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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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Dynamic Motion Control of Two-Link Robots with Adaptive Synergetic Algorithms Abbas, Aya Khudhair; Kadhim, Saleem Khalefa
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.v5i5.22985

Abstract

Robotics is advancing to assist with daily tasks by developing human-like robotic limbs, which involves challenges in integrating software, control systems, electronics, and mechanical designs. To address these challenges, Classic Synergetic Controller (CSC) and Adaptive Synergetic Controller (ASC) algorithms were created using mathematical equations to regulate the robot arm's joint angle position and achieve precise tracking. A comparison with Adaptive Sliding Mode Control (ASMC) and Classical Sliding Mode Control (CSMC) demonstrated that CSC and ASC outperform in efficiency and robustness. ASC improved by 63%, providing smoother angular position tracking and faster response times. CSC reached the desired position angle in 1.5 seconds with oscillations, while ASC achieved it in 2.4 seconds without oscillations and eliminated chattering. CSC's Root Mean Square (RMS) was 1.57 rad, whereas ASC had no RMS value. The improvement rate of ASC over CSC was 100%, ensuring seamless motion, better rise time, and eliminating oscillations, thus providing robust control against disturbances and parameter variations.
Speed Control for Linear Induction Motor Based on Intelligent PI-Fuzzy Logic Ahmed, Ahmed H.; Yahya, Ahmed S.; Ali, Ahmed J.
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.v5i5.22203

Abstract

Nowadays, linear induction motors (LIM) are most used in applications such as transportation, liquid metal pumping, material handling, etc. These applications require large forces and high constant speed under changes in load. The LIM suffers from change in speed as a result of the force loads applied to it instantaneously, which causes high ripple in the force response and not constant speed. This research proposes solutions to these problems by designing an intelligent controller to improve the response variable-speed with different forces. LIM was represented by d-q model using MATLAB/Simulink based-on equivalent circuit equations for LIM and study dynamic performance of this machine. The motor was operated at different speeds and loads; the speed change was observed when the load changed. a PI-controller was designed to control velocity of the machine and keeping its velocity constant at load changes. the values of gains (Kp, Ki) was taken manually by using Ziegler method and this requires a long time as tuning the gain values at every reference speed. An intelligent self-tuning fuzzy-PI controller was prepared to select best values of gains and compared with PI-controller. The simulation outcomes display that fuzzy-PI controller has improved speed and force moving performances machine than PI-controller since we obtained least ripple in the force response. The results obtained in the simulation are interesting, given that the Fuzzy-PI controller designed has nonlinear behavior that achieves wide range of speeds operation for the machine at variable forces compared with traditional PI-controller, and this gave clear improvement in the engine’s performance.
Optimization of Proportional Integral Derivative Controller for Omni Robot Wheel Drive by Using Integrator Wind-up Reduction Based on Arduino Nano Supriadi, Supriadi; Wajiansyah, Agusma; Zainuddin, Mohammad; Putra, Arief Bramanto Wicaksono
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.v5i6.21807

Abstract

The experimental object used is a three-wheeled omni-robot frame, where the wheel axes have an angle difference of 120 degrees from each other. The Omni wheels have a diameter of 48 mm connected to the DC motor axis through a gearbox, which has a ratio of 80 to 1. Each wheel has been controlled using a proportional plus integral plus derivative (PID) controller embedded in a microcontroller, which is an Arduino nano board. The motor axis is equipped with a two-phase optical encoder that definitively generates four cycles per revolution for wheel speed acquisition as the controller input. The wheel speed control signal is distributed to the wheel through the H bridge as the controller output. The controller constants have been directly tuned to the robot frame's physical omni-wheel speed control system. The controller is tuned to minimize steady-state error, achieve fast settling times, and minimize overshoot. The best constants obtained are 1.5 (proportional), 0.012 (integral), and 10 (derivative). Using a tolerance band of +/- 2.5%, the system achieved a settling time of 1.1 seconds and a steady-state error of 0.3%. The control system is unstable when the actuator is saturated, which produces oscillations. Controller optimization has been successful by using integrator wind-up reduction. The steady-state average error was reduced to 9.95% without oscillation after optimization, compared to 46.37% with oscillations before optimization. The controller has been validated with speed-tracking tests on all velocity vector regions. The robot frame has been tested with basic maneuvers such as rotation, concerning, forward, and sideways.
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.
Integration of Modbus-Ethernet Communication for Monitoring Electrical Power Consumption, Temperature, and Humidity Le, Long Ho; Ngo, Thanh Quyen; Toan, Nguyen Duc; Nguyen, Chi Cuong; Phong, Bui Hong
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.v5i6.22456

Abstract

Effective management of electrical energy requires monitoring, controlling, and storing parameters gathered from power measurement devices including voltage, current, temperature, and humidity. This assessment of the quality of electrical energy is essential for management organizations, power companies, and individual consumers to develop efficient electricity usage plans. Based on the requirement, we proposed a hardware implementation for data collection and online communication software integrated with a system for collecting data on consumption of electrical energy. The EM115-Mod CT multifunction industrial meters by FINECO, the KLEA 220P three-phase multifunction meter by KLEMSAN, and the ME96SS–ver.B by MITSUBISHI are involved. Finally, the collected data of electrical consumption, temperature, and humidity can be stored on an SD card, transmitted to the cloud for real-time monitoring on mobile devices, and facilitated by the ESP-WROOM-32 microcontroller from Espressif system.
Enhanced Stacked Ensemble-Based Heart Disease Prediction with Chi-Square Feature Selection Method Sarra, Raniya R.; Gorial, Ivan Isho; Manea, Reham Raad; Korial, Ayad E.; Mohammed, Mustafa; Ahmed, Yousif
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.v5i6.23191

Abstract

Heart disease (HD) is the primary cause of death globally, requiring more accurate, affordable diagnostic technologies. Traditional HD diagnostic methods are adequate but expensive and limited, creating a need for creative alternatives. Machine learning (ML) is one of the many sophisticated technologies healthcare systems use to predict diseases. This work aims to enhance the accuracy and efficiency of HD diagnosis by developing a stacked ensemble classifier that combines predictions from different ML classifiers and uses chi-square feature selection to prioritize significant features. Combining predictions from three basic ML classifiers—decision trees (DT), support vector machines (SVM), and multilayer perceptron (MLP)—the paper creates a stacked ensemble classifier. To raise diagnostic accuracy, this stacked ensemble classifier maximizes the strengths of base classifiers and reduces their errors. Furthermore, applying the chi-square feature selection approach, the study finds five important features for training the classifiers on the Cleveland dataset with thirteen (13) features. Selecting only important features through feature selection minimizes dimensionality, simplifies the classifier, and improves computational performance. This also reduces overfitting, increases generalizability, and speeds up diagnosis, making it more viable for real-time clinical applications. Before and following the feature selection procedure, the ensemble classifier performance is assessed against the base classifiers concerning the accuracy, recall, precision, and f1-score. These metrics are chosen for their ability to validate the effectiveness of the proposed diagnostic tool.  With an accuracy of 85.5%, the stacked ensemble classifier exceeded base classifiers before feature selection. After feature selection, the stacked ensemble classifier’s accuracy improved to 90.8%. These results underline the proposed method as an inexpensive and more efficient diagnostic tool for HD as compared to current methods, enabling earlier HD detection and lowering healthcare costs. In conclusion, this creative method could alter healthcare systems by providing a highly accurate and affordable diagnostic tool for clinical use.
Research Trends and Knowledge Taxonomy of Artificial Intelligence Applications in Supply Chain Management, Logistics, and Transportation: A Systematic Literature Review and Bibliometric Analysis Kriouich, Mohamed; Sarir, Hicham; Louah, Soulaiman
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.v5i5.21859

Abstract

Due to industrialization and globalization, supply chains (SC) have become more and more in need of artificial intelligence (AI), which has sparked conversations on how to use it to improve SC performance globally. Using both quantitative and qualitative methodologies, this study provides a thorough examination of the trends, gaps, and knowledge structure in the literature on AI in SC. Scientific mapping was used to summarize 140 important publications published between 1998 and 2022. Publication years, attribution, journal co-citations, partnerships between countries and institutions, significant papers, related keywords, and historical study groups were all included in the bibliographic analysis. A thematic categorization of the data produced 22 sub-branches of AI application in SC that are covered in five domains: environment, planning and risk management, SC areas, technology, logistics and transportation, and planning and environment. The study identifies current knowledge gaps and recommends future research directions due to limited international cooperation and inadequate platforms for advancing technology research. these findings aid academics and practitioners by providing a coherent intellectual outlook on AI's involvement in SC.
Synergetic Control Design Based Sparrow Search Optimization for Tracking Control of Driven-Pendulum System Al-Khazraji, Huthaifa; Al-Badri, Kareem; Al-Majeez, Rawaa; Humaidi, Amjad J
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.v5i5.22893

Abstract

This study investigates the performance of designing a Synergetic Control (SC) approach for angular position tracking control of driven-pendulum systems. SC is one of the popular nonlinear control techniques that contributed in a variety of control design applications. This research shows a unique application of the SC for angular position tracking control of driven-pendulum systems. Initially, the equations of motion of the system are developed. Subsequently, the control law of the SC is established. For the stability analysis of the closed loop control system, the Lyapunov Function (L.F) is used. To guarantee optimal performance, a Sparrow Search Optimization (SSO) based approach is presented in order to search for the optimum designing parameters of the controller. For performance comparison, the classical Sliding Mode Control (SMC) is introduced. The simulation's outcomes of the study have been confirmed that the proposed control algorithm is addressed the tracking problem of the angular position of the system successfully. Besides, when an external disturbance is inherited in the simulation, the SC exhibits a robustness performance. Moreover, the performance of the SC is slightly similar as SMC. However, the distinct difference in the performance is that the control signal of the SMC exhibits chattering problem, while this phenomenon is absent in the SC. All computer simulations are carried out using MATLAB software.
Errors Detection Based on SDWT and BNN Applied for Position, Velocity and Acceleration Signals of a Wheeled Mobile Robot Saeed, Saad Zaghlul; Alobaidy, Muhamad Azhar Abdilatef; Yosif, Zead Mohammed
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.v5i5.21424

Abstract

Accurate error detection in mobile robots is crucial for reliable operation and prevention of mechanical or electrical failures. Mechanical defects on the wheels of mobile robot make real path deviate from the desired path and trajectory. From the kinematics equations, error in the angular velocity of wheel affects the position, velocity, and acceleration. Each of these signals is fed to the Symelet discrete wavelet transform (SDWT) for the purpose of error's feature detection and extraction. The SDWT with 5-level for each component of the signal produce 10 inputs for the Bayesian Neural network (BNN). The BNN with single layer of 18 neurons classifies the inputs either no error case or specify the wheel(s) at which error had been happened. Straight line and circular paths were tested in the presence of errors in single wheel or both wheels. Two different path's time durations are tested to investigate robustness of the proposed methodology. The simulation’s results of two wheels mobile robot showed that acceleration's signal for a straight-line path has accuracy of 100%, MSE 3.05×10-23 and 9.81×10-17, training iterations are 15 and 23 for 4- and 2-seconds durations; respectively. While for a circular path, displacement's signal gave high accuracy 100%, MSE 8.86·10-16 and 3.76×10-18, training iteration 17 and 13 for 4- and 2-seconds durations; respectively. Acceleration signal can be used for detecting errors in real time by using accelerometer. Limitations such as amount of data besides to the sensor noise affects the proposed methodology.
Neuro-Fuzzy Controller for a Non-Linear Power Electronic DC-DC Boost Converters Al-Dabbagh, Zainab Ameer; Shneen, Salam Waley
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.v5i5.22690

Abstract

The current paper aims to explore the possibility of improving the performance of one of the important systems (boost DC-DC converter) in the field of electrical energy by contributing to the use of electronic power converters to provide the scheduled voltage to the loads with changing operating conditions using traditional (PID control) and expert (Neuro-fuzzy logic control) methods. Test cases are proposed to verify the possibility of improvement and the effectiveness of the system through approved measurement criteria such as improving stability, response time, efficiency, or a performance measure for overshoot and undershoot rates and rise time in addition to steady-state error through which comparison can be made to know the best between the methodology used to evaluate the performance of PID controllers and ANFIS (Adaptive Neuro-fuzzy Inference System). The current paper deals with a study of the operation of non-linear DC-DC Converters with a Neuro-Fuzzy Controller. To verify the system's effectiveness, proposed tests are conducted to simulate operation in real-time. The assumptions adopted are that the input voltage value is available from a direct current source with a voltage of (12) volts, and what is required to supply a load with a voltage ranging between (22-120) depending on the load change. The necessary calculations were made to calculate the converter parameters. The required inductance value was (160μH) and the capacitance value was (276μF). The simulation test was conducted using a model consisting of a resistive load and a step-up converter in addition to the supply source in both the open-loop and closed-loop system states. System tests were also conducted in the presence of the proposed controllers to verify the system's effectiveness.

Page 53 of 71 | Total Record : 708

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