Intelligent Control System Research Articles

Design concept of intelligent integrated control system for neutral beam injection

Abstract Due to the specificity of neutral beam injection (NBI) system, the control of its actual physical system is achieved by the integrated control system (ICS). The current NBI ICS adopts a distributed design structure to balance the system load, which is also the mainstream system design and architecture model of ICS around the world. However, from a practical point of view, the distributed system architecture is not perfect. In the long run, upgrading the system intelligence and automation capabilities is an inevitable choice for the future, so it is necessary to explore ways to transform and upgrade the ICS. At present, Internet of Things (IoT), as an important part of the new generation information technology, can realize the control of everything through data exchange. This is because on the one hand, IoT has wide compatibility and powerful scenario-based capabilities, it not only has the advantages and characteristics of distributed design, but also can pull the NBI subsystems into the same level scenario and lay the foundation for the further construction of digital NBI; on the other hand, the intervention of artificial intelligence makes IoT have some new typical characteristics such as intelligent sensing, ubiquitous connectivity, precise control, digital modeling, real-time analysis and iterative optimization, which is enough to pull the current NBI ICS into a new intelligent control era. Finally, it is worth mentioning that due to its inherent design structure and functional characteristics, ICS tends to be broadly generic, so it is not exclusively used for NBI operation in nuclear fusion, and it can provide some insight into other application areas.

Research on dynamic constraint mechanism and motion control of intelligent vehicles based on preview distance optimization under complex road conditions

Aiming at the coupling and conflict issues between intelligent vehicle dynamics characteristics and path tracking system under complex road conditions, this paper investigates the dynamics constraint mechanism and motion control of intelligent vehicles, and proposes an intelligent vehicle lateral–vertical cooperative control method based on optimized preview distance. To begin with, an intelligent vehicle path tracking control system based on expected yaw velocity has been designed by establishing a three-degree-of-freedom dynamic model for intelligent vehicle. Then, we analyzed the mechanism by which changes in vehicle speed, road curvature, and preview distance affect the accuracy of vehicle path tracking and handling stability. Considering the “human-vehicle-road” system in intelligent transportation systems, critical values for collision and instability were set. Furthermore, we designed a proactive optimization method for preview distance under different working conditions, using an optimization algorithm to improve path tracking accuracy while ensuring vehicle stability, based on the lateral displacement deviation and lateral orientation deviation representing the accuracy of path tracking, as well as the lateral acceleration representing handling stability. Finally, hardware-in-the-loop platform test was conducted. The simulation and test results show that the optimized path tracking algorithm reduces lateral deviation to as low as 0.05 m, and the stability constraint control in the algorithm can be triggered promptly even under extreme conditions.

Coil Formation and Biomimetic Performance Characterization of Twisted Coiled Polymer Artificial Muscles

A biological muscle's force is nonlinearly constrained by its current state (force, length, and speed) and state history. To investigate if artificial muscles can mimic (i.e. biomimetic) the complete mechanical state spectrum of biological muscles, this study uses a novel method to characterize twisted coiled polymer actuators (TCPAs) mechanically. Thus, comprehensive and reproducible test procedures are established to verify artificial muscle biomimetics regarding stress, strain, and strain rate combinations intrinsic to biological muscle. A rheometer performs novel high‐precision mechanical characterization methods to comprehensively verify biomimetic performance. Sample twist level, torque, length, force, and temperature are controlled and measured during twist‐induced coiling, heatsetting/annealing, and mechanical testing. TCPAs are formed from linear low‐density polyethylene monofilament. Linear low‐density polyethylene (LLDPE) TCPAs generate larger stresses than biological muscle through the entire spectrum of strains—contracting more than 40%, exerting more than 0.3 MPa at rest length, and withstanding tension of 8 MPa without damage. Thus, the LLDPE TCPAs attain biological muscle performance statically, but additional tests are required to assess this dynamically. The mechanical performance of LLDPE TCPAs enables biomimetic actuation with an intelligent control and measurement system. Their high‐throughput textile manufacturability positions them for advanced biomechatronic applications—including prosthetics and exoskeletons.

Variable frequency constant pressure water supply system based on Siemens S-1200

Abstract This paper presents the design of an efficient, cost-effective, and intelligent variable frequency water supply control system. The system consists of a PLC, frequency converters, water pumps, pressure sensors, and other components to form a closed-loop control system. It enables closed-loop control of water pressure, ensuring a dynamic balance between water consumption and water supply. A user-friendly HMI is designed based on a software platform for easy operation by management personnel. It allows for seamless switching of the system’s operating modes and the running status of water pumps. Real-time display of pump operation and monitoring of system performance are also provided. Compared to traditional water supply methods, this system exhibits superior reliability, speed, and performance. It is capable of meeting the daily water consumption needs of residents in high-rise buildings.

Intelligent Street Lighting: An IoT-based System for Adaptive Brightness and Fault Management

The research aims to develop an intelligent fault detection and control system for street lights using an STM32 microcontroller and Internet of Things (IoT) technologies. The system uses various sensors and communication methods to manage lighting more effectively and maintain infrastructure better. Light-Dependent Resistors (LDRs), which turn lights on automatically at nightfall and off at dawn, are essential to the system's operation. Moreover, motion is detected by the infrared (IR) sensors on the street, allowing the lighting to save energy by dimming during times of low traffic and brightening when movement is detected. By using control logic and sensor readings, a relay functions as an electronically controlled switch to turn the lights on and off. To process sensor data, the STM32 microcontroller is used in the main functionality. Energy savings are facilitated by the automated on/off switching based on ambient light levels, enabled by the LDR sensor data. In addition, the device includes a GPS module for precise street light location tracking. An IoT platform transmits this location data as well as operational status and real-time sensor readings. Maintenance staff can access regional data on light quality and sensor readings through an LCD display. This extensive data enables maintenance team to focus better, use resources efficiently, and enable remote monitoring and fault identification (e.g., burned-out bulbs). This Internet-of-Things (IoT) system provides an economical and environmentally friendly approach to efficient street light management by integrating automation, remote control, and real-time monitoring.

Proposed Intelligent Irrigation System for Riyadh City Using Fuzzy Logic

Considering the insufficient rainfall and rapid evaporation of soil moisture in desert regions, irrigation becomes very challenging in Riyadh. Automation is therefore required for optimum productivity and water conservation. According to the vision of the Green Riyadh project, recycled water will be used in the irrigation system to irrigate all green lands. The method of irrigation and the quantity of water being used must both be considered. Since drought inhibits plant growth and excessive moisture limits plants' ability to absorb nutrients and raises the possibility of disease development, only the amount of water needed by plants should be used. Artificial intelligence and fuzzy logic are increasingly seen as solutions to be implemented in smart drip irrigation systems. The fuzzy logic control system will aid in water conservation given its shortage, which has become a major worldwide concern. The system also has the advantage of conserving moisture to counteract inadequate rainfall, saving electricity and reducing labor costs. For such advantages, an intelligent irrigation control system based on fuzzy logic adapted to the climate of Riyadh will be proposed. Based on the measured soil moisture, ambient temperature, air humidity and solar irradiance, the fuzzy controller calculates the necessary irrigation duration. The features of the fuzzy logic toolbox in MATLAB are used to create the proposed system.

Exploring the influence of linear infrastructure projects 4.0 technologies to promote sustainable development in smart cities

Industry 4.0 technologies have a high potential to improve the planning and execution of linear projects such as roads, bridges and railroads. These technologies can help reduce emissions, enhance operations' efficiency, and improve users' quality of life. Despite their potential to promote sustainability in smart cities, there is a lack of research focused on analysing their impact. Therefore, this paper aims to identify 4.0 technologies related to linear projects and examine their influence on smart cities' sustainable development. The research method involved four main stages: identification of 4.0 technologies in linear projects, questionnaire design and application, influence analysis, and factor analysis. A systematic literature review identified a set of 4.0 technologies that favour smart cities' sustainable development. Subsequently, 66 experts were consulted to determine which of these technologies have the most significant influence on smart cities' sustainable development, highlighting data analysis and management, intelligent traffic control systems, and artificial intelligence. This study makes three main contributions: (1) it identifies thirty-seven 4.0 technologies associated with linear projects that promote sustainable development in smart cities; (2) it characterises the 4.0 technologies with the most significant influence on the sustainability of smart cities; and (3) it proposes nine principal components into which interrelated 4.0 technologies that promote sustainable development can be grouped. This study provides valuable evidence for smart city managers and offers guidance for the efficient adoption of 4.0 technologies in linear projects.

Low voltage distribution intelligent control system based on distributed photovoltaic grid connection

Abstract This article mainly studied the use of distributed photovoltaic power generation technology to achieve intelligent control of low-voltage distribution networks. With the rapid development of new energy, distributed photovoltaic power generation technology has become an important means to alleviate the current energy crisis and environmental problems. This article first elaborated on the theory and research status of distributed photovoltaic power generation systems. Based on this, artificial intelligence technology was applied to distributed photovoltaic power generation systems for real-time monitoring to enhance their stable and reliable performance. When the simulated grid voltage was set to 220V, the system output voltage was also 220V, showing a deviation of 0.0%, proving that the system has excellent regulation accuracy at this voltage level. This article is an innovative research that integrates distributed photovoltaic power generation with intelligent control, opening up a practical and feasible technical path for the large-scale utilization of clean energy. Therefore, this study has significant academic value and practical significance.

Software defined network-based controller system in intelligent transportation system

<span lang="EN-US">Intelligent transportation system (ITS) is meant for redefining the conventional transport by incorporating various analytical features that not only offers safety but also enriches traffic data quality extensively. Review of existing literature shows that there is a significant gap towards utilizing vehicular adhoc network (VANET) for optimal performance in ITS environment. Therefore, this paper contributes towards a simplified and yet intelligent controller system harnessing potential of software defined network (SDN) towards effective directional management of complex transportation system. The novelty of this model is dual-fold. The first novelty is about the usage of locally and globally processed traffic information for undertaking decision towards clearing the waiting vehicles in observation point in specific route segment. The second novelty is associated with relaying of distinct traffic clearance signal to the distinct vehicles unlike any of teh existing transportation management scheme.</span>

Fringing‐Effect‐Based Capacitive Proximity Sensors

AbstractProximity sensing technology, which can obtain information without physical contact, has become an ideal choice in scenarios where physical contact is not feasible. Despite substantial advancements in tactile sensing, proximity sensing technology still holds great potential and has yet to be fully developed. Among numerous proximity sensing technologies, the fringing‐effect‐based capacitive proximity sensor (FE‐CPS) has garnered considerable attention due to its low cost, low power consumption, wide sensing range, and flexible and versatile structural design. However, research on FE‐CPS has not yet formed a complete system, and its development and intellectualization are still in their infancy, urgently requiring a systematic review to advance its development. This paper systematically summarizes the recent advances in FE‐CPS, from basic theory to practical applications. The working principle and typical structure of FE‐CPS are first introduced, followed by a discussion of methods for optimizing device performance. Furthermore, the application scenarios of FE‐CPS in intelligent pre‐alarm systems, intelligent control systems, and intelligent material perception systems are reviewed. Finally, the future development and challenges faced by FE‐CPS are prospected.

An IoT-Based Smart Street Lighting System to Lower Energy Consumption

Smart Lighting System is an automated and intelligent lighting control system that can be deployed across multiple locations or in one location, utilizing various IoT connection protocols, devices, and sensors. The system is able to intelligently adjust to changes in ambient light conditions, traffic patterns, and pedestrian activity through the integration of Internet of Things (IoT) technologies. IoT-enabled sensors are installed in every street light, creating a network that connects to a central control unit. Using real-time data, this system dynamically modifies street light brightness to maximize energy efficiency and improve safety. It provides a simulation and optimization tool for street light placement, considering factors such as street length, light distance, and the number of lights ahead. This tool aids in visualizing the power consumption implications of different configurations, promoting efficient urban planning. Demonstrates the energy-saving potential of the proposed system. As the street light intervals and the number of lights ahead decreases, the optimal street light power significantly reduces, leading to substantial energy savings. The system's ability to dim or turn off lights during low-traffic or well-lit periods contributes to reduced operational costs and enhanced sustainability.

Enhancing Traffic Control with AI Blockchain and Dynamic Computation Techniques

The rapid urbanization and increasing vehicular density in modern cities have led to significant challenges in traffic management and control. As urban areas continue to expand, the demand for more efficient and intelligent traffic control systems has become increasingly critical. This paper presents a novel approach to enhancing traffic management by integrating Artificial Intelligence (AI), Blockchain technology, and Dynamic Computation Techniques. AI is utilized to analyze and predict traffic patterns, enabling real-time adjustments to traffic signals and flow management. Blockchain provides a secure, transparent, and decentralized platform for data sharing and coordination among various stakeholders, ensuring data integrity and trust. The incorporation of Dynamic Computation Techniques allows for flexible and scalable processing of complex traffic data, facilitating rapid decision-making and adaptation to changing conditions. This multidisciplinary approach not only improves traffic efficiency and reduces congestion but also paves the way for more resilient and sustainable urban transportation systems. The findings highlight the transformative potential of combining AI, Blockchain, and advanced computation methods in the field of traffic control.

A dynamic hybrid model of supercritical once-through boiler-turbine unit including recirculation mode and once-through mode

Supercritical once-through boiler-turbine units play a paramount role in maintaining grid stability owing to their exceptional efficiency and flexibility. Nevertheless, achieving precise modeling of this device under various operational conditions remains a significant challenge. This study conducts modeling and simulation of an actual supercritical once-through coal-fired power plant. The findings signify that the hybrid model proposed herein adeptly encompasses all operational modes of supercritical once-through units, including start-up recirculation mode and once-through mode. To bolster model precision, an optimization algorithm grounded on weighted mean of vectors (INFO) has been employed for parameter identification, while the INFO-XGBoost was utilized to fashion a machine learning model for static parameters. It has been validated that the developed model accurately captures the dynamic characteristics of the unit. Furthermore, the utilization of the INFO-XGboost to model static parameters has been proved to enable the errors in principal outputs of coordinated control system to be within 0.35 % of MAPE across the entire operational process, thus facilitating the design of more refined and intelligent control systems.

Optimizing Urban Mobility: A Comprehensive Study on Intelligent Traffic Signal Control Systems

This paper explores the application of Adaptive Traffic Signal Control (ATSC) systems integrated with Information and Communication Technology (ICT) and the Internet of Things (IoT) to improve urban traffic management. ATSC systems utilize real-time traffic data from various sensors and cameras to adjust signal timings dynamically, aiming to reduce congestion and enhance flow efficiency. The study highlights how these systems process and react to live traffic conditions, discussing the implementation of machine learning models and optimization algorithms that refine signal timing strategies based on traffic density and flow patterns. Challenges like data accuracy, system integration, cybersecurity, and environmental impacts on sensor performance are examined, alongside the potential for improving urban mobility and reducing environmental effects through smarter traffic management solutions. The paper concludes by emphasizing the importance of continuous advancements in technology and infrastructure to support the scalability and effectiveness of ATSC systems in modern cities.

Smart IoT SCADA System for Hybrid Power Monitoring in Remote Natural Gas Pipeline Control Stations

A pipeline network is the most efficient and rapid way to transmit natural gas from source to destination. The smooth operation of natural gas pipeline control stations depends on electrical equipment such as data loggers, control systems, surveillance, and communication devices. Besides having a reliable and consistent power source, such control stations must also have cost-effective and intelligent monitoring and control systems. Distributed processes are monitored and controlled using supervisory control and data acquisition (SCADA) technology. This paper presents an Internet of Things (IoT)-based, open-source SCADA architecture designed to monitor a Hybrid Power System (HPS) at a remote natural gas pipeline control station, addressing the limitations of existing proprietary and non-configurable SCADA architectures. The proposed system comprises voltage and current sensors acting as Field Instrumentation Devices for required data collection, an ESP32-WROOM-32E microcontroller that functions as the Remote Terminal Unit (RTU) for processing sensor data, a Blynk IoT-based cloud server functioning as the Master Terminal Unit (MTU) for historical data storage and human–machine interactions (HMI), and a GSM SIM800L module and a local WiFi router for data communication between the RTU and MTU. Considering the remote locations of such control stations and the potential lack of 3G, 4G, or Wi-Fi networks, two configurations that use the GSM SIM800L and a local Wi-Fi router are proposed for hardware integration. The proposed system exhibited a low power consumption of 3.9 W and incurred an overall cost of 40.1 CAD, making it an extremely cost-effective solution for remote natural gas pipeline control stations.

Field Evaluation and Application of Intelligent Quality Control Systems

During road construction, the accuracy of compaction work is critical for the structural stability and maintenance of the road. Although the plate load test (PLT) is commonly used for quality inspections, it is impractical to test every section due to time and cost constraints. Therefore, simpler testing methods are being extensively developed. This study compared quality inspection results using the commonly used PLT, the relatively simple dynamic cone penetrometer test (DCPT), the lightweight deflectometer (LWD) test, and an intelligent quality control system equipped with accelerometer and global positioning system (GPS) sensors in intelligent compaction (IC) rollers. The results showed a strong correlation between the conventional tests (PLT, DCPT, and LWD) and the values obtained from the intelligent quality control system. The correlation analysis between the intelligent quality control system and PLT, LWDT, and DCPT yielded R-square values of 0.69, 0.91, and 0.95, respectively, indicating significantly high correlations. The implementation of intelligent quality management systems in earthwork construction projects will facilitate a thorough verification of the compaction quality throughout all construction segments, ensuring consistent compaction across the project. By enabling real-time data acquisition and analysis, these systems differ markedly from traditional methods, reducing the frequency and necessity of manual inspections. This approach not only streamlines construction processes, but also enhances operational efficiency. As a result, integrating these intelligent systems is anticipated to significantly increase productivity by optimizing the workflow and resource utilization in earthwork construction.

Intelligent underground garage drainage system based on informatized surveying technology

In recent years, with the impact of environmental factors such as global warming, floods and waterlogging disasters have occurred frequently in China. The traditional drainage devices in underground garages have poor water-blocking effects, which can easily result in sewage entering the garage along with vehicles during severe weather or sudden floods. To address the issue of water accumulation outside underground garages, which prevents vehicles from entering, we propose a set of drainage devices for underground garages to complement the shortcomings of existing market products. This device, controlled by an automated intelligent control system, can effectively control each device to achieve automatic water-blocking and drainage operations in underground garages, as well as car washing. During extremely heavy rainfalls, it can completely seal off the garage and enter flood prevention mode to protect vehicles inside from being submerged. Additionally, as the device is semi-underground, it can save a significant amount of above-ground space without affecting surface traffic or existing facilities, demonstrating practicality and having the potential for widespread adoption.

Development and application of closed-loop-oriented intelligent control system for blast furnace air volume addition or reduction

Adjusting the upper and lower parts is an important means to ensure stable operation of blast furnaces. It is a common operation method to adjust the smelting state of the blast furnace by adjusting the air volume. Most of the domestic blast furnace air volume increase and decrease operations are confirmed by manual experience, and it is difficult to realize intelligent automatic control. This paper presents an intelligent control system for regulating air volume in blast furnaces, based on operational logic, expert knowledge, fundamental theories, and other relevant information. The system enables automatic analysis of the blast furnace's state and intelligent reasoning of the foreman's operational methods, laying the foundation for achieving closed-loop control. The system unifies the control philosophy and stabilizes the control methods for regulating air volume in blast furnaces. This achieves standardization of operation and proceduralization of cause analysis. The system enhances adaptability by creating an information feedback mechanism, increasing acceptance among blast furnace operators, and continuously resolving various anomalies in practical applications. The system currently maintains a true hit rate of over 95%, enabling effective reduction of operational misjudgments, intelligent control of airflow adjustment, and promotion of stable and smooth blast furnace operation.

Systematic Research and Optimization Design of Intelligent Electric Furnace Control System

Systematic Research and Optimization Design of Intelligent Electric Furnace Control System

(Invited) Nanoarray Configured Monoliths for Self-Monitored Multi-Phase Reactive Processes

The incorporation of multiple functions synergistically into one device or platform can enable additions of application merits such as light weight, ease for system integration, reduced system complexity, cost-effectiveness, and programmability. This is especially true for usually complex energy, environmental, and biological systems with the integration need of many active or passive devices and components of various functionalities. In this work, we demonstrated a compact nanoarray configured monolith design with the ability to function as both a catalyst and a sensor to enable the self-monitoring of the complex multi-phase catalytic reaction processes during operation. A bimodular approach has been realized using both electrical and electrochemical interrogation modes for reactive gas and solid phase monitoring directly using the built-in catalysts. This new type of compact and built-in monolithic sensor configuration can lower the catalytic energetics and detect gas/solid composition semi-quantitatively at room temperature up to elevated temperature. Robust calibration curves of built-in sensor responses can be achieved with facile data processing and analyzing. The built-in monolith sensor shows good sensitivity and good selectivity sufficient for the self-monitoring of the reaction process. Besides, the nanoarray based monolith device has shown good environmental tolerance toward practical applications. Such compact monolithic device can serve as a self-monitored reactor, filter, and other function component that can potentially allow in-situ and real time monitoring and functional process control in complex and reactive physical and chemical environments. With further refining of the design and functional integration, this new type of smart monolithic device can provide necessary data and knowledge for future computation-driven situational awareness prediction and intelligent control of various complex dynamic processes and systems.