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Control Systems and Optimization Letters
Published by Peneliti Teknologi Teknik Indonesia
ISSN : -     EISSN : 29856116     DOI : 10.59247/csol
Core Subject : Science, Engineering,
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
Control Systems and Optimization Letters is an open-access journal offering authors the opportunity to publish in all fundamental and interdisciplinary areas of control and optimization, rapidly enabling a safe and sustainable interconnected human society. Control Systems and Optimization Letters accept scientifically sound and technically correct papers and provide valuable new knowledge to the mathematics and engineering communities. Theoretical work, experimental work, or case studies are all welcome. The journal also publishes survey papers. However, survey papers will be considered only with prior approval from the editor-in-chief and should provide additional insights into the topic surveyed rather than a mere compilation of known results. Topics on well-studied modern control and optimization methods, such as linear quadratic regulators, are within the scope of the journal. The Control Systems and Optimization Letters focus on control system development and solving problems using optimization algorithms to reach 17 Sustainable Development Goals (SDGs). The scope is linear control, nonlinear control, optimal control, adaptive control, robust control, geometry control, and intelligent control.
Arjuna Subject : Teknik (semua kategori) - Teknik Kontrol dan Sistem
Articles 118 Documents
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Comparative Analysis of Different Control Strategies and Materials for a Community Microgrid - A Case Study Pranto, Jubaer Akon; Kadir, Md Moin; Khan, Md. Yakub Ali
Control Systems and Optimization Letters Vol 2, No 2 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i2.118

Abstract

The main objective of this study is to conduct a comparative analysis of various control strategies and materials used in the operation of community microgrids. An analysis that contrasts various methods for managing a microgrid's operations in a community context is called comparison research on control strategies for community microgrids. The study's objectives are to evaluate the benefits and drawbacks of various control systems and to pinpoint the best approach for maximizing the microgrid's performance and materials for microgrid. The study compares various control strategies, including islanded mode control, hybrid mode control, and grid-connected mode control. Advanced strategies that integrate economic dispatch with optimal power flow are also evaluated. A comparison is done taking into account variables including resilience, cost-effectiveness, efficiency, stability, and reliability. The findings provide valuable insights into the optimal control approach tailored to the specific needs of community microgrids, considering available resources, local energy consumption patterns, and other critical factors. The report also emphasizes the advantages of employing sophisticated control systems, including enhanced resilience and flexibility, increased cost-effectiveness, and improved integration with the main grid. In general, the comparative analysis of different control strategies for community micro-grids offers insightful knowledge to scholars, engineers, and decision-makers engaged in micro-grid design and operation, assisting in enhancing the efficiency and dependability of these systems for the good of communities.
Impact of Inertial and External Forces on Joint Dynamics of Robotic Manipulator: Experimental Insights Sharkawy, Abdel-Nasser
Control Systems and Optimization Letters Vol 3, No 1 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i1.163

Abstract

In this paper, the effect of the inertial and external forces applied on the links of the robotic manipulator is studied and investigated on the manipulator joints’ parameters through experimental analysis. For this investigation and experiments, KUKA LWR manipulator is used and structured as a 2-DOF manipulator. Experimental work is carried out by commanding a sinusoidal joint motion to the two joints of the manipulator. Different scenarios are studied such as motion with free of collisions, motion with collision on the link between the two joints of the manipulator, motion with collision on the end-effector, and motions with different constant joint speeds. The diagrams of the position, velocity, acceleration, and torque of the manipulator joints are obtained and recorded from KUKA robot controller and then investigated and evaluated. The results reveal that during a motion free of collision, small spikes are found on the signals of the joint position, velocity, acceleration, and torques. These spikes resulted from the inertial forces applied on the joint. During a motion with collision, the signals of joint position, velocity, acceleration, and torque are highly affected due to the collision, inertial forces, and friction. During a collision on the end-effector, the torques of both joints are highly affected. During a collision on a link between the two joints, the torque of the first joint is highly affected, and the torque of the second joint is slightly affected. When the speed of the joint is increased, the torque signal is highly affected. These findings provide insights into the dynamic behavior of robotic manipulators under external forces, with implications for improving control algorithms and collision detection systems.
Potential Applications and Limitations of Artificial Intelligence in Remote Sensing Data Interpretation: A Case Study Hossain, Ikram; Islam, Md Monirul; Martin, Md. Hasnat Hanjala
Control Systems and Optimization Letters Vol 2, No 3 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i3.128

Abstract

This research aims to comprehensively review the applications and limitations of artificial intelligence (AI) in interpreting remote sensing data, highlighting its potential through a detailed case study. AI technologies, particularly machine learning and deep learning, have shown remarkable promise in enhancing the accuracy and efficiency of data interpretation tasks in remote sensing, such as anomaly detection, change detection, and land cover classification. AI-driven analysis has a lot of options because to remote sensing, which can gather massive amounts of environmental data via drones, satellites, and other aerial platforms. AI approaches, in particular machine learning and deep learning, have demonstrated potential to improve the precision and effectiveness of data interpretation tasks, including anomaly identification, change detection, and land cover classification. Nevertheless, the research also points to a number of drawbacks, including challenges related to data quality, the need for large labeled datasets, and the risk of model overfitting. Furthermore, the intricacy of AI models can occasionally result in a lack of transparency, which makes it challenging to understand and accept the outcomes. The case study emphasizes the necessity for a balanced strategy that makes use of the advantages of both AI and conventional techniques by highlighting both effective applications of AI in remote sensing and areas where traditional methods still perform better than AI. This research concludes that while AI holds significant potential for advancing remote sensing data interpretation, careful consideration of its limitations is crucial for its effective application in real-world scenarios.
Impact of IoT and Embedded System on Semiconductor Industry A Case Study Tareq, Abdulla Al; Rana, Md Juel; Mostofa, Md Riad; Rahman, Md Sadiqur
Control Systems and Optimization Letters Vol 2, No 2 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i2.111

Abstract

The fast growth of Internet of Things (IoT) devices and advances in embedded systems are causing major changes in the semiconductor business. Examining major trends, obstacles, and opportunities, this case study investigates how embedded systems, and the Internet of Things are affecting the semiconductor business. It explores how semiconductor technology has developed to satisfy the needs of Internet of Things applications, focusing on connectivity options, low-power design, and sensor integration. The report examines how technological advancements, competitive tactics, and market dynamics are reshaping the semiconductor industry. Because IoT devices are frequently integrated into wearable technology or deployed in remote areas, power efficiency becomes essential. Using sophisticated low-power design approaches and power management features, semiconductors must be engineered to function with the least amount of power possible. Miniaturization of semiconductor technology is required due to the requirement for increasingly compact and smaller devices in embedded systems and the Internet of Things. The strategic implications for semiconductor businesses navigating this dynamic ecosystem are highlighted by drawing insights from industry data, market analysis, and case studies. Through an analysis of actual cases and market developments, this case study offers insightful insights into the changing role of semiconductor technology in facilitating the Internet of Things revolution.
Hybrid Adaptive Backstepping Sliding Mode Controller of Permanent Magnet Linear Synchronous Motors Maamar, Yahiaoui; Alnami, Hashim; Elzein, I. M.; Benameur, Afif; Brahim, Brahimi; Mohamed, Horch; Mahmoud, Mohamed Metwally
Control Systems and Optimization Letters Vol 2, No 3 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i3.165

Abstract

This paper tackles the tracking position control dilemma of permanent magnet linear synchronous motors with parameter uncertainties and load force disturbance. Adaptive nonlinear backstepping control augmented with sliding mode control (SMC) is proposed to solve the problem of load force distribution. The backstepping is a recursive control technique where its stability is ensured at each step. However, its sensitivity to uncertainties, disturbances, and electromagnetic noise leads to unwanted performances. SMC is a well-known nonlinear robust approach for uncertain dynamical systems and reduces its parametric adaptive laws.  However, implementing this technique in real-time applications is stopped by its main shortcoming, the undesirable chattering phenomenon.  The saturation function is used to reduce the chattering phenomenon.  The incorporation of these approaches is a promising solution to provide a suitable position tracking of PMLSM in the presence of parameter uncertainties and load force disturbance. The simulation tests have been performed on the PMLSM system to prove the effectiveness and robustness of the proposed controller law.  The results highlighted satisfactory position tracking performance in transient conditions and steady-state and under different load force disturbances.
Motion System of a Four-Wheeled Robot Using a PID Controller Based on MPU and Rotary Encoder Sensors Sagita, Muhamad Rian; Ma’arif, Alfian; Furizal, Furizal; Rekik, Chokri; Caesarendra, Wahyu; Majdoubi, Rania
Control Systems and Optimization Letters Vol 2, No 2 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i2.150

Abstract

This research addresses the challenge of developing an effective motion system for a four-wheeled omnidirectional robot configured with wheels at a 45-degree angle, allowing for holonomic movement—motion in any direction without changing orientation. In this system, inverse kinematics calculates each wheel's angular velocity to optimize movement. PID control is implemented to stabilize motor speeds, while odometry guides and determines the robot’s position using initial and target coordinates. The robot operates on a 12-volt power supply and two STM32F103C microcontrollers, utilizing an MPU6050 sensor to maintain orientation and optical rotary encoders for accurate positional tracking. Experimental results demonstrate that the robot achieves optimal motion on x and y axes with PID settings of kP = 0.8, kI = 1.0, and kD = 0.08. This configuration yields a rise time of 0.95 seconds, overshoot of 7.36%, and steady-state error of -0.5 RPM at a setpoint of 350 RPM. Using odometry, the robot successfully navigates various movement patterns with average position errors of 1.2% on the x-axis and 1.6% on the y-axis for rectangular patterns, 2.1% on the x-axis and 2.2% on the y-axis for zig-zag patterns, and 1.75% on the x-axis and 1.15% on the y-axis for triangular patterns. The MPU6050 sensor maintains orientation with an error of 0.65% in triangular patterns and 0.85% in rectangular patterns. Through inverse kinematics, PID control, and sensor integration, the robot reliably follows designated coordinate points.
Enhancing Solar Cell Performance: The Impact of Microstructure in Nanostructured Perovskites Kumar, Swarup; Neidhe, Md Musfiqur Rahman; Ahmed, Faisal; Hasan, Md Mehedi
Control Systems and Optimization Letters Vol 3, No 1 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i1.136

Abstract

A revolutionary development in solar cell technology, nanostructured perovskites have the potential to greatly improve stability and power conversion efficiency (PCE). The contribution of microstructure, including defect passivation, surface morphology, crystallinity, and grain size, to perovskite solar cell (PSC) performance optimization is evaluated in this paper. Through nanoscale optimization of these microstructural characteristics, scientists may enhance light absorption, minimize recombination losses, and optimize charge transfer, all of which contribute to increased efficiency. More versatility in bandgap engineering for a range of applications is made possible by the distinct optoelectronic properties of perovskites in conjunction with the benefits of nanostructuring. The endurance of nanostructured perovskites under environmental pressures and the scalability of production techniques are two issues that persist despite these developments. It is essential to overcome these obstacles in order to commercialize PSCs. Potential future developments for lead-free perovskite substitutes and the incorporation of nanostructured materials into hybrid solar systems are also examined in this study. Key results, ramifications, and opportunities for future advancements in nanostructured perovskites for solar energy technology are highlighted in this study, which summarizes the present status of research in this area. The review process aims to summarize current developments in the area and pinpoint the crucial problems that need to be resolved for wider acceptance.
A Comprehensive Review of Environmental and Economic Impacts of Autonomous Vehicles Uzzaman, Asif; Muhammad, Waseem
Control Systems and Optimization Letters Vol 2, No 3 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i3.131

Abstract

The development of autonomous vehicles (AVs) holds great potential for revolutionary improvements in several fields, most notably the economic and environmental domains. This paper analyzes the two-pronged effects of AVs, showing both the advantages and disadvantages that may arise. With their enhanced fuel efficiency, integrated electric vehicle technology, and driving behaviors, autonomous vehicles (AVs) have the potential to drastically reduce emissions and have a positive environmental impact. Lower carbon footprints could also be achieved by improved urban design and the possibility of less traffic congestion. In terms of the economy, AVs present chances for new transportation service business models, lower accident-related costs, and cost reductions in logistics. Nevertheless, there are drawbacks to these developments as well, such as high upfront costs, the possibility of employment displacement in the driving industry, and the requirement for strong regulatory frameworks to guarantee security and safety. It is anticipated that autonomous vehicles will improve lane management, acceleration, and deceleration, which could result in less gasoline being used. In a place like California where traffic congestion is a well-known issue, this is especially pertinent. When paired with electric cars (EVs), autonomous vehicles (AVs) have the potential to significantly lower greenhouse gas (GHG) emissions, supporting California aggressive climate targets, which include becoming carbon neutral by 2045. To maximize the positive effects of AVs while minimizing their negative effects, this review summarizes the most recent studies, offering a fair assessment of their implications. It also identifies important topics for further research.
Benchmark Analysis of Sampling Methods for RRT Path Planning Pratama, Gilang Nugraha Putu; Dhewa, Oktaf Agni; Priambodo, Ardy Seto; Baktiar, Faris Yusuf; Prasetyo, Rizky Hidayat; Jati, Mentari Putri; Hidayatulloh, Indra
Control Systems and Optimization Letters Vol 2, No 2 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i2.132

Abstract

Path planning is a crucial aspect of mobile robot navigation, ensuring that robots can safely travel from their initial position to the goal. In real-world applications, path planning is essential for autonomous vehicles, drones, warehouse robots, and rescue robots to navigate complex environments efficiently and safely. One effective method for path planning is the Rapidly-exploring Random Tree (RRT) algorithm, which is particularly practical in maze-like environments. The performance of RRT depends on the sampling methods used to explore the maze. Sampling methods are important because they determine how the algorithm explores the search space, affecting the efficiency and success of finding an optimal path. Poor sampling can lead to suboptimal or infeasible paths. In this study, we investigate different sampling strategies for RRT, specifically focusing on uniform sampling, Gaussian sampling, and the Motion Planning Network (MPNet) sampling. MPNet leverages a neural network trained on past environments, allowing it to predict promising regions of the search space quickly, unlike traditional methods like RRT that rely on random exploration without prior knowledge. This makes MPNet much faster and more efficient, especially in complex or high-dimensional spaces. Through a benchmarking analysis, we compare these methods in terms of their effectiveness in generating feasible paths. The results indicate that while all three methods are effective, MPNet sampling outperforms uniform and Gaussian sampling, particularly in terms of path length. The mean path length generated, based on a sample size of 30, is 13.115 meters for MPNet, which is shorter compared to uniform and Gaussian sampling, which are 18.27 meters and 18.088 meters, respectively. These findings highlight the potential to enhance path planning algorithms using learning-based sampling methods.
Nanomaterials in Industry: A Review of Emerging Applications and Development Kumar, Swarup; Khan, Saidul Islam; Neidhe, Md Musfiqur Rahman; Islam, Monirul; Hasan, Md Mehedi
Control Systems and Optimization Letters Vol 2, No 3 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i3.135

Abstract

Nanomaterials are materials where at least one dimension is smaller than 100 nanometers, unlocking a realm of extraordinary properties that set them apart from their bulk counterparts. These materials exhibit unique behaviors, such as enhanced electrical conductivity, superior mechanical strength, and heightened chemical reactivity. Due to these qualities, they are widely used in sectors like as electronics, healthcare, energy, and environmental preservation. Nanomaterials have made it possible for electronics to get smaller, and they have enhanced medication delivery and diagnostics in the medical field. They are perfect for energy conversion and storage technologies like solar cells and batteries because of their large surface area and conductivity. Furthermore, the use of nanoparticles in sustainable agriculture and environmental remediation is being investigated. Nevertheless, there are still difficulties in meeting regulatory requirements, guaranteeing safety, and increasing output. This paper looks at the many uses for nanomaterials, emphasizes their promise, and discusses the obstacles preventing a wider industrial acceptance of them.

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