Sampling Interval Research Articles

Quantifying protein kinetics in vivo: Influence of precursor dynamics on product labeling.

Protein kinetics can be quantified by coupling stable isotope tracer methods with mass spectrometry readouts; however, inter-connected decision points in the experimental design affect the complexity of the workflow and impact data interpretations. For example, choosing between a single bolus (pulse-chase) or a continuous exposure protocol influences subsequent decisions regarding when to measure and how to model the temporal labeling of a target protein. Herein, we examine the merits of in vivo tracer protocols, we direct attention towards stable isotope tracer experiments that rely on administering a single bolus since these are generally more practical to use as compared to continuous administration protocols. We demonstrate how the interplay between precursor and product kinetics impacts downstream analytics and calculations by contrasting fast vs slow turnover precursors (e.g. 13C-leucine vs 2H-water, respectively). Although the data collected here underscore certain advantages of using longer lived precursors (e.g. 2H- or 18O-water) the results also highlight the influence of tracer recycling on measures of protein turnover. We discuss the impact of tracer recycling and consider how the sampling interval is critical for interpreting studies. Finally, we demonstrate that tracer recycling does not limit the ability to perform back-to-back studies of protein kinetics. It is possible to run experiments in which subjects are used as their own controls even though the precursor and product remain labeled following an initial tracer dosing.

Handling Domain Drift and Unknown Fault Detection in Rotating Machinery Using Few‐Shot Learning With Data Scaling

ABSTRACTThis article presents a novel approach for fault diagnosis in rotating machinery using the few‐shot learning‐based unknown recognition and classification (FSLB‐UR&C) model. The core contribution of this research lies in addressing the challenges of domain drift, unknown fault detection, and uneven sampling intervals. The model effectively incorporates data scaling using Min–Max scaling to manage vibration data drift without altering the source data distribution, significantly extending the classification range. Principal component analysis visualizations substantiate the superior performance of Min–Max scaling over standard methods in processing drifted data, thus enhancing model accuracy. We validated the model on an in‐house bearing simulator and a coal conveyor motor, demonstrating its enhanced ability to classify drifted data and identify previously unknown faults. Additionally, time interpolation was applied to handle irregular sampling intervals, further optimizing the framework's robustness. Compared to other deep learning techniques, the FSLB‐UR&C model with data scaling exhibits markedly improved performance.

Using Delay for Control

This article reviews two techniques that use delay for control: time-delay approaches to control problems (which initially may be free of delays) and the intentional insertion of delays into the feedback. We begin with a now widely used time-delay approach to sampled-data control. In networked control systems with communication constraints, this is the only method that accommodates transmission delays larger than the sampling intervals. We present a predictor-based design that enlarges the maximum allowable delay, which is important for practical implementations. We then discuss methods that use artificial delays via simple Lyapunov functionals that lead to feasible linear matrix inequalities for small delays and simple sampled-data implementations. Finally, we briefly present a new time-delay approach—this time to averaging. Unlike previous results, this approach provides the first quantitative bounds on the small parameter, making averaging-based control (including vibrational and extremum-seeking control) reliable.

Population pharmacokinetic modeling of paired plasma-breast milk lamivudine data for estimation of infant exposure in breastfeeding mother-infant pairs.

Around 1.2 million women living with HIV give birth annually, majority of whom will breastfeed their infants while receiving antiretroviral therapy (ART). Lamivudine, a component of first-line ART regimens crosses from maternal plasma to breast milk, with measurable concentrations in some breastfed infants. Wide variability in plasma-to-breast milk transfer has been reported within- or across studies, probably due to differences in sampling framework. This work sought to characterize the milk-to-plasma transfer of lamivudine, quantify inter-patient variability and associated factors, and predict exposure of a breastfed infant. We explored data from an observational pharmacokinetic study that included 35 Ugandan mothers and their infants. Mothers received lamivudine doses of 150 mg twice daily or 300 mg once daily as part of their antiretroviral regimen. Pharmacokinetic sampling was undertaken across two visits approximately 8 weeks apart, providing 248 maternal plasma, 256 breast milk-, and 151 infant blood concentrations, measured across a 24-h sampling interval. A one-compartmental model best described the plasma disposition of lamivudine, with first-order absorption, interindividual variability on clearance and volume of distribution, and a proportional residual error model. A lag in time of plasma-to-breast milk drug accumulation was described using an effect compartment model with a milk-to-plasma ratio of 1.77. An estimated daily infant dose of 179.3 μg/kg (range: 125.8, 282.3) closely predicted the observed infant steady-state concentrations and translated into 3.34% (2.13, 7.20) and 3.35% (1.10, 7.15) of the standard daily maternal dose in visits 1 and 2, respectively. The established modeling framework can be extended to other drugs.

Multi‐timescale optimal scheduling of microgrids for generating new energy output scenarios based on correction error sampling intervals

AbstractMicrogrid can realize energy saving and emission reduction and multi‐energy complementation, but the fluctuation of renewable energy output and the error of day‐ahead scheduling will threaten the stability of microgrid operation. For this reason, this article proposes a microgrid multi‐timescale optimal scheduling method based on new energy output scenario generation. First, the microgrid framework of this article is introduced, and an energy cycle emission reduction model taking into account electricity‐to‐gas conversion is designed; second, a new energy prediction error model is established based on the prediction box and Gaussian hybrid model, and the scenario of wind and photovoltaic power generation is generated by correcting the sampling intervals for error sampling; and then, based on the day‐ahead scheduling plan, an intraday cooling, heating and electricity two‐layer rolling optimization model is established to correct the day‐ahead scenario. Finally, the example analysis shows that the cyclic emission reduction model can realize the recycling of resources, reduce the fuel cost and carbon emission, and the generation of scenarios for wind power can smooth out the fluctuation of new energy power, which, together with the intra‐day two‐layer rolling optimization, can reduce the day‐ahead scheduling error and improve the stability of microgrid operation.

Coda-derived source properties estimated using local earthquakes in the Sea of Marmara, Türkiye

Abstract. Accurate estimates of the moment magnitude of earthquakes that physically measure the earthquake source energy are crucial for improving our understanding of seismic hazard in regions prone to tectonic activity. To address this demand, a method involving coda wave modelling was employed to estimate the moment magnitudes of earthquakes in the Sea of Marmara, northwestern Türkiye. This approach enabled us to model the source displacement spectrum of 303 local earthquakes efficiently recorded at 49 regional seismic stations between 2018 and 2020 in the region. The coda wave traces of individual events were inverted across 12 frequency ranges between 0.3 and 16 Hz. The resultant coda-derived moment magnitudes were found to be in good accordance with the conventional local magnitude estimates. However, the notable move-out between local magnitude and coda-derived moment magnitude estimates for smaller earthquakes less than a magnitude of 3.5 likely occurs due to potential biases arising from incorrect assumptions for anelastic attenuation and/or the finite sampling intervals of seismic recordings. Scaling relations between the total radiated energy and seismic moment imply a non-self-similar behaviour for the earthquakes in the Sea of Marmara. Our findings suggest that larger earthquakes in the study area exhibit distinct rupture dynamics compared to smaller ones, resulting in a more efficient release of seismic energy. Hence, we introduce an empirical relationship obtained from the scatter between local magnitude and coda-derived moment magnitude estimates.

Research on Distributed Autonomous Timekeeping Algorithm for Low-Earth-Orbit Constellation

The time of a satellite navigation system is primarily generated by the main control station of the ground system. Consequently, when ground stations fail, there is a risk to the continuous provision of time services to the equipment and users. Furthermore, the anticipated launch of additional satellites will further strain the satellite–ground link. Next-generation satellite navigation systems will rely on time deviation measurements from inter-satellite links to independently establish and maintain a space-based time reference, enhancing the system’s reliability and robustness. The increasing number of low-Earth-orbit satellite navigation constellations provides ample resources for establishing a space-based time reference. However, this also introduces challenges, including extensive time scale computations, increased link noise, and low clock resource utilization. To address these issues, this paper proposes a Distributed Kalman Plus Weight (D-KPW) algorithm, which combines the benefits of Kalman filtering and the weighted average algorithm, balancing the performance with computational resources. Furthermore, an adaptive clock control algorithm, D-KPW (Control), is developed to account for both the short-term and long-term frequency stability of the time reference. The experimental results demonstrate that the frequency stability of the time reference established by the D-KPW (Control) algorithm reaches 7.40×10−15 and 2.30×10−15 for sampling intervals of 1000 s and 1,000,000 s, respectively, outperforming traditional algorithms such as ALGOS. The 20-day prediction error of the time reference is 1.55 ns. Compared to traditional algorithms such as AT1, ALGOS, Kalman, and D-KPW, the accuracy improves by 65%, 65%, 66%, and 67%, respectively.

Trajectory tracking for autonomous surface ships using Gaussian process regression and model predictive control with BVS strategy

Autonomous navigation is critical to the development of next-generation shipping systems. The proposal of intelligent ships enables innovation in the shipping and shipbuilding industry and increases the safety and efficiency of ship operations. Autonomous control of surface ships to follow a prescribed trajectory is critical in a variety of maritime applications. This paper proposes a trajectory tracking control strategy for autonomous surface ships that combines nonparametric modelling using Gaussian Process Regression (GPR) with the Model Predictive Control (MPC) framework. A Bézier curve-based Virtual Ship(BVS) guidance strategy is proposed to convert dynamic trajectory points into reference heading angles and speeds, such that the trajectory tracking problem can be decomposed into heading control and speed control problems. Gaussian process regression is utilised to identify the correlation between propeller revolution speed and ship speed, as well as the correlation between rudder angle and heading angle based on experimental data. Two GPR models are therefore constructed as the prediction models for designing MPC controllers for heading control and speed control, respectively. Nonlinear optimisation algorithms are utilised to search for optimal control commands in each sampling interval to solve the optimisation problem in MPC with GPR models and input constraints. Simulations are carried out to evaluate the effectiveness of the proposed method.

The multi-objective economic statistical design of the p-chart: NSGA II approach

A control chart is a crucially important tool in statistical process control that is essentially used to differentiate between assignable and chance causes of variation. With the advent of Six Sigma in the parlance of quality management, the control chart assumes more significance to hold the gain in the control phase after attaining process improvement in the improve phase. The sample size, sampling interval, and control limits’ multiplier are three important parameters required to design an effective as well as efficient control chart. Many approaches like economic design, statistical design, and economic statistical design of control charts have been studied by several researchers. All these approaches consider a single objective function. In this paper, we have proposed a multi-objective design of the p-chart, where we are minimizing both out-of-control Average Run Length ( ARL δ ) and the expected cost per cycle ( C E ). Non-dominated Sorting Genetic Algorithm II (NSGA II) is used to solve the proposed multi-objective model. The proposed approach is demonstrated with the help of two numerical examples and the corresponding results are found to be quite encouraging for minimizing both objectives in comparison to another pertinent model given in the literature. Sensitivity analysis has been carried out to give credence to the worth of the proposed approach.

An Estimation of Baricitinib by AQbD-driven UV Spectrophotometry Development and Validation Process

Background: Baricitinib (BCTB) is a novel Janus Kinase (JAK) 1 and 2 inhibitor used in the therapy of rheumatoid arthritis, approved by the “Food and Drug Administration” in 2018. It has significant dose-dependent effectiveness and severe side effects. Thus, it is crucial to figure out its concentration in developed dosage forms. The literature search revealed that there has only been one UV spectroscopy technique documented up to this point. Methanol was chosen as the detection medium in this approach, which is not comparable with plasma or serum. As a result, the preliminary research suggested developing a UV spectroscopic approach that can estimate BCTB concentration and compare it to its concentration in the plasma or serum. Thus, in the proposed method, 7.4 pH phosphate buffer was selected as a mobile phase. Aim: Using the Analytical Quality by Design (AQbD) methodology, a simple, robust spectrophotometric method for the detection of BCTB in API form and Niosomes drug delivery system is designed and assessed. Methods: In the AQbD approach, a face-centered CCD design of Design Expert 13 software was used to evaluate two critical method variables: scanning speed and sampling interval. The design space suitability was confirmed by standard error and overlay plots. The 2-D contour and 3-D response surface plots were used to forecast the relationship between the response variable and predictor variables. Results: Baricitinib displays an absorption maximum at 249.40 nm in saline phosphate buffer pH 7.4. The distinguished linearity of the method was obtained over a concentration of 5–30 µg/ml with a correlation coefficient (R2 ) value of 0.998. BCTB % assay was found to be near 99 %. Intraday and Interday precision were found to have % RSDs of 0.067–0.488 and 0.146–0.942, respectively. Conclusion: The established spectrophotometric technique was observed to be precise as per ICH revised guidelines ICH Q2 (R1) and Q14 for analytical method validation. Our findings are instructional for the future design and development of safe and reliable therapeutic dosage forms of BCTB for rheumatoid arthritis.

Timing issues on power side-channel leakage of advanced encryption standard circuits designed by high-level synthesis

In recent years, field programmable gate array (FPGA) have been used in many internet of things (IoT) devices and are equipped with cryptographic circuits to ensure security. However, they are exposed to the risk of cryptographic keys being stolen by side-channel attacks. Countermeasures against side-channel attacks have been developed, but they are becoming more of a threat to IoT devices due to the diversity of attacks. Therefore, it is necessary to understand the basic characteristics of side-channel attacks. Therefore, this study clarifies the relationship between two timing issues, the clock period of the circuit and the power sampling interval, and the amount of side-channel leakage. We design seven advanced encryption standard (AES) circuits with different clock periods and conduct empirical experiments using logic simulations to clarify the correlation between the two timings and the amount of side-channel leakage. T-test is used to evaluate the leakage amount, which is evaluated based on four metrics. From the results, we argue that the clock period and sampling interval do not interfere with each other in the side-channel leakage amount.

Determining the confidence interval for non-probabilistic surveys: Method proposal and validation

This paper proposes a novel method for determining confidence intervals in non-probabilistic survey samples, addressing the challenge of statistically quantifying uncertainty in such samples. The method utilizes combinatorial calculations to assess the myriad combinations possible in the unsampled population, applying the symmetric property of binomial coefficients to optimize computational efficiency. The proposed approach offers a practical solution, making it feasible to calculate confidence intervals even with large sample sizes. This methodology represents a significant advancement in survey research, providing a tool to evaluate and generalize non-probabilistic data, particularly in contexts where probabilistic sampling is not viable. The paper also validates the method using extensive testing, demonstrating its applicability and effectiveness in real-world scenarios.

Influence of Ocean Current Features on the Performance of Machine Learning and Dynamic Tracking Methods in Predicting Marine Drifter Trajectories

Accurately and rapidly predicting marine drifter trajectories under conditions of information scarcity is critical for addressing maritime emergencies and conducting marine surveys with resource-limited unmanned vessels. Machine learning-based tracking methods, such as Long Short-Term Memory networks (LSTM), offer a promising approach for trajectory prediction in such scenarios. This study combines satellite observations and idealized simulations to compare the predictive performance of LSTM with a resource-dependent dynamic tracking method (DT). The results indicate that when driven solely by historical drifter paths, LSTM achieves better trajectory predictions when trained and tested on relative trajectory intervals rather than the absolute positions of individual trajectory points. In general, LSTM provides a more accurate geometric pattern of trajectories at the initial stages of forecasting, while DT offers superior accuracy in predicting specific trajectory positions. The velocity and curvature of ocean currents jointly influence the prediction quality of both methods. In regions characterized by active sub-mesoscale dynamics, such as the fast-flowing and meandering Kuroshio Current and Kuroshio Current Extension, DT predicts more reliable trajectory patterns but lacks precision in detailed position estimates compared to LSTM. However, in areas dominated by the fast but relatively straight North Equatorial Current, the performance of the two methods reverses. The two methods also demonstrate different tolerances for noise and sampling intervals. This study establishes a baseline for selecting machine learning methods for marine drifter prediction and highlights the limitations of AI-based predictions under data-scarce and resource-constrained conditions.

Fastidious adherence to 0/1-hour high-sensitivity troponin T testing reduces emergency department length of stay in patients presenting with suspected acute coronary syndromes

Abstract Background High-sensitivity cardiac troponins (hs-cTn) are key clinical biomarkers in patients presenting with suspected acute coronary syndromes (ACS) to Emergency Departments (EDs). They have facilitated rapid 0h/1h troponin testing protocols, leading to safe, effective, and efficient triage of patients. However, early distinction of myocardial infarction (MI), which has a reported frequency of less than 10% in most cohorts, from the apparently stable hs-cTn elevations of chronic myocardial injury (CMI) remains a challenge. Often CMI patients require confirmation by further testing and observation, prolonging patient length of stay. Further, reliably obtaining serial hs-cTn samples at 60-minutes in a busy ED setting may be difficult. A potential explanation for only 5-6% of Australian sites adopting 0h/1h hs-cTn testing. Methods and Results We undertook a case-control study between March and August 2023 among patients with suspected ACS presenting to Liverpool Hospital ED (Sydney, Australia). 185 patients (cases) underwent hs-cTnT sampling at 30- and 60-minutes (± 5 minutes) after baseline. Diagnosis of MI was made in 8.6%, 35.7% were placed into a further observation group and MI ruled-out in 55.7%. The latter two groups were matched to 169 patients (controls) who received ‘usual’ care, which was a 0h/1h hs-cTnT testing protocol. The case and control arms were matched by age, gender, time from symptom onset and main presenting symptom. Control patients had a median sampling interval of 118 [85 to 174] minutes, rather 60 minutes as the protocol directs. When comparing the paired differences between the case and control arms, there was a mean reduction in length of stay of 104 (95% CI: 41 to 186, P = 0.01) and 47 (95% CI: 11 to 84, P = 0.01) minutes, for the observation and rule-out MI groups respectively. The figure shows the distribution of lengths of stay by group. Among the rule-out group, Wilcoxon signed rank test indicated the control arm had a longer median length of stay of 6.3 [5.1 to 8.4] hours, compared to 5.0 [4.5 to 7.1] hours in the case arm (P=0.005). Similarly, in the further observation group, control patients also had a longer median length of stay of 9.2 [6.9 to 10.4] hours compared to case patients of 8.1 [5.1 to 10.3] hours (P = 0.009). At 30-days, ED representation rates were 10.7% in controls and 8.3% in cases, rehospitalisation 3.6% in controls and 5.3% in cases, cardiac death 0% in both arms and non-cardiac death 1% in both arms. There were no differences in event rates between each arm (P > 0.05). Conclusion Accurate 0h/1h hs-cTnT testing reduces patient length of stay in ED and offers more personalised prediction of MI risk. The importance of adherence to protocol-directed sampling times is clear, where further studies are required regarding routine implementation in a busy ED setting.Exact 60 min TnT and LOSOutcomes at 30 days

Rapid rule-out of acute myocardial infarction using the 0/1-hour algorithm for cardiac troponins in emergency primary care: the OUT-ACS implementation study

Abstract Background Acute chest pain in primary care often results in defensive and unnecessary hospital referrals due to the limited ability to rule out an acute myocardial infarction (AMI) outside of hospital. In a previous prospective observational study (OUT-ACS; One-hoUr Troponin in a low-prevalence population of Acute Coronary Syndrome), we demonstrated that the ESC 0/1-hour algorithm for high-sensitivity cardiac troponin T (hs-cTnT) would be safe and cost-effective if implemented to assess low-risk patients with chest pain in emergency primary care. Purpose We evaluated the real-world applicability, personnel adherence, and disposition of low-risk patients with chest pain following the implementation of the ESC 0/1-hour algorithm in emergency primary care. Methods In April 2023, the ESC 0/1-hour algorithm for hs-cTnT was implemented at a large emergency primary care clinic in Norway. The clinic is located close to a hospital, enabling the patient to wait at the clinic while blood samples are analysed for hs-cTnT at the hospital laboratory. For the following six months, all patients ≥18 years with acute non-traumatic chest discomfort, offered hs-cTnT measurements at the clinic, were anonymously registered, including their discharge diagnoses and disposition from the clinic. By applying the ESC 0/1-hour algorithm, patients were assigned to rule-out, rule-in or further observation. Patients in the observation group were offered a 4-hour hs-cTnT measurement before a final decision. Results During the six-month period, 10.3% (n=3,053/29,631) of the clinic's consultations involved chest complaints. Hs-cTnT measurements were conducted in 32.6% (n=995/3,053) with a median age of 58 years (IQR 45-68), 50.6% being female. A single hs-cTnT <5 ng/L, sampled ≥3 hours since symptom onset, triaged 24.1% (n=240/995) of the patients towards the rule-out group, increasing to 63.8% after adding a 1-hour measurement. In the rule-in group (n=42/995, 4.2%), 16 patients were diagnosed with an AMI. The intermediate observation group (n=318/995, 32.0%, 4 AMIs) was reduced to 23.0 % after a 4-hour hs-cTnT measurement. Healthcare personnel adhered well to the algorithm, with a median sampling interval of 63 minutes (IQR 60-66) and 4.5 hours (IQR 4.1-5.5) for the 1- and 4-hour measurements, respectively. The algorithm was misinterpreted or overruled in 8.8% of the cases. Ultimately, only 14.8% required hospital transfer following troponin assessment at the clinic. The remaining patients were sent home or managed in the outpatient setting. Conclusions The OUT-ACS strategy, utilizing a 0/1/4-hour algorithm for hs-cTnT measurements in emergency primary care, is safe, effective and applicable, and the strategy contributes to reduced hospital referrals for low-risk patients with chest pain.0/1/4-hour algorithm for hs-cTnTDisposition

Relaxed Event Triggered Control of Discrete‐Time Takagi‐Sugeno Systems Based on Homogeneous Polynomial Method

ABSTRACTThis article presents an event triggered control approach for discrete‐time T‐S fuzzy systems, which integrates the homogeneous polynomial method and a dynamic event triggering mechanism. First, a kind of discrete‐time loop functions and the dynamic event triggering scheme based on periodic sampling are constructed to establish stability conditions with known system information. Second, by introducing the homogeneous polynomial method to relax the stability criteria, the sampling interval is increased and the system conservatism is reduced. Additionally, an integrated framework for collaboratively designing the event triggering functions and controllers is proposed, which reduces the complexity of sampling transmission while ensuring system stability. Finally, numerical and practical examples are used to illustrate the feasibility and effectiveness of the proposed method.

Impact of the Extent of Population Aging on Economic Development and Countermeasure

With the comprehensive prosperity and development of human society, human life expectancy shows a rising trend, fertility rate shows a declining trend, the world is facing a severe problem of population aging, the ensuing economic impact is the focus we need to pay attention to. Taking various countries and regions in the world from 1960 to 2019 as samples, this paper conducted an empirical test on transnational panel data and found that old age dependency would significantly hinder economic growth. The conclusion remained valid after the robustness test of replacing explanatory variables, instrumental variable method and changing sample interval. Further study found that old-age dependency ratio will also significantly reduce consumption expenditure. Based on this, this paper gets inspiration and puts forward corresponding policy suggestions to mitigate the adverse impact of population aging on China's social and economic development.

Variable Parameters t Chart: An Application in Automobile Engine Piston Rings Manufacturing Process

The [Formula: see text] type charts possess a drawback as they may falsely indicate out-of-control (OOC) situations when the process standard deviation is unstable. They are not robust against estimation errors or variations in the process standard deviation. To overcome this setback, the [Formula: see text] type charts were developed. This research introduces the variable parameters (VP) [Formula: see text] chart for effectively monitoring the process mean. This study encompasses the implementation, optimal designs and performance evaluation of the proposed VP [Formula: see text] chart. The average time to signal (ATS), standard deviation of the time to signal (SDTS) and expected average time to signal (EATS) criteria are used to assess and compare the performance of the VP [Formula: see text] chart with existing charts, such as Shewhart (SH) [Formula: see text], synthetic (Syn) [Formula: see text], side-sensitive group runs (SSGR) [Formula: see text], exponentially weighted moving average (EWMA) [Formula: see text] and variable sampling interval (VSI) EWMA [Formula: see text] charts. From extensive analysis, we observe that the VP [Formula: see text] chart outperforms the SH [Formula: see text], Syn [Formula: see text] and SSGR [Formula: see text] charts for all sizes of mean shifts. However, the EWMA [Formula: see text] and VSI EWMA [Formula: see text] charts exhibit better performance for small shifts. Additionally, to demonstrate the implementation and usage of the proposed VP [Formula: see text] chart, we present a real-world manufacturing example.

Fuzzy adaptive asynchronous sampled-data consensus control for nonlinear multi-agent systems under DoS attacks

This article investigates the problem of fuzzy adaptive resilient asynchronous sampled-data consensus control (SDCC) for a class of uncertain nonlinear strict-feedback multi-agent systems (MASs) under denial-of-service (DoS) attacks and with a directed graph. Firstly, the upper bounds on the sampling intervals and a distributed sampled-data observer are proposed to estimate the leader's output and its high-order derivatives under DoS attacks. Then, the sufficient conditions to ensure the asymptotic convergence of the designed distributed sampled-data observer are established. Based on the distributed sampled-data observer and backstepping control design technique, a distributed fuzzy adaptive resilient control algorithm is developed. It is proved that the proposed control scheme can guarantee that the controlled MASs are stable, and the consensus errors converge to a small neighbourhood of zero. Finally, a simulation example on heterogeneous vehicular platoons verifies the feasibility of the proposed control scheme.

Enhanced Looped Lyapunov Functional for Sampled-Data Control for T-S Fuzzy Systems with Time Delay

This paper addresses the problem of sampled-data control in T-S fuzzy systems with time delays. Initially, a negative-definite criterion is developed for the matrix containing membership functions with the help of the idea of a switching mechanism. Building upon this criterion, a membership-function-based looped Lyapunov functional is introduced. Furthermore, a sampling-time-dependent looped Lyapunov functional is constructed, incorporating crucial information about the sampling instance. Notably, this functional matrix is not constrained to satisfy positive-definiteness, and it varies with each sampling point. By utilizing the enhanced looped Lyapunov functional, less conservative stability conditions and a new controller criterion for the T-S fuzzy system with time delays are derived. This demonstrates that our proposed stability conditions allow for a larger sampling interval compared to existing work. Finally, the proposed method’s effectiveness is demonstrated through the validation of two simulation examples.