Development Of Control System Research Articles

Determining Resident Status for Taxation in the USA

This paper examines international taxation practices, in particular focusing on the taxation of individuals as well as tax tools used in industrialized countries to optimize tax collection and stimulate economic development. It is also directly relevant for representatives of ministries and departments involved in managing the state budget. The experience of the United States, with its developed and comprehensive tax control system, could be useful in organizing more effective taxation of foreign individuals, including migrant workers residing on the territory of the Russian Federation.

Development of a DC motor control system by Xilinx system generator

This study introduces a numerical approach utilizing a Xilinx System Generator to develop a DC motor control system, showcasing a significant ability to regulate and enhance motor speed. We developed the proposed system in the MATLAB/SIMULINK environment, utilizing a collection of blocks generated by the Xilinx System Generator. We developed and simulated a proportional-integral (PI) controller and pulse width modulation (PWM). Subsequent to simulation, we can transfer the HDL code from the Xilinx System Generator block to a Field Programmable Gate Array (FPGA) board using Vivado. The system can produce accelerated PWM signals, thereby enhancing the control of the DC motor. Because of the FPGA board's superior processing capabilities, it was crucial to design and simulate the control system using Sysgen. We proposed the FPGA board to address this issue, facilitating the efficient and rapid execution of the control signals. This work represents a significant advancement in the enhancement of DC motor control systems. It shows how well Matlab/SIMULINK and Xilinx System Generator can work together to make PI and PWM controllers. These findings present compelling possibilities for future applications in power electronics and industrial automation.

Energy absorption capacity and vibration-control responses of smart perovskite solar cells under external excitation via intelligent velocity feedback control algorithm

Smart perovskite solar cells offer engineers a highly efficient, cost-effective, and flexible solution for renewable energy. Their tunable properties, ease of fabrication, and potential for integration into various surfaces make them ideal for sustainable power generation, driving innovation in clean energy technologies and energy-efficient designs. This comprehensive approach ensures that smart perovskite solar cells can withstand external mechanical excitations more effectively and maintain their integrity over time. Due to the mentioned problem, using a data-driven solution approach, this work presents an intelligent controller for reducing transitory vibrations in smart perovskite solar systems outfitted with functionally-graded graphene origami-enabled auxetic metamaterial (FG-GOEAM) layer in the metal position, sensor, and actuator face sheets. In order to effectively detect and react to vibrations, the system integrates sensor and actuator face sheets. The controller applies the converse and direct piezoelectric equations together with interpolation functions to coupled motion equations in order to efficiently decrease vibration amplitudes. By integrating intelligent control algorithms, system parameters may be monitored and adjusted in real-time to maximize stability and performance. The piezoelectric actuator applies a control force to promptly halt the vibration of the structure. This study determines the controlled dynamic response of the smart perovskite solar cell construction by the use of a velocity feedback control algorithm. Numerical simulations and validation show that the suggested method is effective in mitigating transient vibrations and improving structural integrity. Through the development of intelligent control systems for smart perovskite solar cells, this study offers interesting solutions for engineering applications by reducing transitory vibrations.

ESTIMATION OF VEHICLE YAW SPEED USING PHOTOGRAMMETRY OF TIRE TRACKS AT TRAFFIC ACCIDENT SCENES

Background: This article presents a study on the use of photogrammetry to determine the critical yaw radius of automotive vehicles and, consequently, estimate the skidding speed. Photogrammetry is a non-invasive measurement technique that uses images to digitally reconstruct three-dimensional objects and analyse their geometric characteristics. Aim: The study aims to capture and process images of tire tracks from a vehicle in skidding conditions at the scene of a traffic accident to calculate the critical yaw radius and determine the skid speed. Methods: Images were rectified using specialized software and then processed to calculate the critical yaw radius, allowing the skid speed to be determined based on specific mathematical models. Results: The results obtained demonstrate the predictions and accuracy of photogrammetry as an effective tool for vehicle safety analysis and vehicle dynamics studies in skidding conditions. Discussion: This approach not only provides important information for the development of stability control systems, but also contributes to a deeper understanding of the specificities related to vehicle safety in critical driving situations. Conclusions: Photogrammetry proves to be a valuable method in enhancing vehicle safety analysis and understanding vehicle dynamics during skidding incidents.

An Innovative Applied Control System of Helicopter Turboshaft Engines Based on Neuro-Fuzzy Networks

This study focuses on helicopter turboshaft engine innovative fault-tolerant fuzzy automatic control system development to enhance safety and efficiency in various flight modes. Unlike traditional systems, the proposed automatic control system incorporates a fuzzy regulator with an adaptive control mechanism, allowing for dynamic fuel flow and blade pitch angle adjustment based on changing conditions. The scientific novelty lies in the helicopter turboshaft engines distinguishing separate models and the fuel metering unit, significantly improving control accuracy and adaptability to current flight conditions. During experimental research on the TV3-117 engine installed on the Mi-8MTV helicopter, a parametric modeling system was developed to simulate engine operation in real time and interact with higher-level systems. Innovation is evident in the creation of the failure model that accounts for dynamic changes and probabilistic characteristics, enabling the prediction of failures and minimizing their impact on the system. The results demonstrate high effectiveness for the proposed model, achieving an accuracy of 99.455%, while minimizing the loss function, confirming its reliability for practical application in dynamic flight conditions.

Development and Verification of Microclimate Control System for Enhanced Driver Comfort and Safety Based on Skin Resistance Measurements

Abstract This study deals with the design and construction of a device that enhances driver comfort and safety by automatically adjusting the microclimate in the vehicle cabin based on real-time skin resistance measurements. Using electrodes attached to driver‘s skin and an Arduino microcontroller, the system monitors and evaluates skin conductivity and adjusts heating, ventilation, and air conditioning (HVAC) settings accordingly. Experimental verification in laboratory conditions demonstrated device‘s functionality in changing microclimate parameters. Preliminary results suggest a potential correlation between baseline skin resistance values and the magnitude of observed changes in response to ambient conditions. Subjects with lower baseline skin resistance (≤100,000 Ω) showed smaller changes compared to those with higher baseline resistance (≥100,000 Ω). The current results are graphically processed as the course of skin resistance changes depending on the changing parameters of microclimate.

Improving the power production of Mutriku Wave Power Plant throughout tuning the actual generator and damping valve limits

The Mutriku Wave Power Plant (MWPP) has successfully operated since 2011, demonstrating remarkable operational stability with minimal issues of OWC technology in breakwaters. So far, the design, selection, and operation ranges of the generators at MWPP prioritized reliability. However, once the viability of the technology has been demonstrated, it is important to explore ways to increase energy production with the current design of the technology.The objective of the present work is to improve the power production in MWPP throughout the Power take-off (PTO), increasing the operational limits of the generator and of the damping valve, which is located between the air chamber and the turbine. To this aim, new sensors have been installed in the plant, such as an inlet pressure sensor, to characterise the behaviour of the PTO. Investigations into the power produced and the plant availability have concluded that modifying control laws of the current configuration could help to increase power production under generator thermal operation ranges.The numerical results presented in the paper demonstrate that the potential benefit to the overall energy production of the MWPP could be significant, leading to greater advancements in the feasibility of wave energy technologies. The analysis of changes and improvements into the MWPP control strategies will provide further guidance into the development of novel wave energy control systems and components for future testing.

Modelling and set-point definition for the development of a joint control system of two interconnected wastewater treatment plants and its application in practice

ABSTRACT The use of activated sludge models (ASMs) is a common way in the field of wastewater engineering in terms of plant design, development, optimization, and testing of stand-alone treatment plants. The focus of this study was the development of a joint control system (JCS) for a municipal wastewater treatment plant (mWWTP) and an upstream industrial wastewater treatment plant (iWWTP) to create synergies for saving aeration energy. Therefore, an ASM3 + BioP model of the mWWTP was developed to test different scenarios and to find the best set-points for the novel JCS. A predictive equation for the total nitrogen load (TN) coming from the iWWTP was developed based on real-time data. The predictive TN equation together with an optimized aeration strategy, based on the modelling results, was implemented as JCS. First results of the implementation of the JCS in the real environment showed an increase in energy efficiency for TN removal.

IoT-based home control system using NodeMCU and Firebase

Integrating Internet of Things (IoT) technology in home control systems has led to a significant advancement in the control and monitoring of home appliances and environmental conditions. This study presents the development of an IoT-based home control system that uses the NodeMCU ESP8266 microcontroller to manage several home appliances. This system connects to a Firebase real-time database to store or retrieve data in real-time. The user interacts with the IoT system through a mobile application named My Home, developed using Java programming language, and this mobile application is also interfaced with the Firebase cloud server. The developed IoT system allows users to control home appliances such as lights, sockets, fans, and cookers, and it also provides real-time updates of environmental parameters, such as temperature and humidity, which are measured by a DHT11 sensor. In addition, a PIR motion sensor was integrated into the system to enhance the home's security by detecting intrusion. The system's hardware functionality is based on a written Arduino code, which establishes Wi-Fi connectivity to a predefined network, communicates with the Firebase database, handles appliance control and manages sensor data. This IoT-based home control system shows the potential of integrating microcontrollers with cloud services to create a smart, responsive and user-friendly home control platform. The developed IoT system offers a foundation for advancements, thereby making it a valuable contribution to the growing field of smart home technology.

AINVESTIGATION OF COMPUTER VISION CAPABILITIES IN AUTONOMOUS UNMANNED AERIAL VEHICLES CONTROL SYSTEMS

The development of modern hardware and software has led to a rapid increase in the use of unmanned aerial vehicles, primarily aircraft. One of the promising areas for improving the efficiency of such platforms is the development of autonomous control systems, eliminating the need for human operators. The article presents the results of a study on the possibility of constructing elements of an automatic motion control system based on computer vision for unmanned aerial vehicles. The authors used comparative analysis to evaluate the advantages of two popular instruments: YOLO and SSD. They have also collected data for training the chosen model and tested it in various conditions, described the methodology for creating a training set and presented the model testing results on video images captured by a UAV's camera. The test results confirm that the YOLOv8n model is suitable for object detection on the UAV board using a Raspberry Pi 4 Model B as the single-board hardware platform. The object detection accuracy was from 80% to 90%, with the power consumption of 15-25 watts.

Application of PID Control Technology in Unmanned Aerial Vehicles

Abstract. The rapid advancement of unmanned aerial vehicle (UAV) technology underscores the essential role of PID (proportional-integral-derivative) control in ensuring flight stability, particularly through precise motor speed adjustments. Thus, this paper systematically analyzes the fundamental principles and limitations of traditional PID control, further highlighting its insufficient adaptability to dynamic external conditions, which includes load fluctuations and wind disturbances. In response to these challenges, it explores adaptive control strategies such as self-tuning PID and fuzzy logic PID, points out how these advanced methods can effectively improve flight stability by dynamically adjusting control parameters, and demonstrates their superiority over traditional PID through comparative analysis. In addition, the paper anticipates future developments in UAV control systems, thereby emphasizing the integration of artificial intelligence and machine learning technologies into PID control. And this integration seeks to optimize control strategies and markedly enhance the autonomous decision-making capabilities and adaptability of UAVs. Meanwhile, the paper also predicts the development trend of hybrid control systems, that is, combining PID with other advanced control methods (such as linear quadratic control) to enhance the overall control capabilities of UAVs. In short, it underscores the necessity for continuous innovation and in-depth research in response to the dynamic flight environment and the diverse mission requirements.

Inhibitory Control in Late Childhood as a Predictor of Antisocial Behavior in Adolescence and the Role of Social Context.

Understanding the factors contributing to adolescent antisocial behavior is crucial for effective interventions. Protracted development of cognitive control systems supporting inhibitory control may be linked to increases in adolescent antisocial behavior, suggesting the promotion of inhibitory control as a potential preventative strategy. Concurrently, social contextual factors, including peer relationships, parent-child dynamics, and the neighborhood environment, may exacerbate or buffer the risk posed by low inhibitory control. In a large, longitudinal sample of youth from racially and ethnically diverse low-income families (N = 731), we examined the association between inhibitory control (age 10.5) and antisocial behavior (age 14) and explored contextual factors (neighborhood, peer relationships, parent-child relationship) as potential moderators. Lastly, we investigated whether a randomized controlled trial of the Family Check-Up (FCU) intervention started at age 2 predicted a decreased youth report of antisocial behavior in adolescence via increased inhibitory control in late childhood. We found that lower inhibitory control in late childhood predicted increased antisocial behavior in adolescence. For youth with low inhibitory control, living in a dangerous neighborhood or associating more with deviant peers increased the risk for adolescent antisocial behavior. Finally, the FCU intervention indirectly reduced youth-reported adolescent antisocial behavior via enhancing inhibitory control in late childhood, and the indirect effect was strongest for youth in risky contexts (e.g., low parental knowledge and control). Although risky contexts can exacerbate individual risks related to deficits in inhibitory control, greater inhibitory control may be a protective factor. Additionally, early childhood interventions can improve inhibitory skills and decrease the risk of adolescent antisocial behavior, particularly for youth within risky contexts.

Development and demonstration of advanced predictive and prescriptive algorithms to control industrial installation

This paper explores the use of sophisticated, predictive AI algorithms for monitoring and optimizing industrial installations of CFB power plant. The effectiveness of the system was shown by applying it to a circulating fluidized bed (CFB 1300) power unit. A customized optimization algorithm was developed to manage the oxygen distribution within the combustion chamber. Implementing the developed control system methodology adjusted the fuel distribution, which in turn impacted the overall performance of the boiler. The approach was evaluated under different boiler operating scenarios, including simulated fuel line malfunctions. The devised methodology enables a reduction of approximately 17% in oxygen distribution imbalance within the combustion chamber when a failure was detected. Furthermore, the optimization algorithms facilitate a seamless adjustment in fuel loads, maintaining the necessary oxygen and temperature distribution at the control plane. Moreover, the capabilities of this system were demonstrated for the automatic identification of malfunctions within two crucial parts of the power unit. The initial issue pertains to a fault in the internal phase insulator of the block transformer, and the second occurrence involved the proactive identification of a membrane wall leak over 13 h before its failure.

A review on exploring the potential of PVA and chitosan in biomedical applications: A focus on tissue engineering, drug delivery and biomedical sensors.

Polymers have been integral to the advancement of biomedicine, owing to their exceptional versatility and functionality. Among these, polyvinyl alcohol (PVA) and chitosan both natural polymers stand out for their remarkable biocompatibility, biodegradability, and unique properties. This review article provides a comprehensive examination of the diverse applications of PVA and chitosan in three pivotal areas: tissue engineering, drug delivery, and biosensors. In tissue engineering, the discussion centres on how PVA and chitosan are engineered into scaffolds that not only support cell growth and differentiation but also promote tissue regeneration by closely mimicking the extracellular matrix. These scaffolds offer the necessary mechanical strength and adaptability for various biomedical applications. For drug delivery, the article delves into the development of sophisticated controlled release systems and targeted drug carriers, highlighting the polymers' customizable properties and their mucoadhesive nature, which make them highly effective across multiple drug delivery methods. Furthermore, the potential of PVA and chitosan in biosensor technology is explored, particularly their ability to interact with biomolecules and their intrinsic conductivity attributes that are essential for creating sensitive, reliable, and biocompatible sensors for medical diagnostics. By synthesizing recent research findings and suggesting future research directions, this review underscores the versatility and critical role of PVA and chitosan in pushing the boundaries of biomedical innovation. It offers valuable insights for researchers and scientists dedicated to advancing healthcare through the application of these natural polymers.

Advances in flight control systems for modern commercial aircraft

Abstract. In consequence of the accelerated evolution of aviation technology, the flight control systems of commercial aircraft have undergone substantial technological advancement and system optimization. This paper presents a comprehensive analysis of the recent advancements and future trends in flight control systems for commercial aircraft. The fundamental concepts and historical evolution of flight control systems are delineated, with a particular emphasis on the evolution of autopilot systems and the technological advancements of flight control systems (FCS). Furthermore, it examines innovations in flight control algorithms, including adaptive control and intelligent algorithms, and investigates the impact of fault-tolerant design on flight safety. In terms of system integration, the integration of flight control systems with navigation and communication systems, as well as performance enhancements resulting from system optimization, is explored. In addition, it examines the potential impact of intelligence and networking on future flight control systems, with a particular focus on the prospects for the application of artificial intelligence, quantum computing, and new material technologies. The synthesis of these areas provides a systematic understanding of the technological evolution and future development of flight control systems for commercial aircraft.

Rapid Optimization of Active Disturbance Rejection Controller Parameters for Quadrotor UAVs Using Kriging Surrogate Modeling

The Active Disturbance Rejection Controller (ADRC), celebrated for its superior resistance to interference, presents itself as an exemplary solution for the development of control systems that are designed to accommodate substantial changes in payload weight for quadrotor Unmanned Aerial Vehicles (UAVs) and to endure robust side winds along with other challenging operational scenarios. Despite the inherent complexity due to the numerous parameters required for the configuration of the ADRC, an innovative method utilizing the Kriging surrogate optimization algorithm has been introduced to automate and expedite the generation of these controller parameters. The development of the ADRC begins with the dynamics model of the quadrotor UAV, followed by the identification of key design parameters. These parameters are then rapidly optimized through the Kriging surrogate optimization algorithm. The controller’s effectiveness is confirmed by implementing the ADRC on Pixhawk flight control hardware, with a comparative analysis of the attitude response under various operating conditions, thereby validating the ADRC’s superior anti-disturbance performance.

Research on Autonomous Navigation of Unmanned Aerial Vehicles Based on Correlation-Based Image Comparison Methods

Relevance. Autonomous navigation of unmanned aerial vehicles (UAVs) is one of the key challenges in the modern aerospace industry. Specifically, for small UAVs, the task of autonomous navigation becomes even more complex due to limitations in computational resources and sensor capabilities. Optimizing navigation methods under such conditions is a pressing issue that requires solutions capable of providing high performance with minimal resource consumption.Objective. Improving the efficiency of autonomous navigation for small UAVs by using correlation methods for image comparison. Achieving this objective is linked to the development and evaluation of algorithms that provide high-speed and accurate navigation with limited computational resources. The work uses methods such as the autocorrelation function, Pearson correlation, the structural similarity index, and a simple neural network for image comparison.Solution. The research showed that the autocorrelation-based approach demonstrates the best performance under low computational resources. It ensures high-speed processing of the entire image and shows optimal detection accuracy. Compared to other methods presented in the study, the autocorrelation approach is capable of working not only with noise in the "reference" map but also of using alternative areas with altered patterns as detected regions.The scientific novelty of the study is determined by the systematic comparison of various methods applied to the task of image comparison for small UAVs with limited computational resources. Unlike well-known works in the field of correlation-extreme systems, this research focuses on the use of a "reference map" and a search area, representing two different images of the same terrain taken from different sources. This is a key difference, as most methods are not highly efficient in processing such images where patterns may differ significantly.The practical significance of the developed algorithm lies in the fact that the proposed autocorrelation-based method can be used by developers of autonomous small UAV control systems to reduce computational load and increase data processing speed.

Lumped Parameter Modelling of Common Rail High-Pressure Fuel Injection Pump

Abstract High injection pressure is crucial for both modern and future diesel engines, resulting in enhanced performance, fuel efficiency, and reduced emissions. Fuel atomization, combustion optimization, torque control, and engine NVH are among the critical topics directly influenced by the injection strategy. Common rail diesel injection pumps play a pivotal role in delivering fuel at high pressure, with performance metrics such as flow rate and maximum pressure defining the pump’s technology level. Development efforts have consistently focused on improving performance and efficiency since the inception of common rail systems. Future challenges for diesel engines and injection pumps include meeting carbon neutrality goals, which may require adapting to new fuels, engine control strategies (such as hybrid powertrains and drivetrains), and pump drive concepts. In this context, simulating pump operation is essential for optimizing design, predicting performance, and developing control systems. The article deals with the mechanical-hydraulic modelling of a high-pressure pump. The model is based on the lumped parameter simulation of a cylinder-piston pair interacting with intake and delivery volumes via automatically opening valves. Validation of the model relies on dedicated experimental investigation campaigns, enabling measurement of pressure in the pump’s working chamber and high-speed visualization of the intake valve’s position relative to the pump shaft’s angular position. The article reports a detailed description of the model, the experimental approach, and the synthesis of the results.

Control of Carbon Dioxide Bivalent Heat Pump on Heating of Buildings. Part II

The control system of a bivalent heat pump (BHP), using as low potential heat (LPH) sources both the heat of the return water of the network and the heat of the outside air, is considered. The aim of the work is to determine the parameters of the thermodynamic cycle of the heat pump, which would ensure the operation of the heat pump at variable temperatures and flow rates of the re-frigerant in the load. The set goal is achieved by solving the following tasks: analysis of the methods of synthesis of systems at variable load, analysis of the operation of the system at random disturb-ances, development of the heat pump automatic control system (ACS). The main result of the work is: the scheme of the heat pump, which can work with variable pressures of the evaporator and the gas cooler, as well as the technical solution, in which the enthalpy difference at the evaporator remains constant regardless of the outside air temperatures Significance of the obtained results consists in crea-tion of the BTN scheme, which allows to provide both qualitative and qualitative-quantitative laws of regulation of thermal regime of a building at increase of SOR of a heat pump, thanks to rational choice of a temperature schedule of regulation. Local storage using electrochemical accumulators at the re-quired capacity would be very expensive and would make the entire installation unprofitable. The only reasonable solution is interaction with the grid, which requires, in addition to the technical means of interfacing, the presence of appropriate regulations governing the transfer of electricity generated by the local facility to the grid, especially if the generation will be carried out throughout the year. Keywords: bivalent heat pump scheme, automatic control, random disturbances, reliability, district heating, carbon dioxide.

Control strategy for Twin-turbine generator stabilized platform based on global linearization of DC-Bus voltage

Abstract The stabilized platform is considered a crucial component for the toolface angle control of rotary steering tools. The twin-turbine generator stabilized platform (TGSP) control system aims to achieve stable voltage control of the turbine generator and servo control of the toolface angle. Existing methods, such as feedback linearization, have significant computational demands and are highly model-dependent, which limits their effectiveness under significant mud flow fluctuations. To address these shortcomings, this paper introduces a linear active disturbance rejection voltage controller (LADRC), using the square of the bus voltage as the state variable to achieve global linearization of the bus voltage. This method reduces dependence on precise mathematical models and enhances both robustness and efficiency. Simulation results demonstrate that the proposed method can achieve high-performance voltage control and servo control, providing a reference for the development of TGSP control systems.