Advanced Control System Research Articles

Effects of mobile phone-related distraction on driving performance at roundabouts: Eye movements tracking perspective

Modern road infrastructures are complex networks featuring various elements such as roads, bridges, intersections, and roundabouts, with advanced control systems. Roundabouts have gained prominence as a safer alternative to traditional intersections promoting smoother traffic flow and fewer collisions by guiding traffic in one direction, encouraging reduced speed, and minimizing conflict points.This study investigated driver behavior within roundabouts, focusing on gaze behavior, particularly the left-side mirror and window, under mobile phone distraction conditions. In addition, the effects of roundabout specifications (i.e., number of lanes and size of the central island) and the drivers’ characteristics (i.e., driving experience) were examined.In total, 43 participants, aged 19–56 years including 30 males and 13 females, held a valid driving license, drove through a virtual simulated urban road containing four roundabouts, implemented in a static driving simulator, under baseline condition (no distraction) as well as mobile-induced distraction. Driving simulator data were collected and drivers’ gaze direction and fixation on nine areas of interest were captured with an eye tracker. Resultsshowed that experienced drivers exhibit a more fixation on the left-side mirror and window and were less distracted. Moreover, the road environment, i.e., the number of cars and the roundabout size, significantly influenced the drivers’ attention. As regards the driving performance, the number of infractions increased when the drivers diverted focus from the left side of the car. The outcomes of the present study might help to improve traffic safety at roundabouts.

Enhancing Energy Efficiency: Advances in Smart Grid Optimization

As global energy demands continue to escalate, the imperative for more efficient energy distribution and consumption systems becomes increasingly critical. The advent of smart grid technology offers a promising solution by integrating advanced communication, control systems, and information technologies into traditional power grids. This paper explores recent advances in smart grid optimization, focusing on techniques and technologies that enhance energy efficiency. We delve into the role of artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) in optimizing energy distribution and consumption. Furthermore, we discuss the challenges and future prospects of smart grid optimization.

Efficiency assessment of SOA-based computed torque control: A comparative analysis with NE-based approach

This paper proposes the spatial operator algebra-based computed torque control scheme applied to an anthropomorphic 3-degree of freedom robotic manipulator, which aims to reduce the computational cost of the classical methods and integrate the advantages of low computational cost into advanced robotic control systems. The computational cost is increased due to the calculations of the inverse dynamic and the feedback control loop. The spatial operator algebra (SOA) algorithm provides a systematic derivation, evaluation, and subsequent conceptual interpretation of the manipulator dynamics model. This paper presents a powerful and efficient solution for controlling the dynamics and trajectory of the manipulator. In order to show the efficiency of the solution, the Newton Euler (NE) based control schemes are also applied to the manipulator. The SOA-based controller significantly reduced the computational cost and performed approximately 60%–70% faster than the NE-based controller. Furthermore, different initial states, disturbances, and uncertainty tests are implemented and the SOA-based controller demonstrated successful performance under various conditions while maintaining a lower computational cost. In this study, the advantages and limitations of each method (SOA-based, NE-based) are evaluated and the potential benefits of using SOA in the computed torque control scheme are highlighted. The SOA-based controller, which was verified by simulation, is then implemented in real-time and showed successful performance.

High Speed Fighter UAV with Electric Coil Gun

The paper focus on "High-Speed Fighter Drone Using Electromagnetic Coil Gun and Dropping Mechanism" project represents a significant milestone in the realm of unmanned aerial vehicles (UAVs). This project aimed to design, develop, and demonstrate a cutting-edge high-speed fighter drone equipped with an electromagnetic coil gun and an advanced payload deployment mechanism. The primary objectives were to enhance drone maneuverability, achieve precision targeting capabilities, and enable efficient payload deployment, thus expanding the potential applications of UAV technology. This project involved an intricate blend of engineering, innovation, and technology integration. The drone's design and construction incorporated a lightweight yet durable frame, high-speed motors, and advanced flight control systems, ensuring exceptional agility and stability during flight. The electromagnetic coil gun, a pivotal component, was meticulously designed for precision targeting, delivering impressive accuracy and firing control. Moreover, the payload deployment mechanism, a vital feature of the system, was developed to ensure the precise and reliable delivery of payloads, expanding the drone's versatility across various domains, including defense, surveillance, and emergency response Throughout the project's lifecycle, rigorous testing, calibration, and data analysis were conducted to validate the drone's performance, accuracy of the electromagnetic coil gun, and efficiency of the payload deployment mechanism. The project yielded promising results, demonstrating the drone's exceptional flight performance, electromagnetic coil gun's precision targeting, and payload deployment's reliability. This project report provides a comprehensive overview of the project's objectives, methodologies, results, and significance. It explores the intricate system design, integration processes, testing procedures, and detailed analyses of outcomes. Furthermore, it discusses the challenges faced during development and the potential impact of this high- speed fighter drone in various industries. In conclusion, the "High-Speed Fighter Drone Using Electromagnetic Coil Gun and Dropping Mechanism" project represents a significant leap forward in UAV technology. Its successful realization opens up new possibilities for drone applications, promising advancements in precision targeting, payload deployment, and the overall versatility of UAVs in both civilian and military contexts

Energy Performance Analysis of the Renovation Process in an Italian Cultural Heritage Building

Renovating buildings with cultural heritage significance is an important step toward achieving sustainability in our cities. The benefits are not only energy-related but also encompass social aspects that make these renovations a high apriority. The present work investigates the renovation process of a cultural heritage building in the Municipality of Trento in Italy, specifically focusing on achieving energy savings and renewable energy integration by implementing various renovation actions. These renovation actions include improvements to the building envelope, such as roof insulation and window replacements. Additionally, the renovation actions for active systems involve the installation of a ground-source heat pump for heating/cooling coupled with a borehole thermal energy storage system, which is an innovative technology for the renovation of cultural heritage buildings. The electrical systems of the building are upgraded through the addition of standard rooftop photovoltaics, innovative building-integrated photovoltaics (shingles), and the installation of an LED lighting system. The baseline and the renovation scenarios are studied using the dynamic simulation tool INTEMA.building, written in the programming language Modelica. This tool simulates both the building envelope and the energy systems with a high level of detail, using advanced control systems and adjustable time steps. According to the simulation analysis, the primary energy demand is reduced by 30.49%, the final energy demand by 36.74%, and the net electricity demand by 8.72%. Results from this study can be useful to interested stakeholders (e.g., building owners, architects, construction companies, public agents, and urban planners) dealing with the renovation of cultural heritage and protected buildings. Also, the results can be exploited for estimating energy savings by applying advanced renovation strategies for cultural heritage buildings.

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.

Novel high-safety aeroengine performance predictive control method based on adaptive tracking weight

Increasing attention has been attracted to the dynamic performance and safety of advanced performance predictive control systems of the next-generation aeroengine. The latest research demonstrates that Subspace-based Improved Model Predictive Control (SIMPC) can overcome the difficulty in solving the predictive model in MPC/NMPC applications. However, applying constant design parameters cannot maintain consistent control effects in all states. Meanwhile, the designed system relies too much on sensor-measured data, and thus it is difficult to thoroughly validate the safety of the system because of its high complexity. This means that any potential hardware/software faults will endanger the engine. Therefore, this paper first presents a novel nonlinear mapping relationship to adaptively tune the tracking weight online with the change of Power Lever Angle (PLA) and real-time relative tracking error. Thus, without introducing additional design parameters, an Adaptive Tracking Weight-based SIMPC (ATW-SIMPC) controller is designed to improve the control performance in all operating states effectively. Then, a Primary/Backup Hybrid Control (PBHC) strategy with the ATW-SIMPC controller as the primary system and the traditional speed (Nf) controller as the backup system is proposed to ensure safety. The designed affiliated switching controller and the real-time monitor therein can be used to realize reasonable and smooth switching between primary/backup systems, so as to avoid bump transition. The PBHC system switches to the Nf controller when the ATW-SIMPC controller is wrong because of potential hardware/software faults; otherwise, the ATW-SIMPC controller keeps acting on the engine. The main results prove that the ATW-SIMPC controller with the optimal nonlinear mapping relationship, compared with the existing SIMPC controller, uplifts the dynamic control performance by 32% and reduces overshoots to an allowable limit, resulting in a better control effect in full state. The comparison results consistently indicate that the PBHC can guarantee engine safety in occurrence of hardware/software faults, such as sensor/onboard adaptive model faults. The approach proposed is applicable to the design of a model-based engine intelligent control system.

Direct learning of improved control policies from historical plant data

The continuous optimization of the operational performance of chemical plants is of fundamental importance. This research proposes a method that utilizes policy-constrained offline reinforcement learning to learn improved control policies from abundant historical plant data available in industrial settings. As a case study, historical data is generated from a nonlinear chemical system controlled by an economic model predictive controller (EMPC). However, the method’s principles are broadly applicable. Theoretically, it is demonstrated that the learning-based controller inherits stability guarantees from the baseline EMPC. Experimentally, we validate that our method enhances the optimality of the baseline controller while preserving stability, improving the baseline policy by 1% to 20%. The results of this study offer a promising direction for the general improvement of advanced control systems, both data-informed and stability-guaranteed.

Employing advanced control, energy storage, and renewable technologies to enhance power system stability

As the world witnesses a surge in the adoption of renewable energy sources to meet the surging global power demands, the dynamic and intermittent nature of these sources emerges as a significant hurdle. This article extensively explores the potential of advanced control systems, energy storage technologies, and renewable resources to fortify stability within power systems. Advanced control methodologies are strategically amalgamated with energy storage deployment and the utilization of renewable energy, to advance the reliability, predictability, and sustainability of power systems. The stability analysis, with a dedicated focus on Input-to-input-to-state stability (ISS), is conducted meticulously by applying the Lyapunov function and the Reciprocally Convex Approach, resulting in an impressive stability rate of 23.6%. Additionally, Positive Realness is substantiated by extracting Linear Matrix Inequalities (LMI) in the context of Enhancing Grid Stability with Wind Power. The study places particular emphasis on evaluating ISS and Passivity in both delayed and non-delayed systems, with a specific focus on neutral time-delay systems. This evaluation involves the selection of an appropriate Lyapunov-Krasovskii Functional (LKF) and its derivation, coupled with the integration of reciprocally convex methods, descriptive approach, and Jensen inequality. The outcomes of these analyses shed light on the causes of excess energy and its effective storage, along with highlighting the synergistic impact of integrating renewable sources and controlling grid frequency, voltage, and power in real time. The study also accentuates the robustness achieved through Enhanced Stability and the mitigation of Reduced Fluctuations, especially in the context of renewable energy sources.

An Automated Mechanised Robot for Handling Ricebags

A novel technology that overcomes the difficulties of manual labor has been created to increase productivity in large-scale food storage facilities. With an emphasis on rice bags in particular, this creative approach seeks to seamlessly replace human interaction in duties like picking, storing, moving, and monitoring food bags. The system uses an integrated approach that includes precision grippers, scissor lifts, Cartesian robots, autonomous guided vehicles (AGVs), and an advanced artificial intelligence-driven control system. Especially, the technology known as Simultaneous Localization and Mapping (SLAM) plays a crucial part in guaranteeing the smooth operation of the system. While Cartesian robots precisely perform complex jobs, autonomous mobility from the AGVs enables efficient and accurate movement within the storage space. Scissor lifts add to the system's flexibility in managing different storage arrangements. Rice sacks can be handled carefully and under control thanks to precision grippers. Artificial intelligence algorithms are employed by the overarching control system to facilitate the smooth coordination of various constituents. By combining these cutting-edge technologies, the system not only simplifies operations but also drastically lowers the need for manual labor, opening the door for a more effective and cutting edge method of managing food storage

Illuminating Progress: A Comprehensive Review of the Evolution of Phototherapy for Neonatal Hyperbilirubinemia.

This comprehensive review thoroughly examines the historical evolution, physiological foundations, and contemporary advancements in the application of phototherapy for neonatal hyperbilirubinemia. Neonatal hyperbilirubinemia, a common condition resulting from the immature hepatic processes in newborns, poses potential risks, including neurotoxicity, if left untreated. The review traces the historical progression from early recognition of neonatal jaundice to the development of various phototherapy modalities, showcasing the dynamic landscape of neonatal care. Emphasizing the physiological intricacies of bilirubin metabolism in neonates, the study underscores the vulnerability of newborns to hyperbilirubinemia due to delayed hepatic maturation. Phototherapy is a cornerstone in managing hyperbilirubinemia, demonstrating consistent efficacy in reducing unconjugated bilirubin levels. The implications for clinical practice are significant, offering healthcare professionals insights into tailoring treatment strategies based on individual neonatal characteristics and the severity of jaundice. Integrating advanced monitoring and control systems enhances the precision and safety of phototherapy. Recommendations for future research emphasize the need to investigate long-term outcomes, explore adjunctive therapies, and address resource limitations to ensure global access to effective neonatal care. Overall, this review contributes to the ongoing refinement of neonatal care practices, offering a comprehensive understanding of neonatal hyperbilirubinemia and its evolving treatment landscape.

Recent Advances in Thermal Management Strategies for Lithium-Ion Batteries: A Comprehensive Review

Effective thermal management is essential for ensuring the safety, performance, and longevity of lithium-ion batteries across diverse applications, from electric vehicles to energy storage systems. This paper presents a thorough review of thermal management strategies, emphasizing recent advancements and future prospects. The analysis begins with an evaluation of industry-standard practices and their limitations, followed by a detailed examination of single-phase and multi-phase cooling approaches. Successful implementations and challenges are discussed through relevant examples. The exploration extends to innovative materials and structures that augment thermal efficiency, along with advanced sensors and thermal control systems for real-time monitoring. The paper addresses strategies for mitigating the risks of overheating and propagation. Furthermore, it highlights the significance of advanced models and numerical simulations in comprehending long-term thermal degradation. The integration of machine learning algorithms is explored to enhance precision in detecting and predicting thermal issues. The review concludes with an analysis of challenges and solutions in thermal management under extreme conditions, including ultra-fast charging and low temperatures. In summary, this comprehensive review offers insights into current and future strategies for lithium-ion battery thermal management, with a dedicated focus on improving the safety, performance, and durability of these vital energy sources.

Interface Control and Status Monitoring of Electronic Information Equipment based on Nonlinear Data Encryption

An advanced electronic information equipment interface control and status monitoring system is proposed to ensure the fairness, objectivity, and security of information while identifying responsibility for traffic accidents. Through an in-depth analysis of the system's security requirements and the current landscape of information security technology, a robust security strategy is developed for each crucial system stage. A PC-based platform is developed for efficient data acquisition, secure processing, reliable transmission, and fortified storage, focusing on implementing nonlinear data encryption methods. Performance evaluation of the system involved rigorous testing using files ranging from 3MB to 10MB. The results of the proposed system revealed a significant improvement in the system's overall speed and efficiency, showcasing an average performance enhancement of one quarter compared to the original platform. The proposed system demonstrated an impressive 15% to 30% increase in processing speed, establishing its capability to ensure data integrity protection during information transmission, facilitate accurate identification of data recording equipment post-accident, and safeguard the security of stored data. The developed electronic information equipment interface control and status monitoring system effectively addresses critical challenges associated with ensuring data integrity and security in traffic accident investigations.

Evaluation of LVRT capability and stability analysis of VSC based advanced control approach for grid connected PV system under grid fault conditions

Short circuit faults are a prevalent issue in power systems, causing disruptions to the grid's normal operation. Dynamic behaviours of the conventional power systems during short circuit faults have been extensively studied and understood. The bulk of ongoing research and development are focusing on the dynamic performance of grid-connected renewable energy systems under these fault conditions, due to changes in the grid code and a decrease in system inertia. The development of effective control strategies to enhance the system's reliability during fault conditions is of paramount importance. In this paper, a two-stages grid-connected photovoltaic system (GCPV) having a rated power of 2 MW was created in the MATLAB/Simulink environment. The dynamic behaviour of the presented system was evaluated in two scenarios: steady state conditions and short circuit faults. A line-to-ground short circuit fault was created at the grid side, and its effect on the PV system's operation was observed. An advanced control system was designed to maintain stability during fault conditions. The results demonstrated the efficiency of the designated control system in minimizing the effects of short circuit faults on the GCPV system's function, and restoring the system promptly after the fault was cleared. Furthermore, considering modifications in grid regulations, the low voltage ride through (LVRT) capability of the designed system was analysed and validated according to the UK standards. The Total Harmonic Distortion (THD) level at the common coupling point was also analysed for voltage and current, remaining below the acceptable level of 5% as specified in the IEEE Std. 519.

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.

Application of intelligent controller to speed up vibration attenuation of a sandwich smart structure subjected to external excitation

Intelligent controllers represent a class of advanced control systems designed to adapt, learn, and make decisions based on changing environments and dynamic conditions. These controllers leverage artificial intelligence and computational techniques to enhance traditional control methods, providing a level of adaptability and sophistication that is particularly valuable in complex and uncertain systems. In this work for the first time, the Proportional–integral–derivative (PID) controller the intelligent controller is used to speed up vibration attenuation of a sandwich smart structure subjected to external excitation. The sandwich smart structure is made of a composite core and sensor and actuator patches. Using shear deformation theory, Hamilton’s principle, and compatibility equations, the governing equations of the sandwich smart structure under external excitations are obtained. After that using numerical solver and time-dependent solution procedure, the equations are solved. After obtaining the datasets via mathematical modeling, this kind of dataset is used as the input and output data to train, validate, and test the machine learning method to use it with low computational cost. After that, using this kind of machine learning method, future researchers can use it to estimate a control vibration of the sandwich structure after mechanical excitation. The results suggest some applicable recommendations to speed up the vibration attenuation of a sandwich smart structure subjected to external excitation.

Vehicle speed prediction using a convolutional neural network combined with a gated recurrent unit with attention

Vehicle speed prediction can facilitate many applications, such as optimizing vehicle propulsion systems and designing advanced driver assistance control systems. In a complex and variable traffic environment, many dynamic factors affect vehicle speed and make it difficult to predict accurately. The development of intelligent transportation systems and machine learning methods makes it possible to predict short-term vehicle speed accurately. A novel vehicle speed prediction model is proposed in this paper to improve prediction accuracy based on a deep learning method. A practical temporal and channel attention module (TCAM) is designed for convolutional neural networks (CNNs) to strengthen meaningful information and reduce the amount of unnecessary information. A gated recurrent unit (GRU) network with an attention mechanism is constructed to explore significant hidden relationships among time-series data with its memory function. These two subprediction models are fused to enhance the performance of vehicle speed prediction. Simulation experiments using IPG Carmaker software validate that the proposed model provides better predictive accuracy than traditional and existing vehicle speed prediction methods based on deep learning.

Heritage buildings management: the role of situational awareness and cyber-physical systems

AbstractSafeguarding and conservation of cultural heritage is an important issue that requires both theoretical knowledge and practical implementation to effectively protect this valuable asset. In recent years, new technologies have enabled the development of advanced monitoring and control systems that can provide more precise and timely information about the condition of heritage buildings. Additionally, these systems can collect and analyze a large amount of data, allowing decision-makers to make informed decisions regarding maintenance and other management processes. With a situational awareness model proposed for heritage building conservation as a starting point, this work outlines how the design and implementation of a cyber-physical system to support conservation processes can be done. The model combines decisional processes that involve both humans and automated systems and can be used as a guideline for the realization of decision support systems for the management of heritage buildings. The paper provides a case study describing the steps for the realization of a cyber-physical system for automated monitoring and control at the Royal Palace of Carditello, a prominent cultural attraction in Italy.

Analytical Study on Mechanical Energy Recovery and Utilization of New Energy Vehicles

The pursuit of energy efficiency and sustainability in the automotive industry has become increasingly urgent in response to growing environmental concerns. Herein, this paper focuses on the critical challenge of energy dissipation in new energy vehicles, particularly during braking and suspension vibrations. The paper presents two innovative energy recovery solutions aimed at reducing energy losses and enhancing sustainability. In the realm of sustainable transportation, this study critically evaluates the optimization strategies for braking energy recovery in new energy vehicles. A key recommendation is the integration of a permanent magnet synchronous motor combined with an advanced fuzzy control system. Such an amalgamation not only heightens energy efficiency but also substantially extends the driving range of electric vehicles. Beyond braking energy, the research casts light on an often-overlooked energy source: the vibrational energy from vehicle suspensions. By introducing an innovative electromagnetic damper design coupled with the strategic incorporation of piezoelectric materials, the study unveils avenues to both improve suspension dynamics and transform vibrational energy into usable electrical power. While there are current technological and economic challenges, the ongoing innovations in the design and control processes of new energy vehicles highlight the profound potential within this domain. The insights offered in this context further emphasize the critical need to expand the horizons of sustainable transportation design and management approaches.

Novel Energy-Saving Strategies in Apple Storage: A Review

Storing apples for up to a year is a well-established practice aimed at providing a continuous, locally produced fruit supply to consumers and adapting to market trends for optimized profits. Temperature control is the cornerstone of postharvest conservation, and apples are typically kept at temperatures ranging from 0 to 3 °C. However, the energy-intensive process of the initial cool-down and subsequent temperature maintenance poses significant financial challenges with adverse effects on the carbon footprint. Higher storage temperatures could reduce cooling-related energy usage but also pose the risk of enhanced ripening and quality loss. This work explores different storage technologies aiming to reduce energy consumption, such as 1-methylcyclopropene, ultra-low oxygen, and a dynamically controlled atmosphere with raised temperatures. The integration of advanced monitoring and control systems, coupled with data analytics and energy management, in apple storage is also discussed. These strategies can be implemented without cost-intensive construction measures in standard storage facilities. Furthermore, beneficial side effects of higher storage temperatures in terms of a reduced occurrence of storage disorder symptoms and higher maintenance of quality attributes are also discussed for this special issue on sustainable horticultural production systems and supply chains.