Development Of Control Strategies Research Articles

Fixed‐time adaptive neural network tracking control for output‐constrained high‐order systems using command filtered strategy

SummaryThis article proposes a fixed‐time adaptive neural command filtered controller for a category of high‐order systems based on adding a power integrator technique. Different from existing research, the presented controller has the following distinguishing advantages: (i) a fixed‐time control framework is extended to the tracking control problem of high‐order systems. (ii) The error compensation mechanism eliminates filter errors that arise from dynamic controllers. (iii) Growth assumptions about unknown functions are relaxed with the help of adaptive neural networks. (iv) More general systems: the developed controller can apply to high‐order systems subject to uncertain dynamics, unknown gain functions and asymmetric constraints. Stability analysis shows that all states are semi‐globally uniformly ultimately bounded, and the convergence rate of tracking error is independent of initial conditions. Finally, simulation results validate the advantages and efficacy of the developed control scheme.

Tube-based model predictive control for linear systems with bounded disturbances and input delay

This paper proposes a tube-based model predictive control strategy for linear systems with bounded disturbances and input delay to ensure input-to-state stability. Firstly, the actual disturbed system is decomposed into a nominal system without disturbances and an error system. For the nominal system, solving an optimization problem, where the delayed control input is set as an optimization variable, yields a nominal control law that enables the nominal state signal to approach to zero. Then, for the error system, the Razumikhin approach is used to identify a robust control invariant set. Using the set invariance theorem, an ancillary control law is developed to confine the error state signal in the invariant set. Combining the two results, we obtain a control law that enables the state signal to remain within a robustly invariant tube. Finally, the effectiveness of the developed control strategy is validated by simulations.

Unravelling the role of key genes involved coffee leaf rust resistance

The biotrophic fungus Hemileia vastatrix is the pathogen responsible for coffee leaf rust, a devastating disease in several coffee-producing countries. Despite the importance of studying the interaction between Coffea and H. vastatrix, a more comprehensive understanding of the mechanisms involved in this pathosystem is necessary. The role of eight candidate genes was analyzed aiming at identifying and validating new important coffee resistance genes and understanding their interaction with H. vastatrix. These genes were identified in the most important sources of coffee resistance, the Híbrido de Timor CIFC 832/2 and CIFC 832/1. Previous works found six resistance genes and, in our research, other two new genes were identified in BAC clones and validated by RT-qPCR during compatible and incompatible interactions between Coffea and H. vastatrix. An interactome approach was performed using Coffea-H. vastatrix and Coffea-Coffea proteins to better understand the biological process and the interaction of the host-pathogen. Two networks of interactions from the compiled data were built focused on candidate genes associated with pre-haustorial resistance (12 and 24 h.a.i) in coffee plants against the pathogen. The results showed, for the first time, differentially expressed proteins (DEPs) positively regulated in the incompatible interaction Coffea-H. vastatrix. These coffee proteins interact with each other and with secreted and/or transmembrane pathogen proteins. The obtained results also show that DEPs found are involved in important plant defense pathways such as pathways associated with the response to wounds, signaling, regulation of the innate immune response and the transmembrane receptor protein serine/threonine kinase pathway. The present work shows the involvement of genes in both pathogen recognition and signaling cascades, which act in pre-haustorial defense mechanisms of HdT coffee. Therefore, the candidate genes analyzed, together with the biological processes elucidated, have the potential to contribute to the development of new control strategies against the fungus H. vastatrix within coffee breeding programs aiming to develop cultivars with durable resistance.

CRISPR/Cas9-mediated genome editing of Frankliniella occidentalis, the western flower thrips, via embryonic microinjection.

The western flower thrips, Frankliniella occidentalis, poses a significant challenge in global agriculture as a notorious pest and a vector of economically significant orthotospoviruses. However, the limited availability of genetic tools for F. occidentalis hampers the advancement of functional genomics and the development of innovative pest control strategies. In this study, we present a robust methodology for generating heritable mutations in F. occidentalis using the CRISPR/Cas9 genome editing system. Two eye-colour genes, white (Fo-w) and cinnabar (Fo-cn), frequently used to assess Cas9 function in insects were identified in the F. occidentalis genome and targeted for knockout through embryonic microinjection of Cas9 complexed with Fo-w or Fo-cn specific guide RNAs. Homozygous Fo-w and Fo-cn knockout lines were established by crossing mutant females and males. The Fo-w knockout line revealed an age-dependent modification of eye-colour phenotype. Specifically, while young larvae exhibit orange-coloured eyes, the colour transitions to bright red as they age. Unexpectedly, loss of Fo-w function also altered body colour, with Fo-w mutants having a lighter coloured body than wild type, suggesting a dual role for Fo-w in thrips. In contrast, individuals from the Fo-cn knockout line consistently displayed bright red eyes throughout all life stages. Molecular analyses validated precise editing of both target genes. This study offers a powerful tool to investigate thrips gene function and paves the way for the development of genetic technologies for population suppression and/or population replacement as a means of mitigating virus transmission by this vector.

Pharmacogenetic variability of tuberculosis biomarkers in native and mestizo Peruvian populations.

In Peru, 29 292 people were diagnosed with tuberculosis in 2022. Although tuberculosis treatments are effective, 3.4%-13% are associated with significant adverse drug reactions, with drug-induced liver injury (DILI) considered the most predominant. Among the first-line antituberculosis drugs, isoniazid is the main drug responsible for the appearance of DILI. In liver, isoniazid (INH) is metabolized by N-acetyltransferase-2 (NAT2) and cytochrome P450 2E1 (CYP2E1). Limited information exists on genetic risk factors associated with the presence of DILI to antituberculosis drugs in Latin America, and even less is known about these factors in the native and mestizo Peruvian population. The aim of this study was to determine the prevalence of NAT2 and CYP2E1 genotypes in native and mestizo population. An analytical cross-sectional analysis was performed using genetic data from mestizo population in Lima and native participants from south of Peru. NAT2 metabolizer was determined as fast, intermediate and slow, and CYP2E1 genotypes were classified as c1/c1, c1/c2 and c2/c2, from molecular tests and bioinformatic analyses. Of the 472 participants, 36 and 6 NAT2 haplotypes were identified in the mestizo and native population, respectively. In mestizo population, the most frequent NAT2*5B and NAT2*7B haplotypes were associated with DILI risk; while in natives, NAT2*5G and NAT2*13A haplotypes were associated with decreased risk of DILI. For CYP2E1, c1/c1 and c1/c2 genotypes are the most frequent in natives and mestizos, respectively. The linkage disequilibrium of NAT2 single nucleotide polymorphisms (SNPs) was estimated, detecting a block between all SNPs natives. In addition, a block between rs1801280 and rs1799929 for NAT2 was detected in mestizos. Despite the limitations of a secondary study, it was possible to report associations between NAT2 and CYP2E alleles with Peruvian native and mestizo by prevalence ratios. The results of this study will help the development of new therapeutic strategies for a Tuberculosis efficient control between populations.

Variable horizon multivariate driving pattern recognition framework based on vehicle-road two-dimensional information for electric vehicle

Driving condition is the core factor influencing vehicle energy management and power output strategies, hence realistic and accurate driving pattern recognition is crucial for realizing intelligent control of Electric Vehicles in complex scenarios. However, driving patterns mismatch with real scenarios caused by lack of road environment information and the reduced global recognition accuracy triggered by the fixed recognition horizon together constrain the development of intelligent control strategies. In this study, a framework for variable horizon multivariate driving pattern recognition strategy based on vehicle-road two-dimensional (2D) information is proposed. Specifically, multivariate driving pattern extraction is carried out based on real vehicle-road 2D data, and the extraction data serves as input for training offline driving pattern recognizer. To meet the challenge of large differences and frequent changes in driving conditions during the online application, a variable horizon recognition strategy is proposed based on the optimal horizon matching regularity. Finally, test results show that the proposed approach achieves an average recognition accuracy of 88.42% under different cycles, including 88.39% for complex driving cycles. Furthermore, the recognition accuracy is improved by 63.14% and 47.51% compared with the fixed long/short horizon. The framework can provide a basis for intelligent control strategies, thus broadening the Electric Vehicle application scenarios.

Quality by Design Considerations for Drop-on-Demand Point-of-Care Pharmaceutical Manufacturing of Precision Medicine.

Distributed and point-of-care (POC) manufacturing facilities enable an agile pharmaceutical production paradigm that can respond to localized needs, providing personalized and precision medicine. These capabilities are critical for narrow therapeutic index drugs and pediatric or geriatric dosing, among other specialized needs. Advanced additive manufacturing, three-dimensional (3D) printing, and drop-on-demand (DoD) dispensing technologies have begun to expand into pharmaceutical production. We employed a quality by design (QbD) approach to identify critical quality attributes (CQAs), critical material attributes (CMAs), and critical process parameters (CPPs) of a POC pharmaceutical manufacturing paradigm. This theoretical framework encompasses the production of active pharmaceutical ingredient (API) "inks" at a centralized facility, which are distributed to POC sites for DoD dispensing into/onto delivery vehicles (e.g., orodispersible films, capsules, single liquid dose vials). Focusing on the POC dispensing/dosing processes, QbD considerations and cause-and-effect analyses identified the dispensed API quantity and solid-state form (CQAs), as well as the nozzle diameter, system pressure channel, and number of drops dispensed (CPPs) for detailed investigation. Final assay quantification and content uniformity CQAs were measured from demonstrative levothyroxine sodium single-dose liquid vials of glycerin/water, meeting the standard acceptance values. Each POC facility is unlikely to maintain full quality control laboratory capabilities, requiring the development of appropriate atline or inline methods to ensure quality control. We developed control strategies, including atline ultraviolet-visible (UV-vis) verification of the API ink prior to dispensing, inline drop counting during dispensing, intermediate atline-dispensed volume checks, and offline batch confirmation by liquid chromatography-tandem mass spectrometry (LC-MS/MS) following production.

Differential Gene Expression in Porcine Lung Compartments after Experimental Infection with Mycoplasma hyopneumoniae.

Mycoplasma hyopneumoniae (Mhyo) is the causative agent of porcine enzootic pneumonia (EP), as well as one of the main pathogens involved in the porcine respiratory disease complex. The host-pathogen interaction between Mhyo and infected pigs is complex and not completely understood; however, improving the understanding of these intricacies is essential for the development of effective control strategies of EP. In order to improve our knowledge about this interaction, laser-capture microdissection was used to collect bronchi, bronchi-associated lymphoid tissue, and lung parenchyma from animals infected with different strains of Mhyo, and mRNA expression levels of different molecules involved in Mhyo infection (ICAM1, IL-8, IL-10, IL-23, IFN-α, IFN-γ, TGF-β, and TNF-α) were analyzed by qPCR. In addition, the quantification of Mhyo load in the different lung compartments and the scoring of macroscopic and microscopic lung lesions were also performed. Strain-associated differences in virulence were observed, as well as the presence of significant differences in expression levels of cytokines among lung compartments. IL-8 and IL-10 presented the highest upregulation, with limited differences between strains and lung compartments. IFN-α was strongly downregulated in BALT, implying a relevant role for this cytokine in the immunomodulation associated with Mhyo infections. IL-23 was also upregulated in all lung compartments, suggesting the potential involvement of a Th17-mediated immune response in Mhyo infections. Our findings highlight the relevance of Th1 and Th2 immune response in cases of EP, shedding light on the gene expression levels of key cytokines in the lung of pigs at a microscopic level.

Physiological and chaos effect on dynamics of neurological disorder with memory effect of fractional operator: A mathematical study

Background and objective:To study the dynamical system, it is necessary to formulate the mathematical model to understand the dynamics of various diseases that are spread worldwide. The main objective of our work is to examine neurological disorders by early detection and treatment by taking asymptomatic. The central nervous system (CNS) is impacted by the prevalent neurological condition known as multiple sclerosis (MS), which can result in lesions that spread across time and place. It is widely acknowledged that multiple sclerosis (MS) is an unpredictable disease that can cause lifelong damage to the brain, spinal cord, and optic nerves. The use of integral operators and fractional order (FO) derivatives in mathematical models has become popular in the field of epidemiology. Method:The model consists of segments of healthy or barian brain cells, infected brain cells, and damaged brain cells as a result of immunological or viral effectors with novel fractal fractional operator in sight Mittag Leffler function. The stability analysis, positivity, boundedness, existence, and uniqueness are treated for a proposed model with novel fractional operators. Results:Model is verified the local and global with the Lyapunov function. Chaos Control will use the regulate for linear responses approach to bring the system to stabilize according to its points of equilibrium so that solutions are bounded in the feasible domain. To ensure the existence and uniqueness of the solutions to the suggested model, it makes use of Banach’s fixed point and the Leray Schauder nonlinear alternative theorem. For numerical simulation and results the steps Lagrange interpolation method at different fractional order values and the outcomes are compared with those obtained using the well-known FFM method. Conclusion:Overall, by offering a mathematical model that can be used to replicate and examine the behavior of disease models, this research advances our understanding of the course and recurrence of disease. Such type of investigation will be useful to investigate the spread of disease as well as helpful in developing control strategies from our justified outcomes.

Development of a Genetic Algorithm-Based Control Strategy for Fuel Consumption Optimization in a Mild Hybrid Electrified Vehicle’s Electrified Propulsion System

Increasingly stringent pollutant emission regulations and a customer demand for a high-fuel economy drive the modern automotive industry to hurriedly solve the problem of decarbonization and powertrain efficiency, leading R&D towards alternative powertrain solutions and fuels. Electrification, today, plays the biggest role in the topic, with Mild Hybrid Electrified Vehicles (MHEVs) being the most cost-effective architectures, displaying dominance in smaller markets such as Brazil. One of the biggest challenges for HEVs’ development is the complexity of the hybrid control system, knowing when to actuate the electric machine, and the optimum power delivery, plus the gearshift schedule becomes a hard optimization problem that plays a key role in powertrain efficiency and cost savings for the customer. This paper proposes the implementation of a genetic algorithm (GA) as a machine learning-based control strategy to determine the torque split and the gear engaged for each driving condition of an MHEV operation, aiming to optimize fuel consumption. A quasi-static model of the vehicle was developed in Matlab/Simulink version 2022b, the virtual vehicle was then tested following the FTP75 and HWFET driving cycles. Simulation results indicate that the control decisions taken by the GA are qualitatively coherent for all operation conditions, and even quantitatively coherent in some cases, and that the software has the potential to be used as a control strategy outside the simulation environment, in future steps of development.

Histo-Blood Group Antigen-Producing Bacterial Cocktail Reduces Rotavirus A, B, and C Infection and Disease in Gnotobiotic Piglets.

The suboptimal performance of rotavirus (RV) vaccines in developing countries and in animals necessitates further research on the development of novel therapeutics and control strategies. To initiate infection, RV interacts with cell-surface O-glycans, including histo-blood group antigens (HBGAs). We have previously demonstrated that certain non-pathogenic bacteria express HBGA- like substances (HBGA+) capable of binding RV particles in vitro. We hypothesized that HBGA+ bacteria can bind RV particles in the gut lumen protecting against RV species A (RVA), B (RVB), and C (RVC) infection in vivo. In this study, germ-free piglets were colonized with HBGA+ or HBGA- bacterial cocktail and infected with RVA/RVB/RVC of different genotypes. Diarrhea severity, virus shedding, immunoglobulin A (IgA) Ab titers, and cytokine levels were evaluated. Overall, colonization with HBGA+ bacteria resulted in reduced diarrhea severity and virus shedding compared to the HBGA- bacteria. Consistent with our hypothesis, the reduced severity of RV disease and infection was not associated with significant alterations in immune responses. Additionally, colonization with HBGA+ bacteria conferred beneficial effects irrespective of the piglet HBGA phenotype. These findings are the first experimental evidence that probiotic performance in vivo can be improved by including HBGA+ bacteria, providing decoy epitopes for broader/more consistent protection against diverse RVs.

NOx Emission Predictions in Gas Turbines Through Integrated Data-Driven Machine Learning Approaches

Abstract The reduction of NOx emissions is a paramount endeavor in contemporary engineering and energy production, as these emissions are closely linked to adverse environmental and health impacts. The prediction of NOx emission from gas turbines through several integrated data-driven machine learning methods has been evaluated in study. The study compares the performance of ensemble and conventional machine learning models, demonstrating superior accuracy achieved by the ensemble models. Specifically, the Random Forest model achieved an accuracy rate of 91.68%, XGBoost yielded an accuracy of 91.54%, and CATBoost exhibited the highest accuracy at 92.76%. These findings highlight the capability of data-driven machine learning techniques in enhancing NOx emission predictions in gas turbines. The improved prediction by ensembles can be utilized in the development and implementation of more effective control and mitigation strategies in practical applications. Through the application of these advanced machine learning approaches, the gas turbine industry can play a pivotal role in minimizing its environmental impact while optimizing operational efficiency. This study also provides valuable insights into the effectiveness of ensemble machine learning models, advancing our understanding of their capabilities in addressing the critical issue of NOx emissions from gas turbines.

The Novel Insight of Gut Microbiota from Mouse Model to Clinical Patients and the Role of NF-κB Pathway in Polycystic Ovary Syndrome.

Polycystic Ovary Syndrome (PCOS) is a metabolic disorder characterized by hyperandrogenism and related symptoms in women of reproductive age. Emerging evidence suggests that chronic low-grade inflammation plays a significant role in the development of PCOS. The gut microbiota, a complex bacterial ecosystem, has been extensively studied for various diseases, including PCOS, while the underlying mechanisms are not fully understood. This review comprehensively summarizes the changes in gut microbiota and metabolites observed in PCOS and their potential association with the condition. Additionally, we discuss the role of abnormal nuclear factor κB signaling in the pathogenesis of PCOS. These findings offer valuable insights into the mechanisms of PCOS and may pave the way for the development of control and therapeutic strategies for this condition in clinical practice. By bridging the gap between mouse models and clinical patients, this review contributes to a better understanding of the interplay between gut microbiota and inflammation in PCOS, thus paving newways for future investigations and interventions.

Review of participatory epidemiology application in human health and nutrition and future directions

Participatory Epidemiology (PE) developed as a branch of veterinary epidemiology and has been widely used in infectious disease control and early warning in resource-limited settings. In the beginning, Participatory Epidemiology was focused on linking the researcher’s communication skills with participatory methods to enable the participation of livestock owners and herders in the identification and assessment of livestock diseases as well as in the development, monitoring, and evaluation of the disease control strategies. In this respect, the use of participatory techniques gradually developed into Participatory Epidemiology with increased recognition by epidemiologists who have developed several tools which are being adapted in human health and nutrition. Relative to veterinary uses and human nutrition, Participatory Epidemiology has not been used in public health in Kenya or for studies on important human diseases, such as Kala-azar. However, in the recent past there have been growing interests from epidemiologists to extend the testing and piloting of the Participatory Epidemiology approach in the arid and semi-arid settings of Kenya geared at empowering communities to identify and solve their health needs. Additionally, there is ongoing debate about the ability of epidemiology to give results that can be better applied in public health practice besides its ability to identify disease risk factors. Scientific research has recommended that participatory research be applied in epidemiology to bridge this descriptive gap and action. This review describes the implementation experiences and lessons learnt in the use of Participatory Epidemiology in understanding human diseases, both communicable and non-communicable as well as human nutrition, a key factor in health and wellness. The reviewed articles provide an understanding into the application of PE approaches which have been limited in pastoralist areas, where zoonotic diseases are prone. The article establishes that as opposed to more conventional methods, using Participatory Epidemiology can generate precise and scientifically credible information within a relatively short period. Data generated from Participatory Epidemiology can be sustainably used to effectively find solutions to the common community health problems based on local preferences for control options.

Behavioral and cognitive factors influencing tick-borne disease risk in northeast China: Implications for prevention and control strategies

The growth in ecotourism and nature-based recreational activities in China has resulted in an increased frequency of visits to green spaces, thereby elevating exposure to ticks and the subsequent risk of tick-borne diseases. This study comprehensively investigate individual behavioral and cognitive factors associated with the risk of contracting tick-borne diseases to facilitate the development of effective prevention and control strategies, supporting public health initiatives in high-prevalence regions. We conducted an extensive questionnaire survey among 3000 residents from three northeastern provinces in China (Heilongjiang, Jilin, and Liaoning), where tick-borne diseases exhibit relatively high prevalence. The survey focused on gathering information regarding participants' tick bite history, perception of tick-borne disease risks, and outdoor activity patterns. Using structural equations analysis, we explored the pathways and strengths of the associations between these factors. Our findings revealed an average self-reported tick bite rate of 14% among the participants. Notably, tick-borne encephalitis exhibited the highest self-reported prevalence of infection (4%) among tick-borne diseases, while both Lyme disease and Severe fever with thrombocytopenia syndrome had a prevalence of 2%. The average rate of tick bites among respondents' pets was 14%, with bites predominantly located on the ears, back, and abdomen. The strongest correlation was observed between tick bite rate and subsequent infections, emphasizing its role as the primary contributing factors to infectious status. Moreover, our results indicated that the causal structure of tick-borne disease infections varied across different cities, underscoring the significance of considering the ecological environment and regional knowledge on ticks. This study provides valuable insights into the current landscape of tick-borne disease infections in northeast China and identifies potential behavioral and cognitive factors, an aspect that has not been previously investigated. Our findings enable predictions on the future impact of knowledge dissemination efforts and improved urban facilities on mitigating tick bites and reducing tick-borne disease infections.

Application and division of automatic control system

To make the machine operate optimally and produce the best possible results, we need to complete the following steps: set the goal, measure deviation, correct deviation, and develop control strategies by analyzing feedback mechanisms. The control objectives can be either a constant control to maintain a specific value or a following control to track a present trajectory. The system will measure the deviation between the actual output and the target output, and according to it, the system will generate the corresponding control signal to reduce the size of the deviation. This process involves comparing the difference between the actual output and the desired output and generating control instructions based on this difference.

A Set of Multiresistant Isolates of Mycoplasma bovis Subtype ST-1 with a Variable Susceptibility to Quinolones Are Also Circulating in Spain.

Mycoplasma bovis (M. bovis) is one of the worldwide most important infectious agents involved in respiratory complex diseases (RCD). In Spain, the endemic presence of subtypes ST-2 and ST-3 with phenotypic differences linked to their susceptibility to fluoroquinolones opened the way to develop control strategies focused on previous diagnosis of the subtype and the use of directed therapies when M. bovis were involved in RCD. Surprisingly, microbiological studies conducted during 2023 evidenced for the first time the presence of Spanish isolates of a new polC-subtype, previously classified as ST-1, recovered from calves with respiratory symptoms and pneumonia in different areas of the country (n = 16). Curiously, the minimum inhibitory concentration (MIC) to a panel of antimicrobials revealed phenotypic differences between these ST-1 isolates when using fluoroquinolones (FLQ). There is no geographical correlation between MIC profiles even for a set of 8 isolates recovered from different animals in the same flock. Sequencing of 4 genes (gyrA, gyrB, parC and parE) encoding quinolone resistance-determining regions (QRDR) evidenced the presence of accumulate mutations in 2 ST-1 isolates with high FLQ MICs, but not in all them (n = 3), thus suggesting that, as previously recorded for ST-2 isolates, other mechanisms should be involved in the acquisition of resistence to these antimicrobials. Additionally, as previously detected in the Spanish ST-2 and ST-3, subtype ST-1 isolates are also resistant to macrolides or lincosamides.

First Isolation of Japanese Encephalitis Virus Genotype IV from Mosquitoes in Australia.

Introduction: Widespread transmission of Japanese encephalitis virus (JEV) genotype four (GIV) occurred across mainland Australia in 2022. This resulted in forty-five human cases, including seven deaths, and the identification of JEV infection in over 80 commercial piggeries. Materials and Methods: We collected mosquitoes which were trapped using CO2-baited light traps deployed near piggeries reporting disease or in regions linked to human cases in the Wide Bay region in the state of Queensland. Mosquitoes from four traps yielded JEV RNA by real-time RT-PCR. Pools containing RNA positive mosquitoes were inoculated onto mosquito cell monolayers. Discussion: A single isolate of JEV was obtained from a pool of mixed mosquito species. Near whole genome sequencing and phylogenetic analysis of the JEV isolate demonstrated its high genomic relatedness with JEV GIV pig sequences sampled from Queensland and the state of New South Wales in 2022. Conclusion: We report the first isolation of JEV GIV from mosquitoes collected in Australia. With only a few JEV GIV isolates available globally, the isolate we report will be essential for future research of JEV host interactions, evolution and disease markers, and development of effective therapies, vaccines, diagnostic assays, and mosquito control strategies.

Bumblebees compensate for the adverse effects of sidewind during visually guided landings.

Flying animals often encounter winds during visually guided landings. However, how winds affect their flight control strategy during landing is unknown. Here, we investigated how sidewind affects the landing performance and sensorimotor control of foraging bumblebees (Bombus terrestris). We trained bumblebees to forage in a wind tunnel, and used high-speed stereoscopic videography to record 19,421 landing maneuvers in six sidewind speeds (0 to 3.4 m s-1), which correspond to winds encountered in nature. Bumblebees landed less often in higher windspeeds, but the landing durations from free flight were not increased by wind. By testing how bumblebees adjusted their landing control to compensate for adverse effects of sidewind on landing, we showed that the landing strategy in sidewind resembled that in still air, but with important adaptations. Bumblebees landing in a sidewind tended to drift downwind, which they controlled for by performing more hover maneuvers. Surprisingly, the increased hover prevalence did not increase the duration of free-flight landing maneuvers, as these bumblebees flew faster towards the landing platform outside the hover phases. Hence, by alternating these two flight modes along their flight path, free-flying bumblebees negated the adverse effects of high windspeeds on landing duration. Using control theory, we hypothesize that bumblebees achieve this by integrating a combination of direct aerodynamic feedback and a wind-mediated mechanosensory feedback control, with their vision-based sensorimotor control loop. The revealed landing strategy may be commonly used by insects landing in windy conditions, and may inspire the development of landing control strategies onboard autonomously flying robots.

Research on adaptive coupling trajectory tracking anti-swing control strategy for three-dimensional double-pendulum overhead crane

In the transportation process of a three-dimensional double-pendulum overhead crane, the system is significantly influenced by the pronounced coupling introduced by the double-pendulum effect, posing a considerable challenge for the development of effective anti-swing control strategies. Moreover, uncertainties in certain system parameters and errors in trolley positioning contribute to the complexity of anti-swing control strategy design. To address these practical issues, a control strategy is proposed: First, an S-shaped transport trajectory with minimal positioning error is introduced, incorporating more system parameters into the coupling signal design to enhance system coupling. Based on this, an error-coupled trajectory signal is introduced. Second, the error-coupled trajectory signal is integrated into the energy function, and leveraging adaptive principles, an adaptive coupled trajectory tracking anti-swing control strategy is proposed to estimate uncertain system parameters online. Subsequently, the asymptotic stability of the equilibrium point of the closed-loop system is verified using the Lyapunov techniques and the Barbalat lemma. Finally, through simulations and experiments, it is demonstrated that the proposed control strategy not only ensures precise positioning of the trolley and bridge but also effectively suppresses oscillations of the hook and load, exhibiting excellent control performance. Even in scenarios where system parameters undergo changes or external disturbances are introduced, the proposed control strategy exhibits strong robustness and holds significant practical potential.