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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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Robust Power Management for Smart Microgrid Based on an Intelligent Controller Alsanad, Hamid R.; Al Mashhadany, Yousif; Algburi, Sameer; Abbas, Ahmed K.; Al Smadi, Takialddin
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i1.23554

Abstract

A microgrid (MG) is an autonomous electrical system that can operate independently or link to the grid. It is usual practice to use a single grid organization to improve energy access and ensure a consistent supply of electricity. Microgrids (MGs) can be unstable if islanded given that they lack the predominant grid's high friction and are subject to large voltage and frequency swings. Standards, directions, and accessibility and interoperability criteria all address the dependability of a microgrid, the use of distributed local resources, and cybersecurity. This work presents a revolutionary intelligent controller, Adaptive. This study proposes a novel intelligent controller, the Adaptive Network-based Fuzzier Inference System - Drooping Controller (ANFISDC), with a drooping coefficient modification, to provide optimal power sharing while minimizing power overloading/curtailment. To provide the essential stability and lucrative power sharing for the islanded the microgrid, the dropping coefficient is changed to account for the power fluctuations of RES (renewable energy source) components as well as the relationship between electricity production and demand. Furthermore, secondary control is used to restore the frequency/voltage drop caused by the droop control. Simulations with load fluctuations in MATLAB/Simulink show that the proposed strategy improves the stability and economic viability of microgrids powered by energy from renewable sources based on droop. The outcomes of the simulation demonstrate how well the suggested ANFISDC approach works to keep the microgrid operating steadily and profitably.
Cooperative Formation and Obstacle Avoidance Control for Multi-UAV Based on Guidance Route and Artificial Potential Field Sahal, Mochammad; Maynad, Vincentius Charles; Bilfaqih, Yusuf
Journal of Robotics and Control (JRC) Vol. 5 No. 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23577

Abstract

Research on cooperative control of multi-UAV systems has gained significant attention in the flight control field, with a particular focus on formation control and obstacle avoidance due to their complexity and importance. This paper introduces an approach to a group of quadcopter control by integrating fuzzy controller, guidance route, and Artificial Potential Field (APF) methods. The quadcopter dynamic model, featuring six degrees of freedom, is controlled using a fuzzy state feedback controller in its inner loop. From the outer loop, the formation-making is guided by an easy-to-use and versatile guidance route approach while obstacle avoidance is tackled using the optimal APF method. There are two avoidance strategies that can be compared and analyzed, called "total avoidance" and "minimal avoidance", both individually and as a "combined" strategy. Simulations in various environments with different obstacle sizes show that all control algorithms can accomplish the tasks effectively. Both strategies have their own strength in terms of path length and formation maintenance. A formation performance index, which is calculated based on the difference between the desired position and the actual position of each quadcopter, is used to quantify the effectiveness of the method. A smaller value means better formation maintenance. The total avoidance strategy achieved an average index of 0.8000 and the minimal avoidance strategy reached 1.2227. These metrics highlight the trade-offs of each strategy in maintaining optimal formation. These findings offer valuable insights for the development of more robust multi-UAV systems, with potential applications in autonomous delivery services, surveillance, and environmental monitoring.
Combining Finite State Machine and Fuzzy Logic Control for Accuracy Enhancing Performance of a Tomato-Handling Robot Gripper Mardiati, Rina; Firdaus, Hardiansyah; Setiawan, Aan Eko; Zulherman, Dodi
Journal of Robotics and Control (JRC) Vol. 5 No. 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23579

Abstract

Robotic grippers are becoming increasingly vital in modern agriculture, especially in tasks like harvesting delicate crops such as tomatoes, where precision and care are crucial. These advanced tools are designed to handle tomatoes without causing damage, significantly improving efficiency and reducing labor costs. Research on gripper robots for fruit picking continues to be developed using various methods in an effort to achieve accurate picking results. This study proposes a hybrid method that combines Finite State Machine (FSM) for behavior control with Fuzzy Logic Control (FLC) to optimize the positioning of the gripper. The system utilizes a PixyCam2 CMUcam5 for tomato detection, an Arduino microcontroller for image processing, and a servo mechanism to precisely align the gripper with the target. The experimental results confirm that each component functions as expected, with the gripper successfully performing actions such as idling, gripping, and placing in accordance with the FSM model. Furthermore, the FLC model was tested against simulations, resulting in error rates of 1.004% for the elbow angle and 0.826% for the base angle. The entire system was validated by comparing the performance of the system using FLC and non-FLC in ten tests, each with tomatoes placed in different positions. The results indicate that the proposed gripper, utilizing the FSM-FLC model, achieved a 100% success rate in grasping the target, significantly outperforming the FSM-non-FLC gripper, which achieved only a 20% success rate. These findings have important implications for the agricultural industry. The successful integration of the FSM and FLC models in robotic grippers paves the way for fully automated harvesting systems, potentially reducing costs and enhancing productivity.
Model-free Optimal Control for Underactuated Quadrotor Aircraft via Reinforcement Learning Duong, Quynh Nga; Dang, Ngoc Trung
Journal of Robotics and Control (JRC) Vol. 5 No. 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23585

Abstract

The control of Unmanned Aerial Vehicles (UAVs), especially quadrotor aircraft, has many practical applications such as transporting, mapping, rescue, and agricultural applications. This paper investigates solving the optimal tracking control problem for a quadrotor system. First, an underactuated quadrotor system is considered a highly nonlinear system with six degrees of freedom and four inputs. Second, a hierarchical control structure consisting of position and attitude controller is adopted to address the underactuated problem, the position controller to achieve the desired tracking and generates the references for the attitude controller, and the attitude controller to achieve the reference attitude tracking. Third, to achieve optimal trajectory tracking, two Data- based Reinforcement Learning (RL) algorithms are applied to both position and attitude controllers to find the optimal control input by using the input- output quadrotor system data. Compared with the traditional optimal algorithms which require directly solving the Algebraic Ricatti Equation (ARE) or the Hamilton-Jacobi-Bellman (HJB) equation. It is impossible or difficult to implement due to the high nonlinear dynamic nature of the quadrotor system. By using RL in the proposed method, optimal policies can be learned without the knowledge of quadrotor dynamic information. Applying the learning control policies to the quadrotor system, the vehicle achieves optimal trajectory tracking. Finally, a simulation result is conducted to verify the optimal trajectory tracking for quadrotor with the proposed controller.
Assessment of FLC, PID, Nonlinear PID, and SMC Controllers for Level Stabilization in Mechatronic Systems Al-Samarraie, Shibly A.; Gorial, Ivan I.
Journal of Robotics and Control (JRC) Vol. 5 No. 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23639

Abstract

Liquid level measurement is a vital task in industries such as food processing, chemical manufacturing, and petroleum. The findings show that FLC and SMC offer superior performance in terms of rapid response, precision, and stability, particularly in handling nonlinear processes. By implementing these sophisticated controllers, industries put up benefit from increased work stability, low material waste, and improved energy efficiency. The study’s results directly contribute to improving industrial applications by optimizing production and minimizing costs. The primary feather objective of a liquid level control system of rules is to exert a predetermined changeable level using a storage tank, measurement system, controller, and pump. This paper compares quaternary controllers: Fuzzy logical system Controller (FLC), Proportional-Integral-Derivative (PID), Nonlinear PID, and Sliding Mode verify (SMC) applied to some I and connected tankful systems. The FLC is an intelligent controller that excels at managing non-linear and uncertain systems by interpreting influx and outflow rates and adjusting the system to maintain desired unstable levels. Its adaptability to undefined scenarios is a key innovation. The PID controller is used as a benchmark undefined to its simplicity simply struggles with non-linear systems and time-varying parameters. The Non-linear PID controller improves upon the traditional PID by using wrongdoing saturation functions, providing better control in non-linear systems. The SMC is a robust control method that ensures system stableness in the front of disturbances and uncertainties, making it highly effective for heavy-duty applications. Simulation results show that FLC and SMC cater a faster response and better accuracy in reaching desired unstable levels compared to traditional PID controllers. Both systems demo robust stableness and efficient control. As seen in the provided data, the FLC reaches a steady-state level in as little as 8.34 seconds in Run 1 and 1.088 seconds in Run 2 for the single-tank system. Similarly, the SMC stabilizes the system in approximately 23.17 seconds in the coupled tank system, reflecting its robust control capabilities.
Adaptive Intrusion Detection System with Ensemble Classifiers for Handling Imbalanced Datasets and Dynamic Network Traffic Almania, Moaad; Zainal, Anazida; Ghaleb, Fuad A; Alnawasrah, Ahmad; Al Qerom, Mahmoud
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i1.23648

Abstract

Intrusion Detection Systems (IDS) are crucial for network security, but their effectiveness often diminishes in dynamic environments due to outdated models and imbalanced datasets. This paper presents a novel Adaptive Intrusion Detection System (AIDS) that addresses these challenges by incorporating ensemble classifiers and dynamic retraining. The AIDS model integrates K-Nearest Neighbors (KNN), Fuzzy c-means clustering, and weight mapping to improve detection accuracy and adaptability to evolving network traffic. The system dynamically updates its reference model based on the severity of changes in network traffic, enabling more accurate and timely detection of cyber threats. To mitigate the effects of imbalanced datasets, ensemble classifiers, including Decision Tree (DT) and Random Forest (RF), are employed, resulting in significant performance improvements. Experimental results show that the proposed model achieves an overall accuracy of 97.7% and a false alarm rate (FAR) of 2.0%, outperforming traditional IDS models. Additionally, the study explores the impact of various retraining thresholds and demonstrates the model's robustness in handling both common and rare attack types. A comparative analysis with existing IDS models highlights the advantages of the AIDS model, particularly in dynamic and imbalanced network environments. The findings suggest that the AIDS model offers a promising solution for real-time IDS applications, with potential for further enhancements in scalability and computational efficiency.
Optimal Integral Sliding Mode Controller Design for Micro Gyroscope Based on Time Delay Estimation Faraj, Mohammad A.; Jassam, Sameh; Abbas, Ahmed K.
Journal of Robotics and Control (JRC) Vol. 5 No. 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23676

Abstract

Controlling Micro-Electro-Mechanical Systems (MEMS) gyroscopes often involves dealing with uncertainties and external disturbances, which can complicate control strategies. This article proposes a novel control strategy that integrates Integral Sliding Mode Control (ISMC) with Time Delay Estimation (TDE) and Arithmetic Optimization Algorithm (AOA) to enhance control performance. The proposed controller, OTDISMC, is designed to eliminate chattering and improve robustness against disturbances without relying on system dynamics. Contrary to the conventional controllers structures which depended on the system dynamic in their schemes, a model free controller is formulated without using system dynamics in its formulation. Time delay estimation technique has been undertaken as an efficient approximating strategy to approximate and compensate the lumped uncertain dynamics of the system. AOA has been undertaken to determine the optimum solutions of the coefficients of proposed control approach. The stability has been analyzed and investigated using the Lyapunov stability criterion. To show the effectiveness and validity of the developed controller, computer simulations in nominal and robustness scenarios have been carried out and compared with TDISMC that tuned by trial and error and PSO-TDISMC that tuned by particle swarm optimization (PSO). Simulation results demonstrate that OTDISMC significantly reduces tracking errors and improves robustness. The results indicate the superiority of the proposed controller as compared with traditional TDISMC tuned by classical methods and PSO-TDISMC tuned by particle swarm optimization (PSO).
Evaluation of Voltage/Frequency and Voltage Source Inverter Control Strategies for Single-Phase Induction Motors Using MATLAB Simulation Dakheel, Hashmia S.; Shneen, Salam Waley; Abdullah, Zainab B.; Shuraiji, Ahlam Luaibi
Journal of Robotics and Control (JRC) Vol. 5 No. 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23760

Abstract

There is a growing interest in studying the single-phase induction motor due to its wide use in many applications, the most important of which are domestic and industrial. A simulation model is built by implementing and running the model using MATLAB to identify the system behavior of the induction motor. To study and analyze the system behavior in different cases, it is proposed to implement and run the model in two ways: the first without control techniques and the second using control techniques. Tests are conducted according to a methodology based on scenarios that include all expected cases that can be assumed to suit real-time operation. To evaluate control strategies, the clear effect between their use and non-use must be demonstrated through clear measurement criteria to include response speed and performance improvement. In induction motor tests, the focus is on electrical and mechanical quantities and the transient and steady state of the system, including a 220-volt supply voltage and a 50 Hz frequency. The initial test case refers to using the model to simulate three cases: the first without load, the second with a constant load of one newton meter, and the third operating the motor as a pump by changing the load according to the pumping quantity and linked to the motor output. After conducting these tests, the different simulation results can be indicated in terms of the change in electrical and mechanical quantities over time during the proposed operating period. The results showed the high starting current that may affect the motor, and the response time for the motor to operate at the rated speed can be considered. Therefore, this requires the use of techniques to improve performance and provide response speed with a gradual increase in the starting current to protect the motor from high starting current. Voltage and frequency control techniques, as well as voltage-to-frequency ratio and another technique representing the voltage source inverter, were used. The results indicate a clear improvement through the stability of the motor by operating with a short response time compared to other cases and the specified rotational speed and specified torque, which shows a relatively high-efficiency performance.
Performance Analysis of PID and SMC Control Algorithms on AUV under the Influence of Internal Solitary Wave in the Bali Deep Sea Wahyuadnyana, Kadek Dwi; Indriawati, Katherin; Darwito, Purwadi Agus; Aufa, Ardyas Nur; Tnunay, Hilton
Journal of Robotics and Control (JRC) Vol. 5 No. 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23800

Abstract

Autonomous Underwater Vehicles (AUVs) play a crucial role in deep-sea exploration, but their stability is often compromised by Internal Solitary Waves (ISWs) and nonlinear disturbances in stratified waters. This study aims to evaluate the performance of two control algorithms, Proportional-Integral-Derivative (PID) and Sliding Mode Control (SMC), in mitigating ISW effects on AUV trajectory tracking. Simulations were conducted in Simulink (MATLAB), modeling AUV dynamics under ISW disturbances with intensities ranging from 0% to 100%. The results reveal that both PID and SMC algorithms experience significant performance degradation as ISW intensity increases, with Root Mean Square Error (RMSE) values rising exponentially between 50% and 75% disturbance levels. While SMC offers better resilience to nonlinear disturbances than PID, neither algorithm fully compensates for high ISW intensities. These findings highlight the limitations of conventional control strategies and underscore the need for more robust, adaptive algorithms for reliable deep-sea AUV operations. Future work will explore Nonlinear Model Predictive Control (NMPC) for improved stability in complex marine environments.
Autonomous Robotic Systems with Artificial Intelligence Technology Using a Deep Q Network-Based Approach for Goal-Oriented 2D Arm Control Bashabsheh, Murad
Journal of Robotics and Control (JRC) Vol. 5 No. 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23850

Abstract

Accurate control robotic arms in two-dimensional environments present significant challenges, particularly in dynamic, real-time applications. Traditional model-based approaches require substantial system modeling, rendering them computationally extensive. This paper presents an adaptive Artificial Intelligence (AI)-driven approach through the use of Deep Q-Networks (DQN) control for a two–link robotic arm thus supporting better scalability. The DQN algorithm, a model-free Reinforcement Learning (RL) technique, allows the robotic arm to independently learn optimal control strategies by interaction with the environment and adapting to dynamic conditions. The task of the robot established reaches a specific target (red point) within a limited number of episodes. Key components of the methodology contain problem statement, DQN architecture, representation of the state and action spaces, a reward function, and the training process. Experimental results indicate that the DQN agent effectively learns to find optimal actions with high accuracy and robustness in guiding the arm to the target. The performance steadily improves during initial training, followed by stabilization, indicating an effective control policy. This study contributes to the knowledge of reinforcement learning in robotic control tasks and demonstrates, in particular, the potential of DQN for solving complex, goal-oriented tasks with minimal prior modeling. Compared to conventional control approaches, the DQN-driven one reveals higher flexibility, scalability, and efficiency. Although carried out in a simplified 2D environment, the novelty of this research lies in its emphasis on enabling the robotic arm to accomplish goal-oriented reaching tasks, lays a strong foundation for future applications in industrial automation and service robotics.

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