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In this work, the fabrication of Rochelle salt based piezoelectric structures is illustrated. Structures composed of paper and Rochelle salt are easily manufactured using simple processes. Both manufacturing and the material itself are environmental friendly. Additionally Rochelle salt is biocompatible. In the paradigm of a cleaner piezoelectric technology, the fabrication of active sensing or actuating devices is developed. Thus processing method, material and piezoelectric properties have been studied: (1) pure crystals are used as acoustic actuator, (2) properties of paper impregnated with Rochelle salt are detailed, (3) charge generation is demonstrated on the impregnated material. Actuating and sensing devices are reported in order to highlight the potential of this green piezoelectric material. [ABSTRACT FROM AUTHOR]

The paper considers a digital model of a wheeled agricultural tractor, as well as algorithms for automatic control of the engine and transmission. Simulation of the working conditions was performed in MATLAB and its applications, Simulink, Sim-scape. The tractor model is based on a systemic approach and includes such subsystems as the engine, transmission, undercarriage, hook load, and control actions from the operator, as well as functions of logic and optimisation for gear shifting. With the help of fundamental blocks of the Matlab package, models of tractor physical components are created. The fundamental blocks of the package serve to model the physical components of the tractor: engine, transmission control system, the transmission that includes friction clutches, gears, shafts ties with the necessary characteristics. Simulation of the working processes of a tractor unit is performed with optimisation of the laws of engine and transmission control according to the criterion of fuel efficiency and maximum power usage. The control system parameters were optimised in the transport mode, as well as in the maximum power mode in the traction range. As an illustration of the use of this analysis method, a solution to the problem of optimisation of an agricultural wheeled tractor operating modes is given. The results of this mathematical simulation are presented in this paper. Parameters of tractor operation control algorithm, which provide minimum fuel consumption in tractor operating cycles, are given. The research results showed the effectiveness of the proposed algorithms and the digital model. The presented digital model allows to debug the operating tractor control algorithms and offers an engineering practice for optimising automatic control of the agricultural tractor engine and transmission. [ABSTRACT FROM AUTHOR]

This paper presents a receding horizon optimal control (RHOC) method with an economic objective function for balancing the cost of resources (resource use × cost) with income through yield (yield × product price). This paper considers the two elements that determine the income through yield. The first element is yield and associated fruit development. A new, computationally viable, approach to model the income through yield is proposed and its prediction accuracy with respect to the original model is evaluated. The new approach employs a model that predicts at each time step, the future income through yield based on the assimilates partitioned to the fruits at the current time step. Simulations suggest that the assumptions made to arrive at the model for the new approach, do not significantly affect the accuracy of the predictions. The second element considered in this paper is the product price and the uncertainty inherent in its forecasts. Historical product price data are used to generate artificial product price forecasts. An uncertainty analysis, in combination with the artificial product price forecasts, showed that the product price forecast error does not considerably affect the optimised control strategy. Season-wide simulations with RHOC suggest that the product price forecast error does not considerably affect the value of the economic objective function. • A computationally viable approach to model the income through yield is proposed. • The new approach to model income through yield allows for RHOC with a short horizon. • Historical product price data are used to create artificial product price forecasts. • Product price forecast errors do not considerably affect the control strategy. [ABSTRACT FROM AUTHOR]

Mathematical models of energy systems have been mostly represented by either linear or nonlinear ordinary differential equations. This is consistent with lumped-parameter dynamic system modeling, where dynamics of system state variables can be fully described only in the time domain. However, when dynamic processes of energy systems display both temporal and spatial evolutions (as is the case of distributed-parameter systems), the use of partial differential equations is necessary. Distributed-parameter systems, being described by partial differential equations, are mathematically (and computationally) much more difficult for modeling, analysis, simulation, and control. Despite these difficulties in recent years, quite a significant number of papers that use partial differential equations to model and control energy processes and systems have appeared in journal and conference publications and in some books. As a matter of fact, distributed-parameter systems are a modern trend in the areas of control systems engineering and some energy systems. In this overview, we will limit our attention mostly to renewable energy systems, particularly to partial differential equation modeling, simulation, analysis, and control papers published on fuel cells, wind turbines, solar energy, batteries, and wave energy. In addition, we will indicate the state of some papers published on tidal energy systems that can be modelled, analyzed, simulated, and controlled using either lumped or distributed-parameter models. This paper will first of all provide a review of several important research topics and results obtained for several classes of renewable energy systems using partial differential equations. Due to a substantial number of papers published on these topics in the past decade, the time has come for an overview paper that will help researchers in these areas to develop a systematic approach to modeling, analysis, simulation, and control of energy processes and systems whose time–space evolutions are described by partial differential equations. The presented overview was written after the authors surveyed more than five hundred publications available in well-known databases such as IEEE, ASME, Wiley, Google, Scopus, and Web of Science. To the authors' best knowledge, no such overview on PDEs for energy systems is available in the scientific and engineering literature. Throughout the paper, the authors emphasize novelties, originalities, and new ideas, and identify open problems for future research. To achieve this goal, the authors reviewed more than five hundred journal articles and conference papers. [ABSTRACT FROM AUTHOR]

This paper describes a human interface design concept of automated driving systems. Control authority while using the automated systems will be back to the driver due to the operational limitation of the automated systems of Level 2 and 3. The human interface design of the automated systems in the transition process is essential for enhancing the driver's acceptance of the information provided or the automated controls. In this study, we propose the interface design of the “Request to Intervene (RtI)” that is determined based on the difference between a driver capability and a task demand (contributed by the task‐capability interface model). This concept suggests that when the driver capability exceeds the task demand before the transition mode, the transition will be successfully completed; however, the control authority cannot return to the driver smoothly and comfortably if the task demand is higher than the driver capability. In this paper, occlusion method is introduced as an estimation method of the task demand while driving. Evaluation indices for applying to a driver monitoring system is introduced, which measures the driver capability while the automated system is active. Driving simulator experiments were conducted to investigate the relationship between the driver capability and the task demand under manual drives. [ABSTRACT FROM AUTHOR]

This paper proposes a more efficient water monitoring and control system for water utilities to address the current water wastage problem, utilizing IoT and Android applications. The integrated system aims to enhance real-time data collection, analysis, and remote management, empowering water utilities to optimize resource usage and mitigate environmental impact. Conventional water tanks cannot monitor or control the water level in the tank, leading to a significant amount of wastage. Implementing smart water tank systems with automated monitoring and control features can not only prevent wastage but also enhance overall water management efficiency. Ultrasonic sensors are used to measure the water level, while other parameters such as pH, TDS, and turbidity of the water should be calculated. Additionally, these sensors play a crucial role in ensuring accurate and real-time monitoring of water quality in various applications, such as industrial processes, aquaculture, and environmental monitoring. The integration of ultrasonic technology for water level measurement enhances the overall efficiency and reliability of water management systems. The calculated values from the sensors can be processed by the microcontrollers and uploaded to the internet through the Wi-Fi module (ESP 8266). Some other technologies have certain drawbacks in one way or another. The removal of these shortcomings and the provision of an efficient and economical solution have been the main focus of this paper. Through extensive research and innovation, our team has strived to overcome these limitations, paving the way for a more robust and seamless technological landscape. [ABSTRACT FROM AUTHOR]

Automatic light control systems have garnered significant attention for their potential to enhance energy efficiency and user convenience across various settings. This paper provides a comprehensive overview of these systems, elucidating their principles, components, applications, and associated benefits. The primary objective of automatic light control systems is to intelligently regulate lighting levels based on environmental factors such as occupancy, ambient light, and time of day. Various sensors, including Passive Infrared (PIR), ultrasonic, and photoresistors, are commonly employed to detect changes in the surroundings and trigger appropriate lighting adjustments. Advanced control algorithms and communication protocols facilitate seamless integration with building automation systems, enabling centralized management and optimization of lighting across diverse zones. Moreover, the advent of smart lighting solutions leveraging Internet of Things (IoT) platforms enables remote monitoring and control, empowering users to personalize lighting preferences and further optimize energy consumption. The paper critically reviews the key features, benefits, and challenges associated with automatic light control systems, underscoring their pivotal role in promoting sustainability, comfort, and cost-effectiveness in both residential and commercial environments. Additionally, the paper discusses future research directions, highlighting opportunities for innovation in sensor technology, data analytics, and human-centric lighting design to propel the capabilities and adoption of these systems. [ABSTRACT FROM AUTHOR]

The following paper was selected for the 2018 IEEE Transactions on Control Systems Technology Outstanding Paper Award from papers that appeared in 2016–2017: “Multimode Q Control in Tapping-Mode AFM: Enabling Imaging on Higher Flexural Eigenmodes” by Michael G. Ruppert and S. O. Reza Moheimani Vol. 24, No. 4, pp. 1149–1159, July 2016. [ABSTRACT FROM AUTHOR]

To ensure optimal use of images while preserving privacy, it is necessary to partition the shared image into public and private areas, with public areas being openly accessible and private areas being shared in a controlled and privacy-preserving manner. Current works only facilitate image-level sharing and use common cryptographic algorithms. To ensure efficient, controlled, and privacy-preserving image sharing at the area level, this paper proposes an image partition security-sharing mechanism based on blockchain and chaotic encryption, which mainly includes a fine-grained access control method based on Attribute-Based Access Control (ABAC) and an image-specific chaotic encryption scheme. The proposed fine-grained access control method employs smart contracts based on the ABAC model to achieve automatic access control for private areas. It employs a Cuckoo filter-based transaction retrieval technique to enhance the efficiency of smart contracts in retrieving security attributes and policies on the blockchain. The proposed chaotic encryption scheme generates keys based on the private areas' security attributes, largely reducing the number of keys required. It also provides efficient encryption with vector operation acceleration. The security analysis and performance evaluation were conducted comprehensively. The results show that the proposed mechanism has lower time overhead than current works as the number of images increases. [ABSTRACT FROM AUTHOR]

In order to scientifically and rationally evaluate the susceptibility to landslide hazards in Tekes County, Yili State. This paper takes Tekes County in Xinjiang as an example, on the basis of a comprehensive analysis of the regional geological environment conditions and the distribution pattern and formation conditions of geological disasters, using the data of geological disaster points (landslide center points), and through the correlation matrix calculation of the evaluation factors, the nine evaluation factors with larger absolute values of correlation coefficients were determined to construct the evaluation system of the susceptibility to landslide geological hazards in Tekesi County. Combining the information quantity method and the entropy value method, using the weights determined by the entropy value method, the information quantity method is used to calculate the information quantity value of each factor within the factor, calculate the susceptibility index of landslide geological disasters within the territory of Tekes County, and then carry out the landslide susceptibility evaluation. The susceptibility of landslide disasters was evaluated by ArcGIS. The results show that the landslide disaster susceptibility level in Tekes County can be divided into four levels: high susceptibility, medium susceptibility, low susceptibility, and not susceptible, with areas of 491.3276 km2, 1181.5171 km2, 1674.7609 km2 and 5295.2976 km2 accounting for 5.68%, 13.67%, 19.38% and 61.27% of the total area of Tex County, respectively. The AUC number obtained by the success curve method (ROC) is 0.8736, reflecting the evaluation accuracy of 87.36%, indicating that the model method used in this paper is effective. The results are expected to provide practical data support for landslide disaster control in Tekes County and provide a reference for geological disaster monitoring, early warning and engineering prevention and control deployment in Yili Valley. [ABSTRACT FROM AUTHOR]

Autonomous berthing technology is a crucial engineering control problem within the ship intelligence system, encompassing a series of complex operations and technologies. Firstly, this paper analyses the research on autonomous berthing technology from a bibliometric point of view in order to obtain an overview of its past and present development and to outline the importance of this technology. Secondly, a literature review is conducted on each of the four aspects of autonomous berthing technology, namely sensing technology, berthing type, control method, and evaluation method, which can help to quickly understand the main aspects of this technology. Thirdly, the ship-assisting technologies needed to achieve autonomous berthing are discussed and analysed from six aspects: dynamic collision avoidance, path planning, path tracking, heading control, tug assistance, and shore-based systems. Finally, the challenges faced by the ship autonomous berthing technology on the way of development are summarised, and future development is projected. This paper aims to provide a more comprehensive perspective for analysing and researching ship autonomous berthing technology. [ABSTRACT FROM AUTHOR]

Load frequency control (LFC) plays a critical role in ensuring the reliable and stable operation of power plants and maintaining a quality power supply to consumers. In control engineering, an oscillatory behavior exhibited by a system in response to control actions is referred to as "Porpoising". This article focused on investigating the causes of the porpoising phenomenon in the context of LFC. This paper introduces a novel methodology for enhancing the performance of load frequency controllers in power systems by employing rat swarm optimization (RSO) for tuning and detecting the porpoising feature to ensure stability. The study focuses on a single-area thermal power generating station (TPGS) subjected to a 1% load demand change, employing MATLAB simulations for analysis. The proposed RSO-based PID controller is compared against traditional methods such as the firefly algorithm (FFA) and Ziegler-Nichols (ZN) technique. Results indicate that the RSO-based PID controller exhibits superior performance, achieving zero frequency error, reduced negative peak overshoot, and faster settling time compared to other methods. Furthermore, the paper investigates the porpoising phenomenon in PID controllers, analyzing the location of poles in the s-plane, damping ratio, and control actions. The RSO-based PID controller demonstrates enhanced stability and resistance to porpoising, making it a promising solution for power system control. Future research will focus on real-time implementation and broader applications across different control systems. [ABSTRACT FROM AUTHOR]

In this paper, we first propose a novel fully-decoupled, linear and second-order time accurate scheme to solve the phase-field model of non-Newtonian two-phase flows; the developed scheme is based on a stabilized Scalar Auxiliary Variable (SAV) approach. We strictly prove the unconditional energy stability of the scheme and conduct a numerical simulation to show the accuracy and stability of the proposed scheme. Moreover, we can observe that the parameter r in non-Newtonian fluids can affect spatial patterns during phase transitions, which directly enables us to design and perform optimal control experiments in engineering processes. [ABSTRACT FROM AUTHOR]

Software Defined Networking (SDN) is a paradigm shift in the network industry with decoupling of control and data plane. This helps network engineers to control and manage their network from centralized controller and troubleshoot the network at much better pace. With the benefits that SDN provides, it also has some vulnerable to attacks like Distributed Denial of Service (DDoS) that can disrupt the availability of the network. Most of the recent research work in DDoS detection and mitigation is done over Traditional network environments and packet limit strategy is used for mitigation by researchers with very less focus on redirection of illegitimate traffic which can result in less downtime and helpful in network analysis. The major contribution of this paper is detection and mitigation of DDoS in Software Defined Networks. We have created our own DDoS dataset with over 1.7 million entries. For Detection, we have used two different methods: (1) Snort (IDS) and (2) Machine Learning. In Machine Learning, we have used eight different Machine Learning algorithms that also include Ensemble Classifier and a Hybrid method of SVM-RF. We were able to get 99.1% accuracy which is a substantial growth as compared to recent works. For Mitigation, we have again used two methods: (1) Drop illegitimate traffic and (2) Redirect Illegitimate traffic. Results showed that hybrid SVM-RF algorithm works better as compared to individual Machine Learning algorithms and also as mitigation depends on Detection accuracy, so, the better the detection, the better the mitigation. In this paper detection and mitigation of DDoS has been performed over a SDN network. In which We have created our own dataset with over 1.7 million entries. For detection we have used two method: Snort (IDS), and Machine Learning and for Mitigation, Drop illegitimate traffic, and Redirect Illegitimate traffic. [ABSTRACT FROM AUTHOR]

The presence of decentralized production in modern electrical systems precludes traditional automatic generation control (AGC) strategies. Renewable energy production is highly dependent on the environment; for example, photovoltaic (PV) system, but its power generation is very unstable, which makes power grids containing PV systems face greater challenges. In this paper, we propose a PV-grid control method based on self-gain system compensation to reduce the negative effects of large fluctuations and uncertainties in solar energy production. First, the performance characteristics of solar modules are discussed and a multi-region model with a system with high penetration of solar systems is built. The AGC power is then displayed with different power variations according to different maximum power point tracking methods to analyze the uncertainty and variation of the PV output power and its effects on the AGC. We have also developed and implemented a compensation unit to eliminate the negative effects of PV power on AGC. Finally, the proposed method was demonstrated using the load from the example step as a disturbance variable to obtain a dynamic model of a dual-zone switched grid AGC with two PV systems based on a DMPC-distributed model. As a result of the simulation, the proposed method in this paper can guarantee the excellent properties of the dynamic behavior of the AGC system with large variations and high uncertainty of the PV system, and the validity of the proposed method have also been confirmed. [ABSTRACT FROM AUTHOR]

The mystery of soaring birds, such as albatrosses and eagles, has intrigued biologists, physicists, aeronautical/control engineers, and applied mathematicians for centuries. These fascinating avian organisms are able to fly for long durations while expending little to no energy, utilizing wind to gain lift. This flight technique/maneuver is called dynamic soaring (DS). For biologists and physicists, the DS phenomenon is nothing but a wonder of the very elegant ability of these birds to interact with nature and use its physical ether in an optimal way for better survival and energy efficiency. For the engineering community, the DS phenomenon is a source of inspiration and an unequivocal opportunity for biomimicking. Mathematical characterization of the DS phenomenon in the literature has been limited to optimal control configurations that utilized developments in numerical optimization algorithms along with control methods to identify the optimal DS trajectory taken (or to be taken) by the bird/mimicking system. Unfortunately, all of these methods are highly complex and non-real-time. Hence, the mathematical characterization of the DS problem, we believe, appears to be at odds with the phenomenon/birds-behavior. In this paper, we provide a novel twolayered mathematical approach to characterize, model, mimic, and control DS in a simple real-time implementation, which we believe more effectively decodes the biological behavior of soaring birds. First, we present a differential geometric control formulation and analysis of the DS problem, which allow us to introduce a control system that is simple yet controllable. Second, we establish a link between the DS philosophy and a class of dynamical control systems known as extremum seeking systems. This linkage provides the control input that makes DS a real-time reality. We believe our framework accurately describes the biological behavior of soaring birds and opens the door for geometric control theory and extremum seeking systems to be utilized in biological systems and natural phenomena. Simulation results are provided along with comparisons to powerful optimal control solvers, illustrating the advantages of the introduced method. [ABSTRACT FROM AUTHOR]

Human activities overwhelm our environment with CO2 and other global warming issues. The current electricity landscape necessitates a superior, continuous power supply and addressing such environmental concerns. These issues can be resolved by incorporating renewable energy sources (RESs) into the utility grid. Thus, this paper presents an optimized hybrid fuzzy logic self-tuning PID controller to control the automatic generation control (AGC) of various renewable sources. This controller regulates the frequency deviations of the power system and governs the change in the tie-line load of a multi-area hybrid energy system composed of wind, biomass, and photovoltaic energy sources. MATLAB Simulink software was applied to design and test the system. The PID controller has been tuned using four algorithms, namely, genetic algorithm (GA), pattern search (PS), simulated annealing (SA), and particle swarm optimization (PSO), and we compared the results with the proposed novel optimized PID controller (GA-fuzzy logic self-tuning technique) to validate it. The results show the superiority of the proposed hybrid GA-fuzzy logic self-tuning algorithm over the other algorithms in bringing the power system back to its regular operation. The paper also proposes an operation strategy to lower the utilization of biomass energy in the presence of other renewable energy sources. [ABSTRACT FROM AUTHOR]

This document is a review of advanced digital technologies in modeling and control of technological processes. It highlights the importance of international collaboration in scientific research and the sharing of knowledge and resources. The special issue includes studies in various fields such as mining, mineral processing, vision and image processing systems. The papers cover topics such as power quality data collection, energy generation estimation, predictive models for fault detection, virtual soft sensors, autonomous transportation control systems, machine learning for the oil and gas industry, optical inspection systems, image encryption, critical velocity prediction, computational fluid dynamics, and steel defect recognition. The contributions provide valuable insights and potential solutions for improving efficiency and sustainability in different industries. [Extracted from the article]

Copyright of FME Transactions is the property of University of Belgrade, Faculty of Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Plate heat exchangers are realized by means of a heat transfer mechanism, in which heat is naturally transferred from the hot substance to the object with a lower temperature according to the laws of thermodynamics. Two liquids of different temperatures flow on the wall, heat transfer on the wall and convection of the liquid on the wall, thus promoting heat transfer between the two liquids. Under the same flow rate and power consumption conditions, its heat transfer coefficient is three times that of shell and tube heat exchangers, which is a key and efficient new equipment for effectively using effective resources and saving and developing new energy. However the heat supply plate heat exchanger has a tiny circulation surface and is easily obstructed. Regular maintenance and cleaning, troubleshooting, and plate replacement all necessitate frequent heat exchanger disassembly and installation. The requirements and challenges in dismantling and assembling the heat exchanger are very high, and manual disassembly is wasteful, making consistent force difficult to achieve. The current methods of disassembly and assembly are inefficient and incorrect. The intelligent mechanization of disassembly and assembly equipment is realized in this paper by driving, clamping, automatic control, distance measurement, sensing, and other systems. The problems of uneven force, low efficiency, and precision in plate heat exchanger disassembly and assembly are solved. Our power disassembly equipment for high efficiency heat transfer plate heat exchangers not only enhances disassembly efficiency and precision, but it also ensures the safe operation of the plate heat exchanger heating system, and the heat transfer efficiency and heat exchange efficiency are improved. Furthermore, it has a wide range of applications in the petroleum, chemical, and other industries. [ABSTRACT FROM AUTHOR]

This document is an editorial from the journal "Unmanned Systems" that celebrates Professor Zongli Lin's 60th birthday. It provides a summary of his educational background and career achievements in the field of control theory and its applications. The editorial also mentions his contributions to research, publications, professional service, and leadership in various organizations. Additionally, the document introduces a special issue that features 18 papers from Professor Lin's former classmates, collaborators, and friends, covering topics related to autonomous systems and automation technologies. The editorial expresses gratitude to the authors and reviewers who contributed to the special issue. [Extracted from the article]

In this paper, we introduce a novel method to formally represent elements of control engineering knowledge in a suitable data structure. To this end, we first briefly review existing representation methods (RDF, OWL, Wikidata, ORKG). Based on this, we introduce our own approach: The Python-based imperative representation of knowledge (PyIRK) and its application to formulate the Ontology of Control Systems Engineering (OCSE). One of its main features is the possibility to represent the actual content of definitions and theorems as nodes and edges of a knowledge graph, which is demonstrated by selected theorems from Lyapunov's theory. While the approach is still experimental, the current result already allows the application of methods of automated quality assurance and a SPARQL-based semantic search mechanism. The feature set of the framework is demonstrated by various examples. The paper concludes with a discussion of the limitations and directions for further development. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to solve the challenging problem of automatic carrier landing with the presence of environmental disturbances. Therefore, a global fast terminal sliding mode control (GFTSMC) method is proposed for automatic carrier landing system (ACLS) to achieve safe carrier landing control. Design/methodology/approach: First, the framework of ACLS is established, which includes flight glide path model, guidance model, approach power compensation system and flight controller model. Subsequently, the carrier deck motion model and carrier air-wake model are presented to simulate the environmental disturbances. Then, the detailed design steps of GFTSMC are provided. The stability analysis of the controller is proved by Lyapunov theorems and LaSalle's invariance principle. Furthermore, the arrival time analysis is carried out, which proves the controller has fixed time convergence ability. Findings: The numerical simulations are conducted. The simulation results reveal that the proposed method can guarantee a finite convergence time and safe carrier landing under various conditions. And the superiority of the proposed method is further demonstrated by comparative simulations and Monte Carlo tests. Originality/value: The GFTSMC method proposed in this paper can achieve precise and safe carrier landing with environmental disturbances, which has important referential significance to the improvement of ACLS controller designs. [ABSTRACT FROM AUTHOR]

This paper considers the comparative analysis of the physical and mathematical methods of the optimal operation of aviation objects' automatic control systems, which is an urgent scientific and technical task. The paper also represents the corresponding stability criteria, depending on the operating conditions, and the influence of external factors that allows ensuring the automatic control systems' reliable operation not only in normal circumstances, but also in the presence of probable disagreements. The scientific novelty of the research lies in developing new stability criteria by means of synthesising the influence of different factors and also the ability to ensure optimal functioning of the automatic control systems. Corresponding mathematical and computer models have been developed. The use of these models allows to determine the optimal stability criteria for control systems with parameters of a different physical nature and ensure reliable operation of electromechanical systems for general and special applications based on the current analysis. The research results are illustrated by corresponding mathematical models, engineering calculations and modelling of the optimal control limits depending on the influence of various factors. The simulation results coincide with the theoretical calculations, which indicates the consistency between the microprocessor-based and software of automatic control systems. [ABSTRACT FROM AUTHOR]

This paper deals with a simulation assessment of the impact of the introduction of ETCS L2 and L3 on the capacity of railway lines. The simulation itself is carried out in the OpenTrack simulation software, especially in the tool headway calculator. For this research, an infrastructure model has been built, which represents a typical layout of lines and stations in the Czech Republic. By successive modifications of the model, the significance of the impact of the implementation of ATP systems on the capacity of lines can be simulated. In the paper, the influence of the implementation of different application levels of ATP systems on the capacity of railway lines is evaluated. [ABSTRACT FROM AUTHOR]

Efficient control of tunnel boring machine (TBM) tunneling along the designed tunnel axis in an unknown variable geological environment is a difficult and significant task. At present, the TBM attitude during tunneling is mostly manually controlled based on the deviation between the tunneling axis and the designed tunnel axis and their experiences. The tunneling axis from manual control is often the snakelike motion around the designed tunnel axis, even exceeding the deviation limit, for which this paper analyzed three reasons, the unknown geological environment, the hysteresis of TBM position response, and the unsolved overall optimization of tunneling axis. For these reasons, this paper proposed a real-time optimal control framework of TBM attitude based on reinforcement learning, which contains the geological information predictive model, TBM attitude and position (TBMAP) predictive model, and optimal attitude control policy (OACP). This framework can predict the current geological information in real-time and provide the corresponding real-time optimal attitude control that simultaneously considers the hysteresis of TBM position response and the overall optimization of the tunneling axis. This attitude control framework can be directly deployed to TBM without increasing costs and excessive modifications to the equipment. To verify the effectiveness of this attitude control framework, the Xinjiang Yiner Water Supply Phase II Project, using the TBM method, was adopted as a case study. The results revealed that the accuracy of geological environment recognition reached 94%, and OACP can significantly reduce the accumulated deviation of the tunneling axis from the designed tunnel axis by over 80% compared with the manual control and easily provide real-time decision support for attitude control in actual engineering. [ABSTRACT FROM AUTHOR]

As it is known, primary oil processing facilities are designed for the production of light-colored oil products and various oil fractions that meet modern requirements with their quality. The main equipment of these technological facilities are rectification columns operating under atmospheric and vacuum conditions, which perform the decomposition of crude oil and fuel oil into various product fractions. Generally, the quality indicators of product fractions produced in these columns are provided by stabilizing the temperature modes at certain points (plates) of these or rectification columns by regulating the irrigation consumption supplied to those plates. It should be noted that currently, in accordance with the requirements of the time, the design and development of new control systems that can adequately respond to more dynamic and variable external and internal stimulating influences for complex oil refining technological facilities is one of the main problems facing the automatic control theory. The paper is dedicated to the issue of developing a new automated control system that can provide the required accuracy in terms of quality management. As an adaptive control system, the purpose of such systems is to compensate in time for disturbance influences in the technological devices and to ensure the production of oil products with stable quality indicators, regardless of these influences. The purpose of study is to develop a control system that can meet the given requirements and ensure the produced products with relatively stable quality indicators, regardless of the controlled and uncontrolled exciting effects affecting the oil refinery technological complex. The principle of operation of this system is based not only on the compensation of disturbance influences but also on the calculation of new operation strategies depending on these effects. The development of new automated control system that is able to provide the necessary accuracy of quality control in the lack of information is relevant. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to test the performance of the designed automatic control system based on the Linear Quadratic Regulator (LQR) and Linear Quadratic Gaussian (LQG) algorithms during landing of the helicopter on the ship deck. This paper is a further development of the series based on Topczewski et al. (2020). Design/methodology/approach: The system consists of two automatic control algorithms based on LQR and the LQG. It is integrated with the ship motion prediction system based on autoregressive algorithm with parameters calculated using Burg's method. It is assumed that the source of necessary navigation data is integrated Inertial Navigation System with Global Positioning System. Landing of the helicopter on the ship deck is performed in automatic way, based on the preselected procedure. Performance of the control system is analyzed when all necessary navigation data is available for the system and in case when one of the parameters is unavailable during performing the procedure. Findings: In this paper, description of the designed control system developed for performing the approach and landing of the helicopter using selected procedure is presented. Helicopter dynamic model is validated using the manufacturer data and by test pilots, overview is presented. Necessary information about ship motion model is also included. Tests showing mission performance while using LQR and LQG algorithms applied to the control system are presented and analyzed, taking into account both situations when full navigation data is available/unavailable for the control system. Practical implications: Results of the system performance analyses can be used for selection of the proper control methodology for prospective helicopters autopilots. Furthermore, the system can be used to analyze the mission safety when information about one of the navigation parameters is identified by the navigation system as unavailable or incorrect and therefore unavailable during landing on the ship deck. Originality/value: In this paper, control system dedicated for the automatic landing of the helicopter on the ship deck, based on two different control algorithms is presented. Influence of lack of information about one of the navigation parameters on the mission performance is analyzed. [ABSTRACT FROM AUTHOR]

There are two technical challenges in predicting slope deformation. The first one is the random displacement, which could not be decomposed and predicted by numerically resolving the observed accumulated displacement and time series of a landslide. The second one is the dynamic evolution of a landslide, which could not be feasibly simulated simply by traditional prediction models. In this paper, a dynamic model of displacement prediction is introduced for composite landslides based on a combination of empirical mode decomposition with soft screening stop criteria (SSSC-EMD) and deep bidirectional long short-term memory (DBi-LSTM) neural network. In the proposed model, the time series analysis and SSSC-EMD are used to decompose the observed accumulated displacements of a slope into three components, viz. trend displacement, periodic displacement, and random displacement. Then, by analyzing the evolution pattern of a landslide and its key factors triggering landslides, appropriate influencing factors are selected for each displacement component, and DBi-LSTM neural network to carry out multi-data-driven dynamic prediction for each displacement component. An accumulated displacement prediction has been obtained by a summation of each component. For accuracy verification and engineering practicability of the model, field observations from two known landslides in China, the Xintan landslide and the Bazimen landslide were collected for comparison and evaluation. The case study verified that the model proposed in this paper can better characterize the "stepwise" deformation characteristics of a slope. As compared with long short-term memory (LSTM) neural network, support vector machine (SVM), and autoregressive integrated moving average (ARIMA) model, DBi-LSTM neural network has higher accuracy in predicting the periodic displacement of slope deformation, with the mean absolute percentage error reduced by 3.063%, 14.913%, and 13.960% respectively, and the root mean square error reduced by 1.951 mm, 8.954 mm and 7.790 mm respectively. Conclusively, this model not only has high prediction accuracy but also is more stable, which can provide new insight for practical landslide prevention and control engineering. [ABSTRACT FROM AUTHOR]

Presents the recipients of the IEEE Transactions on Control Systems Technology Outstanding Paper Award. [ABSTRACT FROM AUTHOR]

In this paper, an optimized Genetic Algorithm (GA) based internal model controller-proportional integral derivative (IMC-PID) controller has been designed for the control variable to output variable transfer function of dc-dc boost converter to mitigate the effect of non-minimum phase (NMP) behavior due to the presence of a right-half plane zero (RHPZ). This RHPZ limits the dynamic performance of the converter and leads to internal instability. The IMC PID is a streamlined counterpart of the standard feedback controller and easily achieves optimal set point and load change performance with a single filter tuning parameter ?. Also, this paper addresses the influences of the model-based controller with model plant mismatch on the closed-loop control. The conventional IMC PID design is realized as an optimization problem with a resilient controller being determined through a genetic algorithm. Computed results suggested that GA-IMC PID coheres to the optimum designs with a fast convergence rate and outperforms conventional IMC PID controllers. [ABSTRACT FROM AUTHOR]

The Inverted Pendulum On a Cart (IPOC) system poses a challenge in control engineering due to its inherent instability, nonlinearity, and underactuation. This addresses the fundamental issues arising from its underactuated nature and introduces an approach that combines Takagi-Sugeno (T-S) fuzzy control with an awareness of real-world constraints to create a control system ensuring both stability and practicality. By aligning theoretical insights with extended considerations, the Linear Matrix Inequality (LMI)-based control design is demonstrated in a comprehensive framework. Theorems are introduced and validated, leading to the derivation of LMI conditions. The simulation results are assessed with accompanying comments to demonstrate the effectiveness of the theorems. Through this integration of T-S fuzzy control with additional considerations, the paper aims to bridge the gap between theory and practical applications, advancing the field of control engineering. [ABSTRACT FROM AUTHOR]

The PID controller's well-established advantages have led to its widespread application in practically all industrial operations. The effectiveness of the controller has a major impact on the overall performance of the system, making tuning an essential part of the system's operation, which made this a hot topic for academics to dig into for some time. This paper provides an overview of both contemporary and old PID tuning methods. Techniques can be categorized into two major groups, namely: Traditional and optimization tuning methods are two examples of tuning techniques. A comparison between some of the different methods has as well as being made available. The primary objective of this paper is to give thorough for PID professional controllers. [ABSTRACT FROM AUTHOR]

Recovering the global accurate complex physics field from limited sensors is critical to the measurement and control of the engineering system. General reconstruction methods for recovering the field, especially the deep learning with more parameters and better representational ability, usually require large amounts of labeled data which is unaffordable in practice. To solve the problem, this paper proposes uncertainty guided ensemble self-training (UGE-ST), using plentiful unlabeled data to improve reconstruction performance and reduce the required labeled data. A novel self-training framework with the ensemble teacher and pre-training student designed to improve the accuracy of the pseudo-label and remedy the impact of noise is first proposed. On the other hand, uncertainty guided learning is proposed to encourage the model to focus on the highly confident regions of pseudo-labels and mitigate the effects of wrong pseudo-labeling in self-training, improving the performance of the reconstruction model. Experiments including the airfoil velocity and pressure field reconstruction and the electronic components' temperature field reconstruction indicate that our UGE-ST can save up to 90% of the data with the same accuracy as supervised learning. [ABSTRACT FROM AUTHOR]

Load forecasting is a research hotspot in academia; in the context of new power systems, the prediction and determination of load reserve capacity is also important. In order to adapt to new forms of power systems, a day-ahead automatic generation control (AGC) reserve capacity demand prediction method based on the Fourier transform and the attention mechanism combined with a bidirectional long and short-term memory neural network model (Attention-BiLSTM) optimized by an improved whale optimization algorithm (IWOA) is proposed. Firstly, based on the response time, Fourier transform is used to refine the distinction between various types of load reserve demand, and the power of the AGC reserve band is calculated using Parseval's theorem to obtain the reserve capacity demand sequence. The maximum mutual information coefficient method is used to explore the relevant influencing factors of the AGC reserve sequence concerning the data characteristics of the AGC reserve sequence. Then, the historical daily AGC reserve demand sequences with relevant features are input into the Attention-BiLSTM prediction model, and the improved whale algorithm is used to automatically find the optimal hyperparameters to obtain better prediction results. Finally, the arithmetic simulation results show that the model proposed in this paper has the best prediction performance with the upper (0.8810) and lower (0.6651) bounds of the coefficient of determination ( R 2 ) higher than the other models, and it has the smallest mean absolute percentage error ( M A P E ) and root mean square error ( R M S E ). [ABSTRACT FROM AUTHOR]

Electrode roll forming involves rolling a battery electrode into a preset thickness using a hydraulic roll gap thickness automatic control system (hydraulic AGC for short). The pump-controlled AGC is a highly nonlinear servo system, which is a combination of mechanical, hydraulic and electronic control disciplines; thus, as a new technology, it still faces many challenges in the field of pole plate rolling. In this paper, electro-hydraulic servo pump-controlled AGC technology is replaced by electro-hydraulic servo valve-controlled AGC technology. With pump-controlled AGC high-precision thickness control as the research objective, the fuzzy control method is selected to deal with complex nonlinear systems based on pump-controlled AGC nonlinear stiffness characteristics and nonlinear transmission characteristics. A characteristic compensation control strategy is proposed. At the same time, considering the load fluctuation caused by the uneven thickness of the electrode plate under the intermittent coating rolling condition of a lithium battery, the fuzzy internal model (IMC) compensation control strategy was proposed to compensate the structural characteristics of the electrode plate rolling. Comparative experiments show that the position control accuracy of the pump-controlled AGC system can be improved significantly by using a fuzzy IMC compensation control strategy. The steady-state accuracy of the slope signal can reach ±0.7 μm, and the position-following accuracy of the sinusoidal signal can reach ±1.8 μm. In addition, this study will assist technological upgrades to lithium battery electrode roll forming and fixed-roll-gap rolling, laying a theoretical foundation for the promotion of pump control technology in the field of electrode rolling. [ABSTRACT FROM AUTHOR]

A damaged stator vane can disrupt the circumferential symmetry of the design flow for turbine assemblies, which can lead to a low-engine-order (LEO) forced response of rotor blades. To help engineers be able to better address sudden vane damage failures, this paper conducts a mechanism analysis of the LEO forced response of rotor blades induced by a single damaged vane using an in-house computational fluid dynamic code (Hybrid Grid Aeroelasticity Environment). Firstly, it is found that the damaged vane introduces a family of LEO aerodynamic excitations with high amplitudes by full-annulus unsteady aeroelastic simulations of a single-stage turbine. In particular, the LEO forced response of the rotor blades excited by 3EO is 2.01 times higher than the resonance response excited by vane passing frequency, and the LEO resonance risk of the rotor blades is greatly increased. Then, by analyzing the flow characteristics of the wake and potential field of the stator row with a damaged vane, the localized high transient pressure in the notch cavity and the radial redistribution of the secondary vortex at the stator exit are the main sources of the low-order harmonic components in the flow field. Importantly, the interaction mechanisms in two regions with high LEO excitation amplitude on the rotor blade surface are revealed separately. Finally, an evaluation and comparison of a single damaged vane in terms of aerodynamic performance and LEO forced response was carried out. The results of this paper provide a good theoretical basis for engineers to effectively control the resonance response of rotor blades caused by a damaged stator vane in turbine design. [ABSTRACT FROM AUTHOR]

High Penetration of renewable energy with conventional power system increases the system instability problem during recent year. Load frequency control (LFC) is a valuable part in controlling the smart power system. In this paper create a model for automatic generation control with wind power system using fuzzy logic controller (FLC) in single area power system. It is very important to control the integral load frequency while generating thermal and wind. In this paper integral controller is used to obtain the value of FLC. All FLC controllers are to be analyzed and ultimately compared with existing controllers. And finally, it is proved that the calculation done on the basis of FLC is much better than the existing control schemes. [ABSTRACT FROM AUTHOR]

Amidst the aftermath of the COVID-19 pandemic, the landscape of education in India has undergone a profound transformation. Traditional methods of teaching and learning, once the bedrock of our educational system, now find themselves met with skepticism from both students and educational institutions. In response to this paradigm shift, this paper delves into the pressing need for a comprehensive overhaul of pedagogical methodologies within the context of engineering education in India. It emphasizes the pivotal role of education and pedagogy, advocating for a departure from conventional models towards a more learner-centric and adaptable framework, tailored to the nuances of the Indian education landscape. In this context, the paper sheds light on the intrinsic value of understanding diverse learning styles, particularly within the backdrop of a digitally driven era. The profound impact of digital platforms on modern education in India is explored, underscoring their potential to revolutionize and enhance the learning experience for Indian students. The paper further examines unconventional techniques that challenge the norms of education as conventionally understood, recognizing their potential to bridge the gap between students' expectations and institutional methodologies. Within the discourse of this paper, special attention is accorded to three distinctive non-conventional pedagogical approaches: 'Cyberhunt', 'Keyword Concept' and 'Role Switch Teaching'. These methodologies are scrutinized as quintessential examples of innovative strategies that can foster an intuitive and engaging learning environment within Indian classrooms. 'Cyberhunt' immerses students in experiential learning by assigning them virtual quests that mirror real-world challenges, thereby nurturing critical thinking and problem-solving skills tailored to the Indian context. Similarly, the 'Keyword Concept' approach capitalizes on cognitive associations to enhance memory retention and conceptual understanding, catering to the unique learning styles often found in India. Further, the innovative 'Role Switch' teaching methodology, which involves assigning students diverse roles in course delivery, was successfully implemented to further enrich the learning experience in the context of control engineering course. [ABSTRACT FROM AUTHOR]

Similar to many complex systems, the operation process of electric balanced forklifts has characteristics such as time-varying model parameters and nonlinearity. Establishing an accurate mathematical model becomes challenging, making it difficult to apply model-based control methods in engineering practice. Aiming at the longitudinal control system of electric forklifts containing external disturbances, this paper proposes an improved full-format dynamic linearization model-free adaptive PID control (iFFDL-MFA-PID) method. Firstly, the full-format dynamic linearization (FFDL) method is employed to transform the operating system of the electric balanced forklift into a virtual equivalent linear data model. Secondly, the nonlinear residual term and pseudo-gradient (PG) of the data model are estimated using the difference estimation algorithm and the optimal criterion function, respectively. Furthermore, in order to enhance the robustness of the system, the idea of intelligent PID (iPID) is introduced and the principle of equivalent feedback is utilized to derive the iFFDL-MFA-PID control scheme. The design process of this scheme only requires the use of the input and output data of the system, without relying on the mathematical model of the system. Finally, the iFFDL-MFA-PID method proposed in this paper is simulated and tested with the EFG-BC/320 counterbalanced forklift equipped in the Special Equipment Testing Center and compared with the model-free adaptive control method (FFDL-MFAC) and the PID control method. Simulation results show that the speed-tracking error of the electric forklift truck under the action of the iFFDL-MFA-PID algorithm is maintained within ±0.132 m/s throughout the process, achieving higher tracking accuracy and better robustness compared to the MFAC and PID methods. [ABSTRACT FROM AUTHOR]

The paper examines the specificity of the modern intelligent control systems. The Big Data technology and Data Science algorithms open up great potential in train traffic management based on hazard prevention. An example is given of high reliability and acceptable accuracy of hazardous railway infrastructure failure prediction using methods based on artificial intelligence. A great deal of attention is given to economical methods of ensuring the required levels of functional safety of train control systems. For that purpose, the efficiency of the digital twin-based method was evaluated. It is shown that, under certain conditions, this method allows significantly reducing the cost of a control system while achieving an acceptably high level of functional safety. The method of virtual second channels is based on the same principle of using information redundancy rather than hardware redundancy. The paper presents and analyses the method of virtual second channels in respect to an axle counter-based train control system. It is established that it is possible to ensure a safety integrity level of an entire control system with a virtual second channel at least as high as SIL3. The above methods ensure, on the one hand, a reduction of the amount of equipment and significantly lower cost of the systems and, on the other hand, requires the creation of additional software and substantiation of the acceptability of the achieved level of functional safety. This matter is within the competence of the developer of the control system. [ABSTRACT FROM AUTHOR]

Rotary inverted pendulum (RIP) is a classical model of control engineering. Paper deals with a PID-adaptive structure which is based on structure of neuron to train Kp, Ki, Kd through operation. In simulation, our adaptive controller is proven to work in larger range than classical PID controller. Through experimental model using STM32F4, we prove vibration of system under adaptive-PID is smaller than under classical PID structure. Then, combination of neuron network (NN) and PID control can be used as simple structure for single-input multi output (SIMO) systems which are similar to RIP. [ABSTRACT FROM AUTHOR]

Supervisory control and data acquisition (SCADA) systems are computer systems that gather and analyze real-time data, distributed control systems are specially designed automated control system that consists of geographically distributed control elements, and other smaller control systems such as programmable logic controllers are industrial solid-state computers that monitor inputs and outputs and make logic-based decisions. In recent years, there has been a lot of focus on the security of industrial control systems. Due to the advancement in information technologies, the risk of cyberattacks on industrial control system has been drastically increased. Because they are so inextricably tied to human life, any damage to them might have devastating consequences. To provide an efficient solution to such problems, this paper proposes a new approach to intrusion detection. First, the important features in the dataset are determined by the difference between the distribution of unlabeled and positive data which is deployed for the learning process. Then, a prior estimation of the class is proposed based on a support vector machine. Simulation results show that the proposed approach has better anomaly detection performance than existing algorithms. [ABSTRACT FROM AUTHOR]

The occurrence of late spring frosts due to climate change causes great damage to plantation production worldwide. The main objective of the paper is to provide a comprehensive overview of the problem and to outline effective protective measures against late spring frosts. The nature of frost depends on regional, altitudinal, and geographic differences, but they all share a common problem: they remove heat, resulting in the freezing of new plant growth and flowers. Tissue freezing is affected by critical temperatures and the frost type, intensity, and duration. Protection against late spring frosts can be broadly divided into three categories: active, passive, and chemical measures. In the field of agricultural engineering, various techniques have been thoroughly researched, and their effectiveness has been confirmed by research. These include various sprinkler systems, different heating devices, and large-diameter fans. Conclusive findings are being made on the performance of these systems in sub-zero temperatures and their cost-effectiveness. Climate change increases the importance of protecting permanent crops from late spring frosts and requires advances in agricultural technology to meet changing production demands and challenges. [ABSTRACT FROM AUTHOR]

In order to improve the autonomy and intelligence of group animation behavior, this paper proposes a particle swarm optimization algorithm for automatic control of group animation behavior. After the user inputs the group animation behavior construction target, the individual path is planned according to the fixed behavior rules, and the particle swarm optimization algorithm and the improved artificial fish swarm optimization algorithm are used to select the optimal action for the individual in the group animation according to the top-down control process. The experimental results show that after the PSO converges to the global optimal solution, the evolution times of the PSO converge to the global optimal solution again when the surrounding environment changes 10 times. The population diversity of particle swarm optimization algorithm is obviously better than those of the other two algorithms in the process of automatically controlling the movement of individuals to the circle and to the stand in the crowd animation. Compared with the original algorithm AFSA, the improved artificial fish swarm algorithm proposed in this paper is easier to obtain the global optimal value, and the optimization speed is also improved. In conclusion, the example analysis verifies that the algorithm can effectively realize the automatic control of group animation behavior and has high convergence speed and high autonomy. [ABSTRACT FROM AUTHOR]

The development of digital techniques in control engineering leads to the creation of innovative algorithms for measuring specific parameters. In the field of electric power engineering these parameters may be amplitude, phase and frequency of voltage or current occurring in the analyzed electric grid. Thus, the algorithms mentioned, applied in relation to the quoted parameters, may provide precise and reliable measurement results in the electric grid as well as ensure better grid monitoring and security. Signal analysis regarding its identification due to the type of interference is very difficult because the multitude of information obtained is very large. In order to indicate the best method for determining errors in measuring synchronous parameters of the measured current or voltage waveforms, the authors propose in this paper a new form of one error for all testing functions, which is called an equivalent error. This error is determined for each error's value defined in the applicable standards for each of selected 15 methods. The use of the equivalent error algorithm is very helpful in identifying a group of methods whose operation is satisfactory in terms of measurement accuracy for various types of disturbances (both in the steady state and in the dynamic state) that may occur in the power grid. The results are analyzed for phasor measurement unit (PMU) devices of class P (protection) and M (measurement). [ABSTRACT FROM AUTHOR]

Differing from the response control in civil and engineering applications, the excitation in a closed-loop sensing system is unknown and the control is designed to balance and measure the unknown excitation in real time. In this paper, a novel optimal force-balance control for a closed-loop sensing system with time delay is proposed. By introducing the errors between the control force and the unknown input as new variables, the force-balance control problem is transformed into a response minimization one with a time delay. Then, the discretization method is applied, and the continuous time system with time delay is converted into an extended discrete system without time delay, from which the analytical expression of the optimal force-balance control force is obtained. To illustrate the effectiveness of the proposed control, the optimal control of a practical quartz flexible force-balance accelerometer (FBA) is solved as an application. Numerical results show that the proposed control can realize the accurate measurement of wide-band random acceleration signals with a time delay of up to 10 times of control period. Besides, the proposed control can also significantly inhibit the vibration response of the FBA while guaranteeing measurement accuracy, which will benefit the obtaining of a large dynamic measurement range as a sensing application. [ABSTRACT FROM AUTHOR]

In this paper, we present a re-established functional fractal circuit model of arterial blood flow that incorporates the shunt effect of the branch vessels. Under the background of hemodynamics, we abstracted a family of fractal operators and investigate the kernel function and properties thereof. Based on fractal operators, the intrinsic relation between Bessel function and Struve function was revealed, and some new special functions were found. The results provide mathematical tools for biomechanics and automatic control. [ABSTRACT FROM AUTHOR]