Control Strategy Research Articles

Fault-tolerant control for Markov jump nonlinear systems based on an observer and finite-time fast integral terminal sliding mode control

This paper develops a novel fault-tolerant control based on an observer for underactuated robot manipulators with unknown external disturbances, uncertainties, and actuator faults. First, unlike the traditional approach of treating the robot system as a parameter time-varying system, the underactuated robot manipulator with different drive modes can be considered as one of the classical Markov jump nonlinear systems (MJNSs). Second, an adaptive disturbance observer is designed to estimate the state and disturbances of the system. Finally, based on the observation results, a nonsingular fast integral terminal sliding mode controller (NFITSMC) is utilized to implement fault-tolerant control of the system. Compared with traditional observer and terminal sliding mode controller, the adoption of the novel controller and observer can improve response time and reduce chattering. Especially, in order to eliminate chattering, the integral term is introduced into the nonsingular fast terminal sliding mode controller. Structuring Lyapunov–Krasovskii functional (LKF) and based on linear matrix inequalities (LMIs) techniques, the convergence of control strategy and tracking errors is proved. The simulation results show that the actuator faults can be observed successfully and the error system is finite-time stable.

Transcriptome Analysis Reveals Candidate Genes for Light Regulation of Elsinochrome Biosynthesis in Elsinoë arachidis

Light regulation is critical in fungal growth, development, morphogenesis, secondary metabolism, and the biological clock. The fungus Elsinoë arachidis is known to produce the mycotoxin Elsinochrome (ESC), a key factor contributing to its pathogenicity, under light conditions. Although previous studies have predominantly focused on the light-induced production of ESC and its biosynthetic pathways, the detailed mechanisms underlying this process remain largely unexplored. This study explores the influence of light on ESC production and gene expression in E. arachidis. Under white light exposure for 28 days, the ESC yield was observed to reach 33.22 nmol/plug. Through transcriptome analysis, 5925 genes were identified as differentially expressed between dark and white light conditions, highlighting the significant impact of light on gene expression. Bioinformatics identified specific light-regulated genes, including eight photoreceptor genes, five global regulatory factors, and a cluster of 12 genes directly involved in the ESC biosynthesis, with expression trends confirmed by RT-qPCR. In conclusion, the study reveals the substantial alteration in gene expression associated with ESC biosynthesis under white light and identifies potential candidates for in-depth functional analysis. These findings advance understanding of ESC biosynthesis regulation and suggest new strategies for fungal pathogenicity control.

How does local debt expansion affect earnings management? Evidence from China

ABSTRACT This paper explores the impacts of local government debts (LGD) on earnings management strategy and moderation effects of internal control. Using a purpose-build database incorporating Chinese LGD and listed firms, we find that firms engage in income-increasing earnings management in response to local debt expansion. The results remain stable to endogeneity tests and various robustness checks. Furthermore, the impacts of LGD are markedly pronounced in non-SOEs, firms audited by Big 4 auditors, young and declining firms, and firms located in less-marketized regions. Moreover, this study reveals that internal control exerts moderation effects on the relation between LGD and earnings management.

AUTOMATIC OPERATION OF ON-GRID SOLAR SYSTEM WITH PROTECTION SCHEME

The on-grid solar energy system is becoming a pivotal component of modern electrical grids due to its potential for sustainable energy production and reduced environmental impact. The integration of automatic operation and protection schemes is essential to ensure optimal performance, reliability, and safety of these systems. This research paper delves into the development and implementation of an automatic operation strategy for an on-grid solar system, incorporating a robust protection scheme to mitigate common operational risks. We explore various components and design considerations, detailing the control strategies employed to achieve seamless system function and high performance. A thorough analysis of the data collected from the implemented system highlights the efficacy and reliability of the proposed method. Additionally, the research compares the findings with existing studies to position the results within the broader academic dialogue. Our findings show significant improvements in both the operational efficiency and protection capabilities of the solar system, thus providing valuable insights for future advancements in renewable energy integration. The developed methodologies could lead to enhanced system durability and energy yield, thereby promoting the broader adoption of renewable energy solutions. As we mentioned in Title, we are going to make the Operation of Solar System Automatic with Protection Scheme. This is very beneficial as point of view to on-Grid Solar Systems which is a Need of Today’s World.

Utilisation of Machine Learning in Control Systems Based on the Preference of Office Users

Reducing energy consumption is vital to save natural resources and contribute to the sustainable development in any sector of society. In the building sector, there are many well-known energy efficiency strategies currently being applied. However, considering the advances in technology and in comfort studies, it is possible to see that the current building sector scenario demands new energy efficiency strategies. Such strategies need to be capable of identifying and assuring comfortable environments according to users’ perceptions. Machine learning techniques can be a useful alternative to identify users’ preferences and control lighting and heating, ventilation and air-conditioning systems in buildings. This paper shows a systematic literature review on the use of machine learning algorithms on preference identification and environmental adequacy according to users’ demands. Its contribution is to explore beyond the performance and configurations of the algorithms, addressing users’ preference aspects as well. The strategies found in the literature provided promising results. The most used approach was supervised learning because data can be treated as categories. In general, the control systems have shown good performance, and so have the algorithms. Users were mostly satisfied with environmental conditions. Situations of dissatisfaction were associated with the occupant’s willingness to use the system more than with the control system’s performance. Furthermore, it is also possible to ally user-centred control and energy savings but this relies on occupants’ characteristics and the control strategies used. We underline the importance of identifying whether the users are willing to deal with an automatic control system before making any decision, even if the operation of the system is based on their preferred environmental conditions.

An enhanced control strategy for photovoltaic system control based on sliding mode-PI regulator

An enhanced control strategy for photovoltaic system control based on sliding mode-PI regulator

Spatial Interaction Analysis of Infectious Disease Import and Export between Regions

Human travel plays a crucial role in the spread of infectious disease between regions. Travel of infected individuals from one region to another can transport a virus to places that were previously unaffected or may accelerate the spread of disease in places where the disease is not yet well established. We develop and apply models and metrics to analyze the role of inter-regional travel relative to the spread of disease, drawing from data on COVID-19 in the United States. To better understand how transportation affects disease transmission, we established a multi-regional time-varying compartmental disease model with spatial interaction. The compartmental model was integrated with statistical estimates of travel between regions. From the integrated model, we derived a transmission import index to assess the risk of COVID-19 transmission between states. Based on the index, we determined states with high risk for disease spreading to other states at the scale of months, and we analyzed how the index changed over time during 2020. Our model provides a tool for policymakers to evaluate the influence of travel between regions on disease transmission in support of strategies for epidemic control.

Synchronization of complex networks with PID pinning controller

Recently, synchronization of complex networks has been studied deeply; however, most existing works take the standard proportional control strategy approach to pinning controllers, which produces a steady-state error and has a significant time of fluctuation. In order to solve this problem, we propose a new method using the Proportional-Integral-Differential (PID) control to design a pinning control method for synchronization of complex networks. Based on Lyapunov theory, the stability of the network is proved, and sufficient synchronization conditions are obtained. Finally, examples are given to evaluate the effectiveness of the proposed PID pinning method and compare it to the traditional proportional pinning controller and adaptive pinning controller.

Transmission risks of Omicron BA.5 following inactivated COVID-19 vaccines among children and adolescents in China.

As SARS-CoV-2 Omicron variants circulating globally since 2022, assessing the transmission characteristics, and the protection of vaccines against emerging Omicron variants among children and adolescents are needed for guiding the control and vaccination policies. Weconducted a retrospective cohort study for SARS-CoV-2 infections and close contacts aged <18 years from an outbreak seeded by Omicron BA.5 variants. The secondary attack rate (SAR) was calculated and the protective effectsof two doses of inactivated vaccine (mainly Sinopharm /BBIBP-CorV) within a year versus one dose or two doses above a year after vaccination against the transmission and infection of Omicron BA.5 were estimated. A total of 3442 all-age close contacts of 122 confirmedSARS-CoV-2 infections aged 0-17 years were included. The SAR was higher in the household setting and for individuals who received a one-dose inactivated vaccine or those who received a two-dose for more than one year, with estimates of 28.5% (95% credible interval [CrI]: 21.1, 37.7) and 55.3% (95% CrI: 24.4, 84.8), respectively. The second dose of inactivated vaccine conferred substantial protection against all infection and transmission of Omicron BA.5 variants within a year. Our findings support the rollout of the second dose of inactivated vaccine for children and adolescents during the Omciron BA.5 predominant epidemic phase. Given the continuousemergence of SARS-CoV-2 variants, monitoring the transmission risk and corresponding vaccine effectiveness againstSARS-CoV-2 variants among children and adolescents is important to inform control strategy.

Retrofitting of a solar cooling and heating plant by employing PCM storage and adjusting control strategy

A solar cooling and heating plant with a 1000 l hot water storage tank has been operated since 2018. Two main problems were observed: (1) a fluctuating operation of the absorption chiller due to a high number of stat-up and shut-down procedures; (2) high electricity consumption caused by a low solar fraction SFn of 50.9% during the cooling period. In 2021, the solar plant was retrofitted by employing a shell and tube heat exchanger based on a PCM melting at 64 °C and by adjusting the control strategy. The system performance was evaluated and compared before and after retrofitting based on the operational data in 2021 and 2022. The total number of switch-on times of the chiller shrank by 33.5% and the operation time was increased by 65% in 2022 due to the stable hot water inlet temperature. Contributing to the high heat storage capacity of the PCM storage, more solar thermal energy was utilized and the average SFn reached 75.6% during the cooling period in 2022, leading to a 24.4% reduction in the total electricity consumption compared to 2021.

Waveform Optimization Control of an Active Neutral Point Clamped Three-Level Power Converter System

Currently, the escalating integration of renewable energy sources is causing a steady weakening of grid strength. When grid strength is weak, interactions between inverters or those between inverters and grid line impedance can provoke widespread oscillations in the power system. Additionally, the diverse DC voltage application characteristics of power converter systems (PCS) may lead to over-modulation, generating narrow pulse issues that further impact control of the midpoint potential balance. Existing dead-time elimination methods are highly susceptible to current polarity judgments, rendering them ineffective in practical use. PCS, due to inherent dead-time effects, midpoint potential imbalances in three-level topologies, and narrow pulses, can elevate low-order harmonic content in the output voltage, ultimately distorting grid-connected currents. This is particularly susceptible to causing resonance in weak grids. To enhance the output voltage waveform of PCS, this article introduces a comprehensive compensation control strategy that combines dead-time elimination, midpoint potential balance, and narrow pulse suppression, all based on an active neutral point clamped (ANPC) three-level topology. This strategy gives precedence to dead-time elimination and calculates the upper and lower limits of the zero-sequence available for midpoint potential balance while fully compensating for narrow pulses. By prioritizing dead-time elimination, followed by narrow pulse suppression and finally midpoint potential balance, this method decouples the coupling between these three factors. The effectiveness of the proposed method is validated through semi-physical simulations.

Control strategies and performance analysis of doubly approximation and large-scale system control using hybrid model reduction approach

Control strategies and performance analysis of doubly approximation and large-scale system control using hybrid model reduction approach

Passivity-Based Control with Disturbance Observer of Electromagnetic Formation Flight Spacecraft in the Port-Hamiltonian Framework

Satellite formation flying technology currently represents a focal point in space mission research. Traditional spacecraft payload performance and lifespan are often constrained by propellant limitations. Electromagnetic Formation Flying (EMFF), a propellant-free formation flying technique, has garnered widespread attention. Its inherent strong nonlinearity and coupling present challenges for high-precision control within EMFF. This paper presents the relative motion dynamics of a two-satellite EMFF in the port-Hamiltonian framework and constructs an accurate nonlinear model of the dynamics. Utilizing the concept of Interconnection and Damping Assignment and nonlinear disturbance observer, a composite disturbance-rejection passivity-based controller is designed, offering a method for controlling the magnetic dipole strength of formation satellites. Finally, numerical simulations are conducted to demonstrate the viability of the proposed dynamics model and control strategy.

Global perspectives on the burden of sexually transmitted diseases: A narrative review.

Sexually transmitted diseases (STDs) pose a significant global health challenge with far-reaching social, economic, and public health implications. These infections have haunted humanity from ancient times to today, transcending geographical boundaries and cultural contexts. This article explores the multifaceted landscape of STDs, delving into their epidemiology, pathophysiology, clinical manifestations, and global response strategies. The global prevalence of STDs is staggering, with millions of new cases reported annually. Prominent among these infections is HIV/AIDS, which remains a major global health crisis, affecting over 38 million people worldwide. Additionally, bacterial STDs like chlamydia, gonorrhea, and syphilis continue to pose significant health risks, with millions of new cases reported yearly. Beyond the physical manifestations, STDs have profound social and economic implications. They can result in severe reproductive health issues, stigma, discrimination, and psychological distress, burdening healthcare systems and affecting individuals' quality of life. The global response to STDs has been multifaceted, with international organizations and governments implementing various prevention and control strategies, including sexual education programs and scaling up access to testing and treatment. However, challenges persist, including disparities in healthcare access, sociocultural factors influencing transmission, and evolving pathogens with increasing resistance to treatment. Through case studies and real-world examples, we illuminate the human stories behind the statistics, highlighting the lived experiences of individuals grappling with STDs and the complex interplay of factors shaping their journeys. Ultimately, this review calls for continued research, innovative strategies, and sustained global commitment to mitigating the burden of STDs and promoting sexual health and well-being for all.

Temperature control research for laser-induction hybrid strengthening process

ABSTRACT A comprehensive control methodology has been developed to address challenges such as temperature fluctuations, inconsistent depth penetration, and compositional integrity variations during laser-induction hybrid strengthening performed with consistent power parameters. This approach synergistically integrates a fuzzy controller and a decoupling compensator. In the initial part of this study, a temperature field model was utilized to investigate the effects of different process factors on temperature profiles. The analysis of step response data facilitated the development of mathematical models that delineate the correlation between the input factors and output responses. Subsequently, a decoupling compensation technique was used to reduce the coupling effects between temperature and its rate of change. A fuzzy controller was then developed to reduce the system's reliance on mathematical models, hence improving the accuracy of the control strategy. The results indicate that the application of fuzzy PI control and the implementation of decoupling compensation, the control strategy successfully improves the performance of the controller by minimizing parameter interactions. This strategy also incorporates real-time adjustment of PI parameters. Compared to conventional PI control, this method offers improved response speed and reduced overshoot. The laser-induction hybrid strengthening technique ensures superior temperature control, maintaining steady-state temperature precision within a 1% tolerance.

An Enhanced Power Allocation Strategy for Microgrids Considering Frequency and Voltage Restoration

In a microgrid, load power should be properly shared among multiple distributed generation (DG) units, not only for fundamental power but also for negative sequence and harmonic power. In this paper, the operation of a microgrid under imbalance and nonlinear load conditions is studied, and a consensus algorithm-based distributed control strategy is proposed for the microgrid power allocation, frequency, and voltage restoration. First of all, the output current of DG unit is decomposed by second-order generalized integrator (SOGI) modules to obtain the fundamental power and harmonic power through the power calculation formula. Then, state values of DG units, such as local power, frequency, and voltage, are transmitted on a sparse communication network. Under the action of a consensus algorithm, the real power of DG units is allocated following the equal increment principle; the reactive power, imbalance, and harmonic power are allocated according to the capacities of DG units; and the frequency of the microgrid and the voltage at the point of common coupling (PCC) are rated. In the consensus-based strategy, DG units only communicate with their neighbor units; thus, the “plug and play” function is reserved. Compared with the centralized control strategy, the proposed strategy with a distributed consensus protocol can simplify the maintenance and possible expansions of the system, making the microgrid more flexible. Moreover, as the structure of the detailed network is not required, it is easy to apply in practice. Simulation and experiment results are presented to verify the proposed method.

The ncRNA-mediated regulatory networks of defensins and lysozymes in Riptortus pedestris: involvement in response to gut bacterial disturbances

Insects depend on humoral immunity against intruders through the secretion of antimicrobial peptides (AMPs) and immune effectors via NF-κB transcription factors, and their fitness is improved by gut bacterial microbiota. Although there are growing numbers of reports on noncoding RNAs (ncRNAs) involving in immune responses against pathogens, comprehensive studies of ncRNA-AMP regulatory networks in Riptortus pedestris, which is one of the widely distributed pests in East Asia, are still not well understood under feeding environmental changes. The objective of this study employed the whole-transcriptome sequencing (WTS) to systematically identify the lncRNAs (long noncoding RNA) and circRNAs (circular RNA) and to obtain their differential expression from the R. pedestris gut under different feeding conditions. Functional annotation indicated that they were mainly enriched in various biological processes with the GO and KEGG databases, especially in immune signaling pathways. Five defensin (four novel members) and eleven lysozyme (nine novel members) family genes were identified and characterized from WTS data, and meanwhile, phylogenetic analysis confirmed their classification. Subsequently, the miRNA–mRNA interaction network of above two AMPs and lncRNA-involved ceRNA (competing endogenous RNA) regulatory network of one lysozyme were predicted and built based on bioinformatic prediction and calculation, and the expression patterns of differentially expressed (DE) defensins, and DE lysozymes and related DE ncRNAs were estimated and selected among all the comparison groups. Finally, to integrate the analyses of WTS and previous 16S rRNA amplicon sequencing, we conducted the Pearson correlation analysis to reveal the significantly positive or negative correlation between above DE AMPs and ncRNAs, as well as most changes in the gut bacterial microbiota at the genus level of R. pedestris. Taken together, the present observations provide great insights into the ncRNA regulatory networks of AMPs in response to rearing environmental changes in insects and uncover new potential strategies for pest control in the future.

Elucidating transport dynamics and regional division of PM2.5 and O3 in China using an advanced network model

Air pollution exhibits significant spatial spillover effects, complicating and challenging regional governance models. This study innovatively applied and optimized a statistics-based complex network method in atmospheric environmental field. The methodology was enhanced through improvements in edge weighting and threshold calculations, leading to the development of an advanced pollutant transport network model. This model integrates pollution, meteorological, and geographical data, thereby comprehensively revealing the dynamic characteristics of PM2.5 and O3 transport among various cities in China. Research findings indicated that, throughout the year, the O3 transport network surpassed the PM2.5 network in edge count, average degree, and average weighted degree, showcasing a higher network density, broader city connections, and greater transmission strength. Particularly during the warm period, these characteristics of the O3 network were more pronounced, showcasing significant transport potential. Furthermore, the model successfully identified key influential cities in different periods; it also provided detailed descriptions of the interprovincial spillover flux and pathways of PM2.5 and O3 across various time scales. It pinpointed major pollution spillover and receiving provinces, with primary spillover pathways concentrated in crucial areas such as the Beijing-Tianjin-Hebei (BTH) region and its surrounding areas, the Yangtze River Delta, and the Fen-Wei Plain. Building on this, the model divided the O3, PM2.5, and synergistic pollution transmission regions in China into 6, 7, and 8 zones, respectively, based on network weights and the Girvan Newman (GN) algorithm. Such division offers novel perspectives and strategies for regional joint prevention and control. The validity of the model was further corroborated by source analysis results from the WRF-CAMx model in the BTH area. Overall, this research provides valuable insights for local and regional atmospheric pollution control strategies. Additionally, it offers a robust analytical tool for research in the field of atmospheric pollution.

Semi-quantitative group testing for efficient and accurate qPCR screening of pathogens with a wide range of loads

BackgroundPathogenic infections pose a significant threat to global health, affecting millions of people every year and presenting substantial challenges to healthcare systems worldwide. Efficient and timely testing plays a critical role in disease control and transmission prevention. Group testing is a well-established method for reducing the number of tests needed to screen large populations when the disease prevalence is low. However, it does not fully utilize the quantitative information provided by qPCR methods, nor is it able to accommodate a wide range of pathogen loads.ResultsTo address these issues, we introduce a novel adaptive semi-quantitative group testing (SQGT) scheme to efficiently screen populations via two-stage qPCR testing. The SQGT method quantizes cycle threshold (Ct) values into multiple bins, leveraging the information from the first stage of screening to improve the detection sensitivity. Dynamic Ct threshold adjustments mitigate dilution effects and enhance test accuracy. Comparisons with traditional binary outcome GT methods show that SQGT reduces the number of tests by 24% on the only complete real-world qPCR group testing dataset from Israel, while maintaining a negligible false negative rate.ConclusionIn conclusion, our adaptive SQGT approach, utilizing qPCR data and dynamic threshold adjustments, offers a promising solution for efficient population screening. With a reduction in the number of tests and minimal false negatives, SQGT holds potential to enhance disease control and testing strategies on a global scale.

Terminal Synergetic Control for Plate Heat Exchanger

Terminal synergetic control (TSC) is proposed as a control strategy for the temperature management of a plate heat exchanger. The controller is designed by incorporating a selected macro variable with a time-varying sliding surface. The primary objective is to maintain precise control over the outlet temperature of the cold water. To assess the convergence characteristics of the newly proposed TSC approach, the simulation results achieved using TSC featuring a time-varying macro variable are compared to those obtained from the conventional synergetic control (SC) method. With an appropriate macro variable, the simulation results indicate a notable improvement in the convergence rate provided by our designed TSC method, compared to the conventional one. The desirable property of control input, the chattering-free condition, achieved by both TSC and SC approaches emphasizes the advantage of the synergetic control-based techniques over the conventional sliding mode controller. In conclusion, synergetic control-based techniques offer superior potential solutions for nonlinear feedback control problems.