Integrated Control System Research Articles

Integration of Ship Machinery Maintenance and Control Systems in Maritime Education: Enhancing Industry Readiness

Integration of Ship Machinery Maintenance and Control Systems in Maritime Education: Enhancing Industry Readiness

The possibility of unifying neural interfaces to create an integrated control system for prostheses: a brief review

The purpose of research. To date, neurointerfaces have not been unified to create combined prosthetic control systems. Based on this, this review is aimed at understanding the possibility of integrating neurointerfaces by clarifying the advantages and disadvantages of neurotechnologies related to prosthetics and the possible creation of a combined prosthesis control system.Methods. Analysis of brain-computer interfaces available in the literature in combination with neuroimaging experiments, especially in a hybrid system. A number of databases of scientific literature were used for the analysis, namely Google Scholar, scopus, etc. Links to the database data on the Internet: https://scholar.google.com/, https://www.mdpi.com/journal/sensors, elibrary.ru, https://www.refseek.com, https://link.springer.com/, https://www.base-search.netResults. Brain-computer interfaces are currently being used in a wide variety of fields, including to improve the lives of people with disabilities. However, individual neural interfaces have certain disadvantages that make it difficult to use them to control mechanical devices, including prosthetic limbs. Hybrid neural interface systems (as an integrated software and hardware complex) are significantly superior to those obtained using separate neural interfaces, and these systems can be used for medical purposes.Conclusion. This review provides a brief overview of the disability of people with missing upper limbs and how to improve their lives with prosthetics. The analysis of various hybrid methods of brain research is given. It can be noted that fNIRS technology is the closest technology that can facilitate the integration of neural interfaces, since it has advantages that make it a tool that complements other technologies, its advantages make up for the inherent disadvantages of fNIRS. It has been established that the hybrid system provides a clear advantage over individual neural interfaces.

Rise of Intelligent Machines: Influence of Artificial Intelligence on Mechanical Engineering Innovation

The integration of artificial intelligence (AI) into mechanical engineering has precipitated a profound transformation in the way engineers conceive, design, and execute projects. This paper explores the multifaceted impact of AI on mechanical engineering innovation, elucidating the myriad ways in which intelligent machines are revolutionizing traditional practices and catalyzing unprecedented advancements. In the realm of design, AI algorithms are revolutionizing the conceptualization and optimization processes. By leveraging machine learning and optimization techniques, engineers can explore vast design spaces with unparalleled efficiency, uncovering innovative solutions that might otherwise remain elusive. These AI-driven design tools not only expedite the development cycle but also enable the creation of products and systems with enhanced performance characteristics, such as improved energy efficiency, structural integrity, and functional versatility. Moreover, AI's influence extends beyond the design phase and permeates the entire manufacturing ecosystem. AI-driven automation is reshaping production lines, enabling agile and adaptive manufacturing processes that respond dynamically to changing demands and conditions. Through the integration of sensors, actuators, and AI-powered control systems, factories are becoming increasingly intelligent and autonomous, optimizing resource utilization, minimizing waste, and maximizing throughput.

Enhancing Efficiency and Safety in Self-Guided Logistics Vehicles: A Comprehensive Analysis and Integration of Hardware and Control Systems

Self-guided logistics vehicles have become increasingly popular in various industries and warehouses for material lifting and transportation purposes. These automated systems have proven to be valuable in both small-scale production lines and large areas. However, challenges persist in the field of logistics, where industries and factories face difficulties in efficiently transporting materials. In response to these challenges, self-guided vehicles have been employed, but issues such as collisions with obstacles, material falls, and rigid path constraints have been observed. This project aims to address these challenges by implementing hardware safety precautions and incorporating routing and scheduling capabilities to enhance system automation. The article presents a comprehensive analysis of hardware safety precautions and highlights key features including sensors, actuators, and control systems, which contribute to the overall efficiency and smooth operation of the self-guided logistics vehicle system. By integrating these elements, the system can overcome obstacles, ensure material safety, and adapt to changing traffic conditions, thereby optimizing logistics operations in industries and warehouses.

Challenges and strategic solutions in commissioning and start-up of subsea production systems

The commissioning and start-up of subsea production systems represent critical phases in offshore oil and gas operations, where meticulous planning and execution are essential for success. This abstract presents an overview of the challenges encountered during these phases and proposes strategic solutions to address them effectively. Environmental factors such as deepwater operations and subsea geology pose significant challenges to commissioning and start-up activities. Technical challenges include ensuring equipment reliability and seamless integration of control systems in harsh underwater conditions. Operational challenges such as remote monitoring and intervention and personnel safety further compound the complexities of these operations. Strategic solutions to mitigate these challenges include advanced planning and simulation using digital twin technology and scenario planning. Enhanced risk management strategies, including contingency planning and early detection systems, are vital to anticipate and manage potential issues effectively. Collaborative partnerships between stakeholders, including suppliers and cross-disciplinary teams, foster innovation and enhance project resilience. Drawing from successful case studies and lessons learned, this abstract emphasizes the importance of proactive planning, risk mitigation, and collaborative approaches in ensuring the successful commissioning and start-up of subsea production systems. By implementing these strategic solutions, operators can enhance operational efficiency, minimize downtime, and improve safety outcomes, ultimately contributing to the long-term viability and sustainability of offshore energy production.

Adapting To The New Normal: Assessing Levers Of Control Implementation In Indonesian Organizations Post-COVID

This systematic literature review investigates the application of the Four Levers of Control (4LOC) framework in Indonesian organizations post-COVID-19. The research method involved a systematic search of electronic databases and analysis of relevant literature. Major findings indicate that effective implementation of the 4LOC framework contributes to organizational resilience, performance improvement, and competitive advantage across various sectors. Cultural factors, leadership styles, and strategic alignment emerge as critical determinants of success. The study underscores the versatility and effectiveness of the 4LOC framework in navigating challenges posed by dynamic environments. Conclusions highlight the importance of adaptive strategies, cultural sensitivity, and integrated control systems in driving sustainable growth and organizational success in Indonesia. Results indicate that LoC implementation has positively impacted organizational performance across sectors such as hospitality, start-ups, state-owned enterprises, government organizations, manufacturing, and the service industry. Notably, effective utilization of belief systems, boundary systems, diagnostic control systems, and interactive control systems has been instrumental in fostering innovation, resilience, and strategic alignment. These findings underscore the importance of robust management control systems in achieving organizational objectives and navigating uncertainties. The review concludes by recommending further research to explore longitudinal effects, contextual influences, and the role of emerging technologies in enhancing LoC implementation.

Planning a Sewage Treatment System for Smart Housing Societies: A Review

Smart housing societies are increasingly incorporating sustainable practices, including on-site sewage treatment. This paper reviews key considerations for planning a sewage treatment system in such communities. Factors like resident population, water usage patterns, and available space are analyzed. Different treatment technologies, including activated sludge, constructed wetlands, and membrane bioreactors, are evaluated for their suitability in smart housing contexts. The integration of smart monitoring and control systems for optimizing treatment processes and water reuse is discussed. Finally, the importance of adhering to local regulations and involving residents in the planning process is highlighted.

Enhancement of Power Quality through the Implementation of a Hybrid Artificial Intelligence Model within Solar-Wind Energy Systems

The generation of electricity is significantly assisted by hybrid solar-wind power systems, which also play a crucial role in the advancement of intelligent grids. Furthermore, the integration of wind and solar energy storage control systems, in conjunction with energy markets, has rendered the electricity grid more economically feasible. To meet the growing demand for electricity, hybrid renewable energy systems connected to microgrids consist of substantial identifying elements. The issue of harmonic distortion in microgrids arising from nonlinear loads is a fundamental area of research. Understanding the impact of microgrids on power quality is also of great importance. This study focuses on enhancing power quality in solar-wind hybrid systems by incorporating emerging Artificial Intelligence (AI) methodologies. The proposed approach comprises two main components: (i) detection of distortions caused by voltage fluctuations and (ii) improvement of power quality through an AI-based control system. Initially, power distortions are identified and eliminated using the Enhanced Kalman Filter (EKF) algorithm. A Hybrid Artificial Neural Network with Fuzzy Intelligence (HANFI) model is then developed based on this power data. This model integrates two prominent neural network architectures with fuzzy systems. Experimental validation of the system is conducted using MATLAB, which facilitates the simulation of hybrid energy systems.

Incubator Control System Automatic Temperature Stabilizer Bangkok Chicken with LM35 Sensor

Many cases of death of chicks in farmers are caused by drastic changes in temperature which result in stress and death of chickens. In this study the authors made a chick coop design equipped with an LM35 sensor which functions to control the temperature of the chicks. The research object is Bangkok chicken aged 7-10 days which has a temperature requirement of 300oC. The test results show that the temperature stabilizer incubator for both the sensor test separately and the integration of the incubator control system as a whole works according to the program design, this is based on the test results that the lights and fans work according to the LM35 sensor reading input with 100% accuracy where as long as 3 days of testing the average temperature of the first, second and third days is below 300oC so that the output is in the form of lights on and the fan is off. On Day 3 all the chickens were healthy and growing well.

Age-related differences in functional connectivity associated with pain modulation

Growing evidence suggests that aging is associated with impaired endogenous pain modulation, and that this likely underlies the increased transition from acute to chronic pain in older individuals. Resting-state functional connectivity (rsFC) offers a valuable tool to examine the neural mechanisms behind these age-related changes in pain modulation. RsFC studies generally observe decreased within-network connectivity due to aging, but its relevance for pain modulation remains unknown. We compared rsFC within a set of brain regions involved in pain modulation between young and older adults and explored the relationship with the efficacy of distraction from pain. This revealed several age-related increases and decreases in connectivity strength. Importantly, we found a significant association between lower pain relief and decreased strength of three connections in older adults, namely between the periaqueductal gray and right insula, between the anterior cingulate cortex (ACC) and right insula, and between the ACC and left amygdala. These findings suggest that the functional integrity of the pain control system is critical for effective pain modulation, and that its function is compromised by aging.

Learning robust autonomous navigation and locomotion for wheeled-legged robots.

Autonomous wheeled-legged robots have the potential to transform logistics systems, improving operational efficiency and adaptability in urban environments. Navigating urban environments, however, poses unique challenges for robots, necessitating innovative solutions for locomotion and navigation. These challenges include the need for adaptive locomotion across varied terrains and the ability to navigate efficiently around complex dynamic obstacles. This work introduces a fully integrated system comprising adaptive locomotion control, mobility-aware local navigation planning, and large-scale path planning within the city. Using model-free reinforcement learning (RL) techniques and privileged learning, we developed a versatile locomotion controller. This controller achieves efficient and robust locomotion over various rough terrains, facilitated by smooth transitions between walking and driving modes. It is tightly integrated with a learned navigation controller through a hierarchical RL framework, enabling effective navigation through challenging terrain and various obstacles at high speed. Our controllers are integrated into a large-scale urban navigation system and validated by autonomous, kilometer-scale navigation missions conducted in Zurich, Switzerland, and Seville, Spain. These missions demonstrate the system's robustness and adaptability, underscoring the importance of integrated control systems in achieving seamless navigation in complex environments. Our findings support the feasibility of wheeled-legged robots and hierarchical RL for autonomous navigation, with implications for last-mile delivery and beyond.

Application of Fuzzy logic and IoT in a small-scale Smart Greenhouse System

This research aims to address the critical challenges caused by global population increase, climate change, and urbanization on food production by means of a Fuzzy Logic controller in a small-scale greenhouse and improving agricultural efficiency and sustainability.The growing global population has stressed the significance of implementing efficient food production techniques. Furthermore, the impact of climate change and extreme weather events complicates the production process. The goal of this study is to combine an innovative small greenhouse design with a fuzzy logic-based control system in order to develop an IoT-enabled self-regulating environment for autonomous plant growing.Continuous advances in technology have been focusing on automating greenhouses to provide self-regulating microclimates and optimal developmental conditions. Current trends focus on automating greenhouses to provide self-regulating microclimates. Looking ahead, the strategy entails combining automated approaches with precisely planned structures via IoT.This study's key findings include the successful integration of a fuzzy logic control system with greenhouse infrastructure, allowing for real-time monitoring and adjustment of environmental parameters, as well as the development of a pilot IoT ecosystem demonstrating the feasibility of self-regulating microclimates for improved plant growth.

Control System Design

There are a number of tasks in the modern world related to the control and management of the product life cycle in industries, that are solved using PLM systems. The PLM systems consist of several systems or modules connected by a enterprise service bus. The ESB allows interaction between modules, including data exchange. However, the enterprise could implement a business process using its own information system, and it is necessary to solve the problem of smooth implementation of the PLM system and it’s use with the organization's one. This paper considers an approach based on using an adapter mechanism that contains not only an interface for interacting with an information system, but also implements control logic, that generates the patterns of system behavior as output data obtained using the data-driven control approach. The purpose of the article is to model the control system for integration with the PLM system. The general visions of the chalengies, the methods are used to build the adapter, the adapter’s space in the PLM are considered, the proposed approach and the further research notes are described.

Development of Portable Magnetic Adsorption Amphibious Robot

In this study, a portable magnetic adsorption amphibious robot which can operate on and below the waterline is developed for special curved environments, such as the pile legs of offshore platforms and the outer walls of ships. An open robot integrated control system based on a domestic chip is developed, and two operating modes of local control operation and remote wireless operation are realized. A permanent magnet adsorption scheme combining a magnetic adsorption track and a synchronous belt wheel is designed, static and dynamic analysis of the wall-climbing operation of the robot is carried out, and a kinematic model of the underwater robot is established. The experimental results show that the robot can effectively complete amphibious tasks and can realize the accurate control of attitude in water, proving it to be an effective tool for amphibious tasks, such as operating on the pile legs of offshore platforms and the outer walls of ships.

Evaluation of power grid vulnerability indices accounting for wind power uncertainty

As power systems undergo transformative changes driven by the integration of renewable energy sources, smart grids, and advanced control systems, evaluating vulnerabilities becomes paramount for ensuring resilience. This article introduces a robust framework for computing vulnerability indices, encompassing both cascading indices and spectral graph metrics. Cascading indices are initially computed. Then these indices are compared with pure and extended spectral graph metrics to rank the lines under N−1 contingency conditions. The research also delves into N−k contingency scenarios, examining the system’s vulnerability across various degrees of damage size. The influence of uncertainties related to wind power generation and load demand on line vulnerability is addressed. The Point Estimate Method (PEM) is employed to effectively accommodate these uncertainties offering a reduction in computational time. To validate the proposed framework and analysis methodologies, two case studies are employed—the IEEE 30-bus and 57-bus systems. It is found that the inclusion of wind power and load uncertainty affects the vulnerability of the system. While the impact on cascading indices is negligible for small networks, it becomes notable in the case of larger networks.

Bandgap Dynamics in Locally Resonant Metastructures: A General Theory of Internal Resonator Coupling

The dynamics of metastructures, incorporating both conventional and internally coupled resonators, are investigated to enhance vibration suppression capabilities through a novel mathematical framework. A close-form formulation and a transfer function methodology are introduced, integrating control system theory with metastructure analysis, offering new insights into the role of internal coupling. The findings reveal that precise internal coupling, when matched exactly to the stiffness of the resonator, enables the clear formation of secondary bandgaps, significantly influencing the vibration isolation efficacy of the metastructure. Although the study primarily focuses on theoretical and numerical analyses, the implications of adjusting mass distribution on resonators are also explored. This formulation methodology enables the adjustment of bandgap characteristics, underscoring the potential for adaptive control over bandgaps in metastructures. Such capabilities are crucial for tailoring the vibration isolation and energy harvesting functionalities in mechanically resonant systems, especially when applied to demanding heavy-duty applications.

Integrated Active Suspension and Anti-Lock Braking Control for Four-Wheel-Independent-Drive Electric Vehicles

This paper presents an integrated control scheme for enhancing the ride comfort and handling performance of a four-wheel-independent-drive electric vehicle through the coordination of active suspension system (ASS) and anti-lock braking system (ABS). First, a longitudinal-vertical coupled vehicle dynamics model is established by integrating a road input model. Then the coupling mechanisms between longitudinal and vertical vehicle dynamics are analyzed. An ASS-ABS integrated control system is proposed, utilizing an H∞ controller for ASS to optimize load transfer effect and a neural network sliding mode control for ABS implementation. Finally, the effectiveness of the proposed control scheme is evaluated through comprehensive tests conducted on a hardware-in-loop (HIL) test platform. The HIL test results demonstrate that the proposed control scheme can significantly improve the braking performance and ride comfort compared to conventional ABS control methods.

Monitoring of Thiamethoxam Resistance in Turkish House Fly Strains, Musca domestica (Diptera: Muscidae).

The house fly, Musca domestica, is vector for pathogens and parasites and causes economic damage to livestock by reducing forage conversion efficiency, negatively impacting weight gain and milk production. It has shown resistance to multiple insecticide classes. The aim of this research was to determine the susceptibility levels of seventeen field M. domestica strains to thiamethoxam, a neonicotinoid insecticide, in Türkiye. Insecticide susceptibility of the house flies to thiamethoxam was determined using the WHO glass jar method. A probit analysis program was used to determine LD50 values, and then the resistance ratios were compared with insecticide-susceptible strain. All strains were ≥18.5-fold resistant to thiamethoxam. The data showed that 10 out of 17 strains had either high or very high resistance levels. Our findings revealed that house flies from solid waste landfills in Samsun, Ankara, and Kocaeli exhibited higher resistance ratios compared to those found in animal shelters. Conversely, in Gaziantep, Antalya, İzmir and Erzurum, the exact opposite trend was observed. Regarding the LD50 values among solid waste storage areas, the lowest rate was obtained from Gaziantep (0.72 gr ai/m2), and the highest rate was obtained from Ankara (9.35 gr ai/m2). Furthermore, regarding the LD50 values among animal shelters, the lowest was obtained from Samsun (0.37 gr ai/m2), and the highest was obtained from Denizli (21800 gr ai/m2). The use of integrated control systems is recommended for controlling house fly populations, including insecticide class rotations for preventing, or at least, delaying the onset of resistance.

Autonomous Vessel Design for Efficient Marine Debris Collection: A MATLAB Simulink and Arduino-Based Approach

The growing threat that marine pollution poses to human health and biodiversity requires innovative responses. This study aims to create autonomous, unmanned vessels for collecting marine debris to prevent the negative effects of marine litter, especially plastics and industrial waste. The study uses MATLAB Simulink for dynamic modeling, simulating how the ship interacts with different debris and maritime settings. The effectiveness of advanced control systems that combine feedforward and feedback controls is evaluated regarding debris collection and pathfinding. The methodology of this study involves the design and simulation of the vessel’s hydrodynamic behavior, control systems, and debris interaction mechanisms. A scaled-down prototype has been built, integrating elements like a double-propeller hull, Arduino-driven control mechanisms, and a debris-gathering system. The prototype’s efficiency in gathering debris and mobility is demonstrated by real-world testing conducted in controlled situations. According to the results, the integration of modern control systems has been found to greatly improve the vessel’s debris collection efficiency. How the prototype functions in different situations demonstrates its ability to serve as a scalable solution for marine pollution. The result of this study emphasizes the possibility of automated, unmanned marine debris collection vessels as an environmentally friendly way to reduce marine pollution. The effective prototype testing, modeling, and simulation bring up new avenues for research and development in this area, which benefits the preservation of marine ecosystems.

Controlling temperature using proportional integral and derivative control algorithm for hybrid forced convection solar dryer

Drying is one of the crucial processes in agricultural production, especially in grain processing. The drying process can improve grain quality and affect the grain content. However, maintaining the temperature is a challenge in the drying process. Because it can influence the drying performance and produce a low-efficiency reduction of water content, in this study, the hybrid drying system is proposed to improve the performance of the forced convection dryer system. The proposed system used a proportional integral and derivative (PID) control system to obtain the optimal temperature. The proposed system was compared with natural drying and forced convection methods. The experimental result showed that the proposed system performed excellently for three performance evaluations. The average temperature was obtained as the highest of the other methods, with 54.68 °C and 54.55 °C for coffee and cocoa beans. The water content can be reduced by an average of 27.38% and 42.67% for coffee and cocoa beans. Then, the proposed system also had the highest reduction efficiency of water content than the other methods, with 62.71% and 36.94% reductions for coffee and cocoa beans, respectively. The results indicate that the proposed hybrid system performs better than the other methods.