modern Control, Feedback Control, PID Controller, Fuzzy Logic Controller, State Feedback Controller, Neural Network Control, Linear Control, Optimal Control, Nonlinear Control, Robust Control, Adaptive Control, Geometry Control, Visual Control, Tracking Control, Artificial Intelligence, Power Electronic Control System, Grid Control, DC-DC Converter Control, Embedded Intelligence, Network Control System, Automatic Control and etc.
Arjuna Subject : Teknik (semua kategori) - Teknik Kontrol dan Sistem
Articles 708 Documents
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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.
Design of an Integral Fuzzy Logic Controller for a Variable-Speed Wind Turbine Model Almaged, Mohammed; Mahmood, Ali; Alnema, Yazen Hudhaifa Shakir
Journal of Robotics and Control (JRC) Vol 4, No 6 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i6.20194

Abstract

The demand for electricity is continuously growing around the world and thus the need for renewable and long-lasting sources of energy has become an essential challenge. Wind turbines are considered one of the major sources of renewable electricity generation. Therefore, there is a crucial demand for wind turbine model and control systems that are capable of precisely simulating the actual wind power systems. In this paper, an advanced fuzzy logic controller is proposed to control the speed of a wind turbine system. Initially, aero dynamical, mechanical and electrical models of two mass wind turbines models are derived. Analytical calculation of the power coefficient is adopted through a nonlinear function of six coefficients that mainly depends on pitch angle and tip speed ratio. The ultimate power output from the turbine can reach up to 50 % which is achieved at zero pitch angle with an approximately tip speed ratio of eight. This is then followed by designing a hybrid fuzzy-plus I pitch controller to regulate the speed of the wind turbine shaft. In general, fuzzy logic control strategy have the advantages over traditional control techniques especially when the system is highly non-linear and has to deal with strong disturbances such as wind turbulence. To evaluate the reliability and robustness of the controller, the response of the wind turbine system is tested under several types of disturbances including wind fluctuation, sudden disturbances on high and low speed shafts. Simulation findings reveals that the performance of fuzzy-integral control technique outweighs that of conventional fuzzy approach in terms of multiple performance evaluation indexes such as zero overshoot and steady state error, rise time and a settling time of (32.9 s) (44.7 s) respectively. The reliability and robustness of the controller is tested by applying speed and torque disturbances of 25% of their maximum ranges. Results have revealed that the controller was able to reject all disturbances efficiently with a change in pitch angle up to a maximum of 10 degrees in order to retain a constant rotor speed at 1000 rpm.
IoAT: Internet of Aquaculture Things for Monitoring Water Temperature in Tiger Shrimp Ponds with DS18B20 Sensors and WeMos D1 R2 Satra, Ramdan; Hadi, Mokh. Sholihul; Sujito, Sujito; Febryan, Febryan; Fattah, Muhammad Hattah; Busaeri, Siti Rahbiah
Journal of Robotics and Control (JRC) Vol 5, No 1 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i1.18470

Abstract

Cultivation of tiger prawns stands as a crucial sector in Indonesia's fisheries industry, significantly contributing to the country's foreign exchange. However, challenges persist in the cultivation process, particularly concerning suboptimal harvest outcomes. A critical factor in tiger prawn cultivation is the water temperature within shrimp ponds, a parameter directly influencing shrimp growth. The recommended normal temperature range is 28-31°C. Deviations from this range can adversely impact the shrimp's metabolic system and appetite, resulting in stress and potential mortality. Temperature fluctuations can lead to severe issues such as hindered growth, reduced productivity, and increased shrimp mortality. Real-time monitoring of air temperature emerges as a pivotal element in ensuring the success of shrimp farming. This research aims to provide a practical solution for shrimp cultivation by presenting a system that enables farmers to adjust air temperature in ponds in real-time through a user-friendly website application. The ability to promptly respond to abnormal temperature fluctuations empowers farmers to optimize cultivation conditions, thereby reducing shrimp mortality rates. The research focuses on creating a water temperature monitoring system for tiger prawn ponds using cloud storage through the Firebase platform. By implementing real-time temperature monitoring, financial risks for shrimp farmers can be mitigated, preventing losses attributed to temperature-induced shrimp mortality. The research utilizes the DS18B20 temperature sensor and WeMos D1 R2 as the control center. The website displays air temperature measurements, showcasing a high accuracy of 99% with a minimal error of 1.2%. This underscores the system's effectiveness in measuring air temperature both above and below the pond. The incorporation of IoT technology for monitoring water quality in ponds offers a practical and innovative approach to tiger prawn cultivation, with the potential to enhance production outcomes in each harvest.
Key Factors that Negatively Affect Performance of Imitation Learning for Autonomous Driving Rijanto, Estiko; Changgraini, Nelson; Saputra, Roni Permana; Abidin, Zainal
Journal of Robotics and Control (JRC) Vol 5, No 1 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i1.20371

Abstract

Conditional imitation learning (CIL) has proven superior to other autonomous driving (AD) algorithms. However, its performance evaluation through physical implementations is still limited. This work contributes a systematic evaluation to identify key factors potentially improving its performance. It modified convolutional neural network parameter values, such as reducing the number of filter channels and neuron units, and implemented the model into a vision-based autonomous vehicle (AV). The AV has front-wheel steering with an Ackermann mechanism since it is commonly used by passenger cars. Using the Inertia Measurement Unit, we measured the vehicle’s location and yaw angle along the experimental route. The AV had to move autonomously through new road sectors in the morning, afternoon, and night. First, an overall performance evaluation was carried out. The results showed a 99% success rate from 648 evaluation experiments under different conditions in which the 1% failure rate happened at new intersections. Then, a turning performance evaluation was conducted to identify key factors leading to failure at new intersections. They include fast speed, dazzling light reflection, late navigation command change instant, and the untrained turning driving pattern. The AV never failed while driving on the trained routes. It had a 100% success rate when driving slower, even under various lighting conditions and at various driving patterns, including untrained intersections. Although this study is limited to identifying key factors at three constant speeds, the results become the foundation for future research to improve CIL performance for AD, including by incorporating multimodal fusion and multi-route networks.
Analysis of Problems and Prospects for Improving Automatic Control Systems of Interconnected Electric Drives Nalibayev, Nurgali; Kozhageldi, Bolat; Omarov, Zhaksylyk; Zhanpeiissova, Aizhan; Tashimbetov, Murat
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.v5i2.20711

Abstract

The aim of this study was to analyse the problems and prospects for improving automatic control systems of interconnected electric drives. Various methods, including analytical, classification, functional, statistical, and synthesis, were used to provide recommendations for error correction in the design processes of these systems and to detail their functioning. The study revealed the peculiarities and differences of automatic control systems of interconnected electric drives. The study analysed the errors made during the operation of these systems and the reasons for their occurrence. It also identified uncertainties in the development process and their impact on the functioning of the systems. The mechanism's efficiency, development, and complexity in different spheres were analysed. The text also considered issues related to estimating the operation of systems, limitations during operation, and the influence of limitations on results. Recommendations for promoting more effective regulation have been provided. The research showed that these systems play a crucial role in complex technological processes. The results have vague practical implications for developing the mechanism of automatic control systems for interconnected electric drives to apply and influence a certain device. In conclusion, the study analysed the problems and prospects for improving automatic control systems for interconnected electric drives.
Solvability and Weak Controllability of Fractional Degenerate Singular Problem Fatma, Achab; Batiha, Iqbal; Imad, Rezzoug; Takieddine, Oussaeif; Ouannas, Adel
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.v5i2.20474

Abstract

In this paper, our objective is to investigate the unique solvability and the weak controllability of the fractional degenerate and singular problem. The energy inequality method is gives a sufficient conditions for the existence and the uniqueness of the strong solution of our problem. This problem is ill-posed in the sense of Hadamard. To address this, we attempt regularization through a fractional Tikhonov regularization method, which not only establishes weak controllability but also provides a full characterization of the optimal control.
Improved Droop Control Based on Modified Osprey Optimization Algorithm in DC Microgrid Aribowo, Widi; Suryoatmojo, Heri; Pamuji, Feby Agung
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.v5i3.21347

Abstract

In this research, a modified Osprey optimization algorithm (MOOA) is presented to optimize droop control parameters. MOOA is a modification of the Osprey optimization algorithm by adding levy flight which has the advantage of exploiting a wider space and being adaptive to environmental changes. This research also modifies droop control, Proportional Integral Derivative (PID) is applied to secondary control. PID has flexibility in responding to changes in system conditions and fast response in dealing with system changes. The PID parameters are optimized using MOOA and are called MOOA-PID. The MOOA method is validated using 23 CEC2017 benchmarks-function and performance on DC microgrid systems. This research uses the latest algorithms as a comparison, namely One-to-One Based Optimizer (OOBO), Preschool Educational Optimization Algorithm (PEOA), and the red-tailed hawk (RTH) algorithm in testing 23 CEC2017 benchmark functions. From the simulation of the 23 CEC2017 benchmark function, it is known that the MOOA method has better capabilities. MOOA has advantages in 15 out of 23 benchmark functions. In DC microgrid system testing, MOOA-PID is compared with the Proportional Integral (PI) method which is optimized with MOOA and is called MOOA-PI. Testing on the microgrid is aimed at determining the performance of the transient response of power, voltage and current in the system. Tests on DC microgrid systems found that the application of MOOA-PID in secondary control had better capabilities than MOOA-PI. The average value of voltage overshoot from MOOA-PID is 9.828% better than MOOA-PI. The average ITSE MOOA-PID score is 22.3% better than MOOA-PI.
Improving the Productivity of Laying Hens Through a Modern Cage Cleanliness Monitoring System that Utilizes Integrated Sensors and IoT Technology Ishak, Fauzi; Wardhana, Ichlasul Amal Restu; Mutiara, Giva Andriana; Periyadi, Periyadi; Meisaroh, Lisda; Alfarisi, Muhammad Rizqi
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.v5i4.21610

Abstract

Animal husbandry plays a crucial role in the Indonesian economy. One example is layer farming. The cage's environmental conditions can have an impact on the health of laying hens, including factors like temperature, humidity, and the presence of ammonia gas. This research aims to support chicken farmers in identifying and monitoring the environmental conditions surrounding their chicken coops, with the goal of enhancing the productivity of laying hens. This study is organized using a prototype development approach. The proposed system utilizes Arduino UNO as a microcontroller, ESP32 as a connecting node from hardware to software, MQ-135 sensor as an ammonia gas sensor, DHT-22 sensor as a temperature and humidity sensor, and 16x2 I2C LCD to display the collected data. WIFI connected web monitoring system built with Laravel, MySQL, and Bootstrap. An improvement to the existing system is the integration of an ammonia gas odor sensor calibrated against clean air as a reference. Testing was conducted for a continuous period of 7 days. Comparison of test results is performed with existing devices to observe the difference in measured values. The measurement result demonstrates a remarkable ability to accurately measure temperature, humidity, and ammonia levels in the air. The difference with the comparable device was about 2%.  Meanwhile, the monitoring dashboard for IoT functional monitoring operates effectively, allowing chicken farmers to efficiently analyze the cleanliness of their chicken coops. All measurement parameters are conveniently recorded in the form of tables and graphs, providing valuable information.
Integration of Convolutional Neural Networks and Grey Wolf Optimization for Advanced Cybersecurity in IoT Systems Jaddoa, Israa Ahmed
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.v5i4.22178

Abstract

The rapid integration and application of the Internet of Things in daily life have significantly improved connectivity and intelligent control to various devices. However, it has exposed such systems to increased susceptibility to cyber challenges, such as infiltration, data sovereignty, and cyber-attacks. There is a need for an efficient and secure solution to these apparent security concerns which require complex social structures to adapt to various learning lessons quickly. The purpose of this study is to provide an inventive evolutionary operation to enhance the security of IoT networks and by integrating Convolutional Neural Networks and items of Grey Wolf Optimization algorithms – Standard GWO, Modified GWO and Advanced modified GWO. The GWOs were used to include surveillance accuracy layout, hence boosting detection accuracy. The action Lloyd testing found that smaller OWG intelligence (which achieved initially) unlimited interpretations which increased the percentage and was 97.4 %. This approach was further increased with FGWE, achieving 97.7 percentage, and 97.8 2.02% errors. The performance of both was 98.4 and 97.5 for the two classes, respectively. The current study’s results reveal the effectiveness of computational development to enhancing secure IoT networks and offer a secure prototype for potential study to optimize the security structure. effet for keynote curricular scenarios due to the system cause and trusty security solutions.
Safe Experimentation Dynamics Algorithm for Identification of Cupping Suction Based on the Nonlinear Hammerstein Model Suresh, Kavindran; Ghazali, Mohd Riduwan; Ahmad, Mohd Ashraf
Journal of Robotics and Control (JRC) Vol 4, No 6 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i6.18909

Abstract

The use of cupping therapy for various health benefits has increased in popularity recently. Potential advantages of cupping therapy include pain reduction, increased circulation, relaxation, and skin health. The increased blood flow makes it easier to supply nutrients and oxygen to the tissues, promoting healing. Nevertheless, the effectiveness of this technique greatly depends on the negative pressure's ability to create the desired suction effect on the skin. This research paper suggests a method to detect the cupping suction model by employing the Hammerstein model and utilizing the Safe Experimentation Dynamics (SED) algorithm. The problem is that the cupping suction system experiences pressure leaks and is difficult to control. Although, stabilizing the suction pressure and developing an effective controller requires an accurate model. The research contribution lies in utilizing the SED algorithm to tune the parameters of the Hammerstein model specifically for the cupping suction system and figure out the real system with a continuous-time transfer function. The experimental data collected for cupping therapy exhibited nonlinearity attributed to the complex dynamics of the system, presenting challenges in developing a Hammerstein model. This work used a nonlinear model to study the cupping suction system. Input and output data were collected from the differential pressure sensor for 20 minutes, sampling every 0.1 seconds. The single-agent method SED has limited exploration capabilities for finding optimum value but excels in exploitation. To address this limitation, incorporating initial values leads to improved performance and a better match with the real experimental observations. Experimentation was conducted to find the best model parameters for the desired suction pressure. The therapy can be administered with greater precision and efficacy by accurately identifying the suction pressure. Overall, this research represents a promising development in cupping therapy. In particular, it has been demonstrated that the proposed nonlinear Hammerstein models improve accuracy by 84.34% through the tuning SED algorithm.

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