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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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Sliding Mode Control based on Neural State and Disturbance Observers: Application to a Unicycle Robot Using ROS2 Nawress, Barhoumi; Gharbi, Asma Najet Lakhal; Braiek, Naceur Benhadj
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.v5i4.21650

Abstract

The major problem dealing with mobile robots is the trajectory tracking control problem, in the presence of random disturbance and unmeasurable angular velocity. In this paper, we propose a Sliding Mode Control (SMC) based on a Nonlinear Disturbance Observer (NDO) and a Neural State Observer (NSO). The (SMC-NDO) controller displays limitations in mitigating external disturbances. Therefore, this research contribution suggests a novel approach that integrates a Neural State Observer (NSO) into the (SMC-NDO) controller, to significantly enhance the performance of a control system. The combined approach improves disturbance reduction while simultaneously estimating the unmeasurable angular velocity, ultimately leading to more accurate path tracking. Furthermore, the Lyapunov method is used to ensure the stability of the closed-loop control on the one hand, and the stability of the Neural State Observer based on the Backpropagation algorithm on the other hand. Numerical simulations and the implementation of the Simulator in ROS/Gazebo demonstrate better performance of our proposed approach (SMC-NSONDO) compared to the Sliding Mode control-based Disturbance Observer (SMC-NDO) and the Sliding Mode Control (SMC). The control proposal in this work is ready for use on most ROScompatible robots. This experiment should offer an enlightening perspective to robotics researchers.
Analysis of ANN and Fuzzy Logic Dynamic Modelling to Control the Wrist Exoskeleton Karis, Mohd Safirin; Kasdirin, Hyreil Anuar; Abas, Norafizah; Saad, Wira Hidayat Mohd; Zainudin, Muhammad Noorazlan Shah; Ali, Nursabilillah Mohd; Aras, Mohd Shahrieel Mohd
Journal of Robotics and Control (JRC) Vol 4, No 4 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i4.19299

Abstract

Human intention has long been a primary emphasis in the field of electromyography (EMG) research. This being considered, the movement of the exoskeleton hand can be accurately predicted based on the user's preferences. The EMG is a nonlinear signal formed by muscle contractions as the human hand moves and easily captured noise signal from its surroundings. Due to this fact, this study aims to estimate wrist desired velocity based on EMG signals using ANN and FL mapping methods. The output was derived using EMG signals and wrist position were directly proportional to control wrist desired velocity. Ten male subjects, ranging in age from 21 to 40, supplied EMG signal data set used for estimating the output in single and double muscles experiments. To validate the performance, a physical model of an exoskeleton hand was created using Sim-mechanics program tool. The ANN used Levenberg training method with 1 hidden layer and 10 neurons, while FL used a triangular membership function to represent muscles contraction signals amplitude at different MVC levels for each wrist position. As a result, PID was substituted to compensate fluctuation of mapping outputs, resulting in a smoother signal reading while improving the estimation of wrist desired velocity performance. As a conclusion, ANN compensates for complex nonlinear input to estimate output, but it works best with large data sets. FL allowed designers to design rules based on their knowledge, but the system will struggle due to the large number of inputs. Based on the results achieved, FL was able to show a distinct separation of wrist desired velocity hand movement when compared to ANN for similar testing datasets due to the decision making based on rules setting setup by the designer.
Using Imperialist Competitive Algorithm Powered Optimization of Bifacial Solar Systems for Enhanced Energy Production and Storage Efficiency Hadi, Husam Ali; Kassem, Abdallah; Amoud, Hassan; Nadweh, Safwan; Ghazaly, Nouby M.; Abdulhasan, Mahmood Jamal
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.v5i4.22100

Abstract

Interest in renewable energy has grown due to increased environmental awareness and concern about climate change. Among the various renewable energy technologies, grid-connected bifacial PV systems are particularly important due to their higher efficiency compared to conventional systems. However, maximizing energy harvesting and storage efficiency remains a challenge for these systems, requiring the use of an efficient charge controller and an appropriate battery. The process of setting charge controller parameters and selecting the best storage technology is complex and requires a thorough study of various operating conditions. The main research contribution of this paper is the development of an efficient optimization methodology to increase the energy production and storage efficiency of the studied systems using optimization algorithms. The imperialist competitive algorithm (ICA) is used in the system design to improve performance through optimal adjustment of charge controller parameters and selection of appropriate storage technology. This decision was based on factors such as energy production from PV panels, energy consumption from loads, and energy storage in batteries. Performance is also evaluated using both the flower pollination algorithm (FPA) and Gray Wolf optimization (GWO) algorithms. The study evaluated system performance by analyzing energy production, storage efficiency, and cost effectiveness. The results showed that the ICA algorithm is effective in improving energy production and storage, resulting in higher energy output, better battery efficiency, and lower system costs. In addition, lithium-ion batteries were identified as the best storage technology. This research demonstrates the potential of the ICA approach to increase efficiency and reduce costs in the PV systems.
Optimization of Load Frequency Control Gain Parameters for Stochastic Microgrid Power System D., Murugesan; K., Jagatheesan; Shah, Pritesh; Sekhar, Ravi
Journal of Robotics and Control (JRC) Vol 4, No 5 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i5.19772

Abstract

Interconnected multi-area microgrids are vital for the future of sustainable and reliable power systems. Effective load frequency control (LFC) is indispensable for ensuring their stable operation. This paper introduces a PID-based LFC system tailored for a stochastic microgrid with diverse power sources, including solar, wind, diesel engine generators, and electrical batteries. The gain parameters of the proposed microgrid PID LFC controller are optimized using genetic algorithms (GA), teaching learning-based optimization (TLBO), and cohort intelligence algorithms. Integral time-multiplied absolute error (ITAE) and integral time-squared error (ITSE) serve as the cost functions for all optimization algorithms. The study evaluated the performance of these optimized microgrid PID LFC configurations under random step load disruptions. Our primary findings reveal that the cohort intelligence-optimized PID LFC controller excels in minimizing computation time (upto 76% and 94% lesser than GA and TLBO respectively) and exhibits superior robust response characteristics. Moreover, the cohort intelligence algorithm requires fewer iterations (upto 66% and 90% lesser than GA and TLBO respectively) and enhances power supply quality within the multi-power microgrid electrical framework, specifically in terms of effective load frequency control.
Injury Prediction in Sports using Artificial Intelligence Applications: A Brief Review Kumar, G. Syam; Kumar, M. Dilip; Reddy, Sareddy Venkata Rami; Kumari, B. V. Seshu; Reddy, Ch. Rami
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.v5i1.20814

Abstract

Avoiding injuries in sports has always depended on historical records and human experience. This is despite using injuries being a major and unsolvable issue. The development of more precise preventative procedures using the now available approaches has been excruciatingly sluggish. The development of artificial intelligence (AI) and machine learning (ML) as potentially valuable procedures to improve damage prevention and recovery procedures has been made possible by technological advances that have made these areas more accessible. This article presents a detailed summary of ML approaches as they have been used to predict and anticipate sports injuries to this point in time. The research conducted over the last five years has been collated, and its results have been untaken. Assuming the present absence of accessible sources, standardized statistics, and a dependence on obsolete deterioration prototypes, it is impossible to draw any definitive conclusions regarding the real-world effectiveness of machine learning in terms of its application to the prediction of sports injuries. However, it has been hypothesized that resolving these two problems would make it possible to deploy innovative, strong machine-learning architectures, which will hasten the process of increasing the state of this area while also offering proven clinical tools.
Addressing Challenges in Dynamic Modeling of Stewart Platform using Reinforcement Learning-Based Control Approach
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.v5i1.20582

Abstract

In this paper, we focus on enhancing the performance of the controller utilized in the Stewart platform by investigating the dynamics of the platform. Dynamic modeling is crucial for control and simulation, yet challenging for parallel robots like the Stewart platform due to closed-loop kinematics. We explore classical methods to solve its inverse dynamical model, but conventional approaches face difficulties, often resulting in simplified and inaccurate models. To overcome this limitation, we propose a novel approach by replacing the classical feedforward inverse dynamic block with a reinforcement learning (RL) agent, which, to our knowledge, has not been tried yet in the context of the Stewart platform control. Our proposed methodology utilizes a hybrid control topology that combines RL with existing classical control topologies and inverse kinematic modeling. We leverage three deep reinforcement learning (DRL) algorithms and two model-based RL algorithms to achieve improved control performance, highlighting the versatility of the proposed approach. By incorporating the learned feedforward control topology into the existing PID controller, we demonstrate enhancements in the overall control performance of the Stewart platform. Notably, our approach eliminates the need for explicit derivation and solving of the inverse dynamic model, overcoming the drawbacks associated with inaccurate and simplified models. Through several simulations and experiments, we validate the effectiveness of our reinforcement learning-based control approach for the dynamic modeling of the Stewart platform. The results highlight the potential of RL techniques in overcoming the challenges associated with dynamic modeling in parallel robot systems, promising improved control performance. This enhances accuracy and reduces the development time of control algorithms in real-world applications. Nonetheless, it requires a simulation step before practical implementations.
Enhancing Pulmonary Disease Classification in Diseases: A Comparative Study of CNN and Optimized MobileNet Architectures Mohammed, Omar Nadhim
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.v5i2.21422

Abstract

Background: Deep learning technologies, especially Convolutional Neural Networks (CNNs), are revolutionizing the field of medical imaging by providing advanced tools for the accurate classification of pulmonary diseases from chest X-ray (CXR) images. In our study, we employed both traditional CNN models and MobileNet architectures to classify various chest diseases using CXR images. Initially, a conventional CNN model was utilized to estab- lish a baseline accuracy. Subsequently, we adopted MobileNet, known for its efficiency in processing image data, to enhance classification performance. To further optimize the system, we applied Energy Valley Optimization (EVO) for hyperparameter tuning. The baseline CNN model achieved an accuracy of 85.91%. The implementation of MobileNet significantly improved this metric, reaching a pre-optimization accuracy of 93.30%. Post-EVO optimization, the accuracy was further enhanced to 94.18%. Comparative analysis of accuracy, precision, recall, F1-score, and ROC curves was conducted to illustrate the impact of hyperparameter tuning on model performance in medical diagnostics. Our findings demonstrate that while standard CNNs provide a solid foundation for CXR image classification, the integration of MobileNet architectures and EVO for hyperparameter adjustment significantly boosts diagnostic accuracy. This advancement in automated medical image analysis could potentially transform the landscape of pulmonary disease diagnosis, offering a more robust framework for accurate and efficient patient care.
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.
Design of Power Control Circuit for Grid-Connected PV System-Based Neural Network Al-Jaboury, Omar N. Rajab; Hamodat, Zaid; Daoud, Raid W.
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.v5i3.20751

Abstract

This research explores the application of neural networks in managing grid- photovoltaic (PV) systems. this paper aims to improve the performance and reliability of PV systems using artificial intelligence capabilities, specifically neural networks. The main emphasis of this system is to control active and reactive power and to track the maximum power point (MPPT). This study introduces an intelligent control technique for fuel cell distributed generation (DG) grid connection inverters. The algorithm allows for the management of both active and reactive power for the unit. The algorithm provides local reactive power compensation, making it economically viable. The controller modeling and performance validation are conducted using MATLAB/Simulink and Sim power system blocks, demonstrating its capacity for enhancing power factor. This makes fuel cell technology a clean, highly controllable, and economically viable option for DG systems. The system maximizes the energy extraction of PV panels and maintains them at their ideal PowerPoint across various environmental conditions. It also raises the voltage from 260 volts to 350 volts.  Simulations and practical evaluations validate the proposed control system. The obtained results indicate that the total harmonic distortion (THD) of the grid current under operating conditions was less than 1.86%. This demonstrates significant improvements in the efficiency and reliability of PV systems. The neural network controller shows remarkable flexibility and the ability to quickly adapt to fluctuations in load and radiation, which contributes to developing a more sustainable and stable energy network.
Optimizing the Tuning of Fuzzy-PID Controllers for Motion Control of Friction Stir Welding Robots Marliana, Eka; Wahjudi, Arif; Nurahmi, Latifah; Batan, I Made Londen; Wei, Guowu
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.v5i4.21697

Abstract

Friction stir welding (FSW) is defined as a solid-state welding method that is required to be accurate, especially for its motion. This requirement can be satisfied by implementing an accurate controller. The aim of this research was to develop an accurate control system based on a fuzzy-proportional integral derivative (PID) controller for parallel manipulator FSW robots. In order to achieve a higher accuracy in motion control, the tuning optimisation process for a fuzzy-PID controller was conducted using a genetic algorithm (GA) and particle swarm optimisation (PSO). The optimisation algorithms were applied to simultane-ously tune the fuzzy rules and output of the membership function from the fuzzy inference system (FIS). The PID controller was designed and tuned using a MATLAB® PID Tuner to obtain the desired response. It was then developed into a fuzzy-PID controller with Sugeno type-1 FIS with 2 inputs and 1 output. The tuning optimisation of the fuzzy-PID controller using GA and PSO was performed to achieve the global minimum integral absolute error (IAE) of the angular velocity. MATLAB® Simulink® was employed to test and simulate the controllers for three motors in the FSW robot model. The IAE values of the PID controller implemented for each motor were 0.03644, 0.04893, and 0.04893. The IAEs of the implemented fuzzy-PID-GA (output and rules) controller were 2.061, 2.048, and 2.048; of the implemented fuzzy-PID-GA (output) controller were 0.03768, 0.05059, and 0.05059; of the fuzzy-PID-PSO (output and rules) controller were 0.01886, 0.0253, and 0.02533; and of the fuzzy-PID-PSO (output) controller were 0.03767, 0.05059, and 0.05059. Therefore, the fuzzy-PID-PSO (output and rules) controller gave the most accurate results and outperformed the others. Keywords—Angular velocity, control system, friction stir welding, fuzzy-PID, genetic algorithm, motion, motor, parallel manipulator, particle swarm optimisation.

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