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Alfian Ma'arif
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International Journal of Robotics and Control Systems
Published by Association for Scientific Computing Electronics and Engineering
ISSN : -     EISSN : 27752658     DOI : https://doi.org/10.31763/ijrcs
Core Subject : Engineering,
Control & Systems Engineering Electrical & Electronics Engineering
International Journal of Robotics and Control Systems is open access and peer-reviewed international journal that invited academicians (students and lecturers), researchers, scientists, and engineers to exchange and disseminate their work, development, and contribution in the area of robotics and control technology systems experts. Its scope includes Industrial Robots, Humanoid Robot, Flying Robot, Mobile Robot, Proportional-Integral-Derivative (PID) Controller, Feedback Control, Linear Control (Compensator, State Feedback, Servo State Feedback, Observer, etc.), Nonlinear Control (Feedback Linearization, Sliding Mode Controller, Backstepping, etc.), Robust Control, Adaptive Control (Model Reference Adaptive Control, etc.), Geometry Control, Intelligent Control (Fuzzy Logic Controller (FLC), Neural Network Control), Power Electronic Control, Artificial Intelligence, Embedded Systems, Internet of Things (IoT) in Control and Robot, Network Control System, Controller Optimization (Linear Quadratic Regulator (LQR), Coefficient Diagram Method, Metaheuristic Algorithm, etc.), Modelling and Identification System.
Arjuna Subject : Teknik (semua kategori) - Teknik Kontrol dan Sistem
Articles 361 Documents
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Comparative Study of Takagi-Sugeno-Kang and Madani Algorithms in Type-1 and Interval Type-2 Fuzzy Control for Self-Balancing Wheelchairs Manutsawee Kiew-ong-art; Phichitphon Chotikunnan; Anantasak Wongkamhang; Rawiphon Chotikunnan; Anuchit Nirapai; Pariwat Imura; Manas Sangworasil; Nuntachai Thongpance; Anuchart Srisiriwat
International Journal of Robotics and Control Systems Vol 3, No 4 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i4.1154

Abstract

This study examines the effectiveness of four different fuzzy logic controllers in self-balancing wheelchairs. The controllers under consideration are Type-1 Takagi-Sugeno-Kang (TSK) FLC, Interval Type-2 TSK FLC, Type-1 Mamdani FLC, and Interval Type-2 Mamdani FLC. A MATLAB-based simulation environment serves for the evaluation, focusing on key performance indicators like percentage overshoot, rise time, settling time, and displacement. Two testing methodologies were designed to simulate both ideal conditions and real-world hardware limitations. The simulations reveal distinct advantages for each controller type. For example, Type-1 TSK excels in minimizing overshoot but requires higher force. Interval Type-2 TSK shows the quickest settling times but needs the most force. Type-1 Mamdani has the fastest rise time with the lowest force requirement but experiences a higher percentage of overshoot. Interval Type-2 Mamdani offers balanced performance across all metrics. When a 2.7 N control input cap is imposed, Type-2 controllers prove notably more efficient in minimizing overshoot. These results offer valuable insights for future design and real-world application of self-balancing wheelchairs. Further studies are recommended for the empirical testing and refinement of these controllers, especially since the initial findings were limited to four-wheeled self-balancing robotic wheelchairs.
Integrated Modelling and Control of Linear Actuator Based Automatic Pedal Pressing Mechanism for Low-Speed Driving in a Road Traffic Delay Azrul Azim Abdullah Hashim; Salmiah Ahmad; Nor Maniha Abd Ghani; Ahmad Nor Kasruddin Nasir
International Journal of Robotics and Control Systems Vol 3, No 3 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i3.1058

Abstract

Sitting in traffic congestion for hours in a posture that requires recurrent actions of manually pressing the pedal and braking excessively can result in fatigue, especially on the driver's leg and back. This fatigue can have long-term implications and adversely affect the driver's health. Thus, this paper aims to model and develop a control system that utilizes a linear actuator to replace the leg activities involved in pressing and releasing the brake pedal. This approach, combined with the implementation of a PID controller, offers a novel solution to control the vehicle speed by integration with the linear actuator that focus on low-speed driving condition. The design process begins with creating a 3D model using SolidWorks to visualize the movement of the linear actuator and Pedal subsystem. This model is then connected to Matlab-Simulink, where a PID controller is implemented and integrated into the electrical circuit to control the actuator's movement. Integration with the vehicle dynamic model enables a comprehensive analysis of the system's behavior on the vehicle dynamics. This research compares the trial and error method with the Matlab tuner for implementing the PID controller. The performance of the system will be evaluated based on the steady state error, overshoot, rise time, and settling time. The results demonstrate that the Matlab tuner outperforms trial and error method by achieving a faster response and significantly reducing steady state error during robustness testing. With the integration of the linear actuator, the system is capable of tracking the desired speed and has the potential to replace the leg activities involved in pressing and releasing the brake pedal. For future work, validating the proposed mechanism with a physical prototype of the linear actuator and pedal using hardware-in-the-loop techniques poses a challenge, as hardware constraints may vary with different environments.
Microgrid Energy Management using Weather Forecasts: Case Study, Discussion and Challenges Mst. Sumi Akter; Asm Mohaimenul Islam; Md Maruf Hasan
International Journal of Robotics and Control Systems Vol 3, No 4 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i4.1000

Abstract

The main objective of this study is to demonstrate the integration of weather forecasts which can lead to a significant reduction in energy costs and carbon emissions while ensuring the reliability of the microgrid operation. By serving a small area or a particular building, the incorporation of weather forecasts can considerably increase the efficiency of microgrid energy management. The planning and operation of microgrids can be greatly improved by using weather predictions, which give useful information about upcoming weather conditions. By forecasting future energy demand and supply based on meteorological conditions, Microgrid Energy Management (MEM) is utilized to optimize the energy management decisions in microgrid systems. Making better choices regarding energy generation, storage, and consumption may be aided by the incorporation of weather forecasts, which can offer a more precise and trustworthy estimate of the energy demand and supply. This strategy can result in increased energy efficiency, decreased energy prices, and decreased carbon emissions, all of which are important goals for contemporary power systems. A promising approach for raising energy effectiveness and lowering greenhouse gas emissions in contemporary power networks is MEM. The incorporation of weather forecasts into MEM can improve decision-making regarding energy management by giving a better insight of future energy demand and supply. This essay examines the advantages and disadvantages of using weather forecasts in MEM through the presentation of a case example. By providing valuable information about future weather conditions, weather forecasts this review explain the Optimized Renewable Energy Integration, Improved Energy Storage Utilization, Load Shifting and Demand Response, Efficient Grid Management for reducing reliance on fossil fuels and lowering energy cost and carbon emissions. In order to address the issues related with MEM employing weather forecasts, this study offers potential fixes for increasing the accuracy of weather forecasts and emphasizes the necessity for more research in this area.
Evaluation of Stochastic Gradient Descent Optimizer on U-Net Architecture for Brain Tumor Segmentation Purwono Purwono; Iis Setiawan Mangkunegara
International Journal of Robotics and Control Systems Vol 3, No 3 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i3.1104

Abstract

A brain tumor is a type of disease that is quite dangerous in the world. This disease is one of the main causes of human death and has a high risk of recurrence. There are several types of brain tumor locations such as edema, necrosis to elevation. Segmenting the location of this disease is important to do to support faster recovery efforts. The Convolutional Neural Network (CNN) algorithm, which is part of the deep learning method, can be an alternative to this segmentation effort. The U-Net architecture is part of the CNN algorithm which specifically works on medical image segmentation. This study experimented to build a special U-Net architecture for medical image segmentation that had been optimized with SGD. The data used is BraTS2020O which contains a collection of MRI data. This optimization aims to improve the performance of the U-net architecture for segmenting brain tumor images. The results of the study show that the SGD optimization carried out has succeeded in providing better performance than previous studies. This can be seen from the performance value obtained at 0.9879. This accuracy value indicates an increase in accuracy from previous studies. High accuracy indicates that the SGD-optimized model has good segmentation prediction performance.
Optimized PID Controller of DC-DC Buck Converter based on Archimedes Optimization Algorithm Ling Kuok Fong; Muhammad Shafiqul Islam; Mohd Ashraf Ahmad
International Journal of Robotics and Control Systems Vol 3, No 4 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i4.1113

Abstract

This research assesses the suitability of the Archimedes Optimization Algorithm (AOA) as a metaheuristic technique to fine-tune a PID controller in a closed-loop DC-DC buck converter. The converter's core function is to regulate output voltage, ensuring stability despite load fluctuations and input voltage changes.  The operational effectiveness of the converter hinges significantly on the gain settings of the PID controller and determining the optimal gain setting for the PID controller is a non-trivial task. For robust performance, the PID controller necessitates optimal gain settings, attainable through metaheuristic optimization. The algorithm aids in identifying ideal proportional, integral, and derivative gains based on varying load conditions. Leveraging the metaheuristic algorithm, the PID controller is optimized to minimize voltage errors, reduce overshoot, and enhance response time. The proposed PID controller, optimized using AOA, is contrasted with PID controllers tuned via alternative algorithms including the hybrid Nelder-Mead method (AEONM), artificial ecosystem-based optimization (AEO), differential evolution (DE), and particle swarm optimizer (PSO). Performance evaluation involves injecting a voltage disturbance into the buck converter with load changes of up to 20%. Results demonstrate the superiority of the AOA-optimized PID controller in voltage recovery.  It demonstrates a faster response time and outstanding voltage regulation performance, while also exhibiting minimal performance degradation during load changes. This study concludes that the AOA optimization algorithm surpasses other methods in tuning the PID controller for closed-loop DC-DC buck converters.
Comparison of Feature Extraction with PCA and LTP Methods and Investigating the Effect of Dimensionality Reduction in the Bat Algorithm for Face Recognition Azita Mousavi; Hadis Arefanjazi; Mona Sadeghi; Ali Mojarrad Ghahfarokhi; Fatemehalsadat Beheshtinejad; Mahsa Madadi Masouleh
International Journal of Robotics and Control Systems Vol 3, No 3 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i3.1057

Abstract

Face recognition is one of the challenging subjects of image processing. Facial recognition is often a biometric method that basically uses faces to recognize people. The face recognition system consists of three main steps: finding the face in the image, feature extraction and classification. The face recognition system faces challenges such as changes in lighting, changes in age, changes in facial expressions, etc. One of the important issues in this system is the algorithm execution speed. For this purpose, the dimensions of the feature vectors should be small enough, especially when the database is large. Since the face recognition system must be performed on a wide range of databases, dimensionality reduction techniques are required to reduce time and increase accuracy. Dimension reduction methods are used for this purpose. Two methods of dimensionality reduction, including LTP and PCA, are given in this research. In this research, first, the LTP feature vectors are extracted from the face image, and then the effective features are selected using the Bat algorithm. Therefore, this algorithm has three main phases of feature extraction, feature selection and classification. This algorithm is implemented on the ORL database, which contains 400 images of 40 different people with a size of 112×92 pixels. In addition to reducing the time required for testing, the proposed method has provided a very good accuracy of 99%.
Optimizing Three-Tank Liquid Level Control: Insights from Prairie Dog Optimization Davut Izci; Serdar Ekinci
International Journal of Robotics and Control Systems Vol 3, No 3 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i3.1116

Abstract

The management of chemical process liquid levels poses a significant challenge in industrial process control, affecting the efficiency and stability of various sectors such as food processing, nuclear power generation, and pharmaceutical industries. While Proportional-Integral-Derivative (PID) control is a widely-used technique for maintaining liquid levels in tanks, its efficacy in optimizing complex and nonlinear systems has limitations. To overcome this, researchers are exploring the potential of metaheuristic algorithms, which offer robust optimization capabilities. This study introduces a novel approach to liquid level control using the Prairie Dog Optimization (PDO) algorithm, a metaheuristic algorithm inspired by prairie dog behavior. The primary objective is to design and implement a PID-controlled three-tank liquid level system that leverages PDO to regulate liquid levels effectively, ensuring enhanced stability and performance. The performance of the proposed system is evaluated using the ZLG criterion, a time domain metric-based objective function that quantifies the system's efficiency in maintaining desired liquid levels. Several analysis techniques are employed to understand the behavior of the system. Convergence curve analysis assesses the PDO-controlled system's convergence characteristics, providing insights into its efficiency and stability. Statistical analysis determines the algorithm's reliability and robustness across multiple runs. Stability analysis from both time and frequency response perspectives further validates the system's performance. A comprehensive comparison study with state-of-the-art metaheuristic algorithms, including AOA-HHO, CMA-ES, PSO, and ALC-PSODE, is conducted to benchmark the performance of PDO. The results highlight PDO's superior convergence, stability, and optimization capabilities, establishing its efficacy in real-world industrial applications. The research findings underscore the potential of PDO in PID control applications for three-tank liquid level systems. By outperforming benchmark algorithms, PDO demonstrates its value in industrial control scenarios, contributing to the advancement of metaheuristic-based control techniques and process optimization. This study opens avenues for engineers and practitioners to harness advanced control solutions, thereby enhancing industrial processes and automation.
Evolution, Design, and Future Trajectories on Bipedal Wheel-legged Robot: A Comprehensive Review Zulkifli Mansor; Addie Irawan; Mohammad Fadhil Abas
International Journal of Robotics and Control Systems Vol 3, No 4 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i4.1107

Abstract

This comprehensive review delves into the realm of bipedal wheel-legged robots, focusing on their design, control, and applications in assistive technology and disaster mitigation. Drawing insights from various fields such as robotics, computer science, and biomechanics, it offers a holistic understanding of these robots' stability, adaptability, and efficiency. The analysis encompasses optimization techniques, sensor integration, machine learning, and adaptive control methods, evaluating their impact on robot capabilities. Emphasizing the need for adaptable, terrain-aware control algorithms, the review explores the untapped potential of machine learning and soft robotics in enhancing performance across diverse operational scenarios. It highlights the advantages of hybrid models combining legged and wheeled mobility while stressing the importance of refining control frameworks, trajectory planning, and human-robot interactions. The concept of integrating soft and compliant mechanisms for improved adaptability and resilience is introduced. Identifying gaps in current research, the review suggests future directions for investigation in the fields of robotics and control engineering, addressing the evolution and terrain adaptability of bipedal wheel-legged robots, control, stability, and locomotion, as well as integrated sensory and perception systems, microcontrollers, cutting-edge technology, and future design and control directions.
Identification and Control of Epidemic Disease Based Neural Networks and Optimization Technique Ahmed J. Abougarair; Shada E. Elwefati
International Journal of Robotics and Control Systems Vol 3, No 4 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i4.1151

Abstract

Developing effective strategies to contain the spread of infectious diseases, particularly in the case of rapidly evolving outbreaks like COVID-19, remains a pressing challenge. The Susceptible-Infected-Recovery (SIR) model, a fundamental tool in epidemiology, offers insights into disease dynamics. The SIR system exhibits complex nonlinear relationships between the input variables (e.g., population, infection rate, recovery rate) and the output variables (e.g., the number of infected individuals over time). We employ Recurrent Neural Networks (RNNs) to model the SIR system due to their ability to capture sequential dependencies and handle time-series data effectively. RNNs, with their ability to model nonlinear functions, can capture these intricate relationships, enabling accurate predictions and understanding of the dynamics of the system. Additionally, we apply the Pontryagin Minimum Principle (PMP) based different control strategies to formulate an optimal control approach aimed at maximizing the recovery rate while minimizing the number of affected individuals and achieving a balance between minimizing costs and satisfying constraints. This can include optimizing vaccination strategies, quarantine measures, treatment allocation, and resource allocation. The findings of this research indicate that the proposed modeling and control approach shows potential for a comprehensive analysis of viral spread, providing valuable insights and strategies for disease management on a global level. By integrating epidemiological modeling with intelligent control techniques, we contribute to the ongoing efforts aimed at combating infectious diseases on a larger scale.
Finite-Time Synchronization of the Rabinovich and Rabinovich-Fabrikant Chaotic Systems for Different Evolvable Parameters Edwin A. Umoh; Alfian Ma'arif; Omokhafe J. Tola; Iswanto Suwarno; Muhammed N. Umar
International Journal of Robotics and Control Systems Vol 3, No 4 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i4.1149

Abstract

This paper addresses the challenge of synchronizing the dynamics of two distinct 3D chaotic systems, specifically the Rabinovich and Rabinovich-Fabrikant systems, employing a finite-time synchronization approach. These chaotic systems exhibit diverse characteristics and evolving chaotic attractors, influenced by specific parameters and initial conditions. Our proposed low-cost finite-time synchronization method leverages the signum function's tracking properties to facilitate controlled coupling within a finite time frame. The design of finite-time control laws is rooted in Lyapunov stability criteria and lemmas. Numerical experiments conducted within the MATLAB simulation environment demonstrate the successful asymptotic synchronization of the master and slave systems within finite time. To assess the global robustness of our control scheme, we applied it across various system parameters and initial conditions. Remarkably, our results reveal consistent synchronization times and dynamics across these different scenarios. In summary, this study presents a finite-time synchronization solution for non-identical 3D chaotic systems, showcasing the potential for robust and reliable synchronization under varying conditions.

Page 14 of 37 | Total Record : 361

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