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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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Momentum-Based Push Recovery Control of Bipedal Robots Using a New Variable Power Reaching Law for Sliding Mode Control Al-Tameemi, Ibrahim; Doan, Duc; Patanwala, Abizer; Agheli, Mahdi
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.v5i5.23379

Abstract

A significant challenge in deploying bipedal robots for human-oriented real-world applications is their ability to maintain balance when externally disturbed. Current momentum-based balance control strategies often exhibit inadequate robustness to disturbances due to reliance on simple proportional controllers and imprecise incorporation of desired angular momentum changes. Furthermore, the sequential activation of momentum and posture correction controllers compromises system stability when confronted with consecutive disturbances. This paper proposes and validates a new Variable Power Reaching Law for Sliding Mode Control (SMC) to enhance the regulation of linear momentum against disturbances. The proposed reaching law adjusts dynamically to the system's errors, ensuring fast convergence and minimal chattering. In this paper, we precisely define the desired angular momentum change in relation to the Center of Pressure (CoP), a crucial stability metric, as well as the desired linear momentum and ground reaction forces.  The null-space method, which allows for simultaneous task execution by using unused degrees of freedom, is employed to ensure effective balance and upright posture without interference. The posture correction control is projected onto the null-space of momentum control. Simulation results confirm that the proposed control system effectively stabilizes the robot against external disturbances, regulating momentum and restoring upright posture. The null-space method proves effective in maintaining balance under multiple disturbances by simultaneously controlling momentum and posture. Comparative evaluations show that our approach outperforms traditional momentum-based controls and nonadaptive reaching laws, reducing CoP fluctuations, managing disturbances up to 117 N, and minimizing chattering and steady-state error. These advancements underscore the potential for deploying bipedal robots in dynamic environments.
Design and Analysis of a Hybrid Intelligent SCARA Robot Controller Based on a Virtual Reality Model Al Mashhadany, Yousif; Abbas, Ahmed K.; Algburi, Sameer; Taha, Bakr Ahmed
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.v5i6.23158

Abstract

SCARA robots have been used in various fields of robotics, such as biomedical engineering, automation, industrial, and gaming. However, our SCARA (Selective Compliance Assembly Robot Arm) VR model stands out with its realistic design and construction assumptions. The VR testing of the robot's motion envelope has facilitated a more precise inverse kinematics solution and verification of the dynamic process. The intelligent controller of this application, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) technique and a classical proportional-integral-derivative (PID) controller, offers an optimized solution to the accuracy problem. The hybrid ANFIS controller starts with the PID setting parameters of the resultant solution. Following thorough testing of the suggested SCARA manipulator with an intelligent controller in a virtual reality environment, researchers recognized the physical system's potential for implementation utilizing multiple control approaches. Despite the intricacy of its design and implementation, the intelligent controller's software ensures that the system runs at top efficiency. This application replicates the user interface of the MATLAB/SIMULINK var (2022b), which produced promising robotics results, demonstrating its trustworthiness as a realistic, intelligent model, and virtual reality was critical in the development of the SCARA manipulator. It digs into the design and analysis of a hybrid intelligent controller for SCARA robots, which are widely used in assembly lines and manufacturing. Finally, the proposed controller combines the best features of an Adaptive Neuro-Fuzzy Inference System (ANFIS) with a conventional proportional-integral-derivative (PID) controller to resolve application accuracy difficulties as efficiently as possible. 
Optimizing Network Security with Machine Learning and Multi-Factor Authentication for Enhanced Intrusion Detection Mahmood, Rafah Kareem; Mahameed, Ans Ibrahim; Lateef, Noor Q.; Jasim, Hasanain M.; Radhi, Ahmed Dheyaa; Ahmed, Saadaldeen Rashid; Tupe-Waghmare, Priyanka
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.v5i5.22508

Abstract

This study examines the utilization of machine learning methodologies and multi-factor authentication (MFA) to bolster network security, specifically targeting network intrusion detection. We analyze the way in which the integration of these technologies effectively tackles existing security concerns and constraints. The research highlights the importance of incorporating energy conservation and environmental impact reduction into security solutions, in addition to traditional cryptography and biometric methods. In addition, we tackle the limitations of centralized systems, such as vulnerabilities to security breaches and instances of system failures. The study examines different security models, encompassing categories, frameworks, consensus protocols, applications, services, and deployment goals in order to determine their impact on network security. In addition, we offer a detailed comparison of seven machine learning models, showcasing their effectiveness in enhancing network intrusion detection and overall security. The objective of this study is to provide in-depth understanding and actionable suggestions for utilizing machine learning with MFA (Multi-Factor Authentication) to enhance network defensive tactics.
Enhanced Total Harmonic Distortion Optimization in Cascaded H-Bridge Multilevel Inverters Using the Dwarf Mongoose Optimization Algorithm Salih, Sinan Q.; Mejbel, Basim Ghalib; Ahmad, B. A.; Taha, Taha A.; Bektaş, Yasin; Aldabbagh, Mohammed M; Hussain, Abadal-Salam T.; Hashim, Abdulghafor Mohammed; Radhi, Ahmed Dheyaa
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.v5i6.23548

Abstract

Total harmonic distortion (THD) is one of the most essential parameters that define the operational efficiency and power quality in electrical systems applied to solutions like cascaded H-bridge multilevel inverters (CHB-MLI). The reduction of THD is crucial due to the fact that improving the system’s power quality and minimizing the losses are key for performance improvement. The purpose of this work is to introduce a new DMO-based approach to optimize the THD of the output voltage in a three-phase nine-level CHB-MLI. The proposed DMO algorithm was also subjected to intense comparison with two benchmark optimization techniques, namely Genetic Algorithm and Particle Swarm Optimization with regards to three parameters, namely convergence rate, stability, and optimization accuracy. A series of MATLAB simulations were run to afford the evaluation of each algorithm under a modulation index of between 0.1 and 1.0. The outcome of the experiment amply proves that in comparison with THD minimization for the given OP, the DMO algorithm was significantly superior to both RSA-based GA and PSO algorithms in their ability to yield higher accuracy while requiring lesser computational time. Consequently, this work could expand the application of the DMO algorithm as a reliable and effective means of enhancing THD in CHB-MLIs as well as advancing the overall quality of power systems in different electrical power networks.
Voltage Regulation and Power Management of DC Microgrid with Photovoltaic/Battery Storage System Using Flatness Control Method Mutlag, Ashraf Abdualateef; Abd, Mohommed Kdair; Shneen, Salam Waley
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.v5i6.22530

Abstract

This research aims to propose a power management strategy (PMS) based on the flatness control method for a stand-alone DC microgrid system. The goal of the proposed strategy is to create an efficient PMS using nonlinear flatness theory in order to provide a constant DC bus voltage and the best possible power-sharing mechanism between the battery and the PV array. A maximum power point tracking (MPPT) technique based on an artificial neural network (ANN) to optimize the PV's power. Moreover, the suggested PMS technique was tested in a simulation environment based on MATLAB®/Simulink. The obtained results demonstrate that the proposed PMS method can stabilize the bus voltage under variations in load or solar radiation. Additionally, the PMS method reduced bus voltage spikes and guaranteed good power quality, which extended the battery's lifespan and increased its efficiency. Also, the proposed approach outperforms the standard PI approach in terms of tracking efficiency and has a lower rate of overshoot in the bus voltage under different load scenarios. Therefore, the method is effective when compared with the classical PI approach. The overshoot in the PI method is 58 V, while the overshoot in the DC voltage is 5 V in the proposed method. The tracking speed of the proposed system is very low, and the slower speed was observed in the classical method, and the rise time of PI was 7.9ms, while the proposed method equals 2.2ms.
Application of Sentiment Analysis as an Innovative Approach to Policy Making: A review Firdaus, Asno Azzawagama; Saputro, Joko Slamet; Anwar, Miftahul; Adriyanto, Feri; Maghfiroh, Hari; Ma'arif, Alfian; Syuhada, Fahmi; Hidayat, Rahmad
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.v5i6.22573

Abstract

This literature review comprehensively explains the role of sentiment analysis as a policymaking solution in companies, organizations, and individuals. The issue at hand is how sentiment analysis can be effectively applied in decision making. The solution is to integrate sentiment analysis with the latest NLP trends. The contribution of this research is the assessment of 100-200 recent studies in the period 2020-2024 with a sample of more than 5,000 data, as well as the impact of the resulting policy recommendations. The methods used include evaluation of techniques such as Deep Learning, lexicon-based, and Machine Learning, using evaluation matrices such as F1-score, precision, recall, and accuracy. The results showed that Deep Learning techniques achieved an average accuracy of 93.04%, followed by lexicon-based approaches with 88.3% accuracy and Machine Learning with 83.58% accuracy. The findings also highlight the importance of data privacy and algorithmic bias in supporting more responsive and data-driven policymaking. In conclusion, sentiment analysis is reliable in areas such as e-commerce, healthcare, education, and social media for policy-making recommendations. However, special attention should be paid to challenges such as language differences, data bias, and context ambiguity which can be addressed with models such as mBERT, model auditing, and proper tokenization.
Integrated Deep Hybrid Learning Model Upon Spinach Leaf Classification and Prediction with Pristine Accuracy Elumalai, Meganathan; Fernandez, Terrance Frederick
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.v5i5.22546

Abstract

Over the years, Agriculture has been a mainstay of life for Indians and about half the working population of Tamil Nadu. Spinach is an integral part of everyone’s meal and its nutrient content is higher than other veggies. The nutrients are unique for varied varieties so there is a dire need to classify them and thus to predict them. Furthermore, exactitude prediction leads to easy detection of spinach leaves. In this work, we selected 5 varieties of spinach leaves populated under a huge dataset. We implemented the same employing a Deep Hybrid approach which is a fusion of conventional Machine Learning with state-of-the-art Deep Learning using Orange toolkit. Out of the plethora of these AI Domaine approaches, four classifiers, such as Support Vector Machine (SVM), k- Nearest Neighbour(kNN), Random Forest (RF), and Neural Network (NN) were chosen and implemented. Existing methods using these algorithms have achieved promising results, with individual accuracies of 98.80% (RF), 98.20% (KNN), 99.9% (NN), and 99.60% (SVM). However, the IDHLM aims to surpass these individual performances by integrating them into a cohesive framework. This approach leverages each algorithm's complementary strengths to achieve even higher classification accuracy. The abstract concludes by highlighting the potential of the IDHLM for achieving pristine accuracy in spinach leaf classification.
Soft Actuator Based on a Novel Variable Stiffness Compound Extensor Bending-Pneumatic Artificial Muscle (CEB-PAM): Design and Mathematical Model Al-Mayahi, Wafaa; Al-Fahaam, Hassanin
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.v5i2.21127

Abstract

Soft robots have gained prominence in various fields in recent years, particularly in medical applications such as rehabilitation, due to their numerous advantages. The primary building blocks of a soft robot are often pneumatic artificial muscles (PAM). The Extensor PAM (EPAM), including Extensor Bending PAM (EB-PAM), is characterized by its low stiffness, and because stiffness is important in many robotic applications, for example, in rehabilitation, the degree of disability varies from one person to another, such as spasticity, weakness, and contracture. Therefore, it was necessary to provide an actuator with variable stiffness whose stiffness can be controlled to provide the appropriate need for each person, this study presents a new design for the EB-PAM that combines the EB-PAM and contractor PAM (CPAM), It has higher stiffness than traditional EPAM, A stiffness of over 850 N/m was achieved, whereas EB-PAM only reached a stiffness of less than 450 N/m, it is also possible to change its stiffness at a specific bending angle. It is also possible to obtain fixed stiffness at different angles.  A mathematical model was developed to calculate the output force of the new muscle by calculating its size and the pressure applied to it and comparing the model with experimental results. The mathematical model was enhanced by calculating the wasted energy consumed by the actuator before the bladder begins to expand, and also by calculating the thickness of the bladder and the sleeve. To make the muscle lighter, cheaper, and work under low pressures, balloons were used in manufacturing, offering practical advantages for soft robotic applications.
Mathematical Model of a Robot-spider for Group Control Synthesis: Derivation and Validation Kravchenko, Viktor V.; Efremov, Artem A.; Zhilenkov, Anton A.; Kozlov, Vladimir N.; Kristina, Grycshenko; Anton, Popov; Mark, Psarev; Mikhail, Serebryakov
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.v4i6.20325

Abstract

A six-legged spider robot is a complex object from the point of view of the problem of synthesizing a system for controlling its movement. To synthesize an advanced control system for such a robot, which must solve non-trivial problems of overcoming obstacles, functioning under conditions of external disturbances, etc., we first solve the problem of synthesizing an information model of the object, on the basis of which its control system will subsequently be built.The paper compares two methods for synthesizing the information model of a six-legged spider-robot. In the first method, an information model is automatically synthesized from a CAD model of a spider-robot in a MATLAB-based graphical programming environment Simulink. In the second method, the information model is synthesized in the environment of dynamic modeling of technical systems SimInTech on the basis of a system of differential equations in the Cauchy form. Control loops and external influences are added to the information models synthesized in each of the modeling environments. The study showed that each of the resulting models has both its own individual advantages and disadvantages. They are mainly related to taking into account the mutual influence of various blocks of models on each other. It is shown that, in the end, the two models complement each other and make it possible to obtain an advanced basis for further synthesis of the motion control system.The results obtained in this work make it possible to use information models as a basis for the development of a control system for a physical model of a six-legged spider-robot, printed on a 3D printer and assembled on the basis of the Arduino hardware platform.
Nonlinear Model Predictive Control-based Collision Avoidance for Mobile Robot Ismael, Omar Y.; Almaged, Mohammed; Abdulla, Abdulla Ibrahim
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.v5i1.20615

Abstract

This work proposes an efficient and safe single-layer Nonlinear Model Predictive Control (NMPC) system based on LiDAR to solve the problem of autonomous navigation in cluttered environments with previously unidentified static and dynamic obstacles of any shape. Initially, LiDAR sensor data is collected. Then, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, is used to cluster the (Lidar) points that belong to each obstacle together. Moreover, a Minimum Euclidean Distance (MED) between the robot and each obstacle with the aid of a safety margin is utilized to implement safety-critical obstacle avoidance rather than existing methods in the literature that depend on enclosing the obstacles with a circle or minimum bounding ellipse. After that, to impose avoidance constraints with feasibility guarantees and without compromising stability, an NMPC for set-point stabilization is taken into consideration with a design strategy based on terminal inequality and equality constraints. Consequently, numerous obstacles can be avoided at the same time efficiently and rapidly through unstructured environments with narrow corridors.  Finally, a case study with an omnidirectional wheeled mobile robot (OWMR) is presented to assess the proposed NMPC formulation for set-point stabilization. Furthermore, the efficacy of the proposed system is tested by experiments in simulated scenarios using a robot simulator named CoppeliaSim in combination with MATLAB which utilizes the CasADi Toolbox, and Statistics and Machine Learning Toolbox. Two simulation scenarios are considered to show the performance of the proposed framework. The first scenario considers only static obstacles while the second scenario is more challenging and contains static and dynamic obstacles. In both scenarios, the OWMR successfully reached the target pose (1.5m, 1.5m, 0°) with a small deviation. Four performance indices are utilized to evaluate the set-point stabilization performance of the proposed control framework including the steady-state error in the posture vector which is less than 0.02 meters for position and 0.012 for orientation, and the integral of norm squared actual control inputs which is 19.96 and 21.74 for the first and second scenarios respectively. The proposed control framework shows a positive performance in a narrow-cluttered environment with unknown obstacles.

Page 55 of 71 | Total Record : 708

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