Exponential synchronization of chaotic Lur'e systems with time-varying delay via sampled-data control

In this paper, we study the exponential synchronization of chaotic Lur＇e systems with time-varying delays via sampled-data control by using sector nonlinearties. In order to make full use of information about sampling intervals and interval time-varying delays, new Lyapunov-Krasovskii functionals with triple integral terms are introduced. Based on the convex combination technique, two kinds of synchronization criteria are derived in terms of linear matrix inequal- ities, which can be efficiently solved via standard numerical software. Finally, three numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed results.

Stochastic asymptotical synchronization of chaotic Markovian jumping fuzzy cellular neural networks with mixed delays and the Wiener process based on sampled-data control

We investigate the stochastic asymptotical synchronization of chaotic Markovian jumping fuzzy cellular neural networks （MJFCNNs） with discrete, unbounded distributed delays, and the Wiener process based on sampled-data control using the linear matrix inequality （LMI） approach. The Lyapunov–Krasovskii functional combined with the input delay approach as well as the free-weighting matrix approach is employed to derive several sufficient criteria in terms of LMIs to ensure that the delayed MJFCNNs with the Wiener process is stochastic asymptotical synchronous. Restrictions （e.g., time derivative is smaller than one） are removed to obtain a proposed sampled-data controller. Finally, a numerical example is provided to demonstrate the reliability of the derived results.

Fault tolerant synchronization of chaotic systems based on T-S fuzzy model with fuzzy sampled-data controller

In this paper the fault tolerant synchronization of two chaotic systems based on fuzzy model and sample data is investigated. The problem of fault tolerant synchronization is formulated to study the global asymptotical stability of the error system with the fuzzy sampled-data controller which contains a state feedback controller and a fault compensator. The synchronization can be achieved no matter whether the fault occurs or not. To investigate the stability of the error system and facilitate the design of the fuzzy sampled-data controller, a Takagi Sugeno （T-S） fuzzy model is employed to represent the chaotic system dynamics. To acquire good performance and produce a less conservative analysis result, a new parameter-dependent Lyapunov-Krasovksii functional and a relaxed stabilization technique are considered. The stability conditions based on linear matrix inequality are obtained to achieve the fault tolerant synchronization of the chaotic systems. Finally, a numerical simulation is shown to verify the results.

Free-matrix-based time-dependent discontinuous Lyapunov functional for synchronization of delayed neural networks with sampled-data control

This paper is concerned with the synchronization of delayed neural networks via sampled-data control. A new technique, namely, the free-matrix-based time-dependent discontinuous Lyapunov functional approach, is adopted in constructing the Lyapunov functional, which takes advantage of the sampling characteristic of sawtooth input delay. Based on this discontinuous Lyapunov functional, some less conservative synchronization criteria are established to ensure that the slave system is synchronous with the master system. The desired sampled-data controller can be obtained through the use of the linear matrix inequality（LMI） technique. Finally, two numerical examples are provided to demonstrate the effectiveness and the improvements of the proposed methods.

Overview of multirate sampled-data control system

The basic analysis and synthesis approaches for multirate sampled-data control system are reviewed. After giving the definition and some properties of multirate system are given, its origination, development and design methods are discussed in detail. Finally, some remarks, expectations and conclusions on the present research status and the research directions are given.

Data Driven Vibration Control:A Review

With the ongoing advancements in sensor networks and data acquisition technologies across various systems like manufacturing,aviation,and healthcare,the data driven vibration control(DDVC)has attracted broad interests from both the industrial and academic communities.Input shaping(IS),as a simple and effective feedforward method,is greatly demanded in DDVC methods.It convolves the desired input command with impulse sequence without requiring parametric dynamics and the closed-loop system structure,thereby suppressing the residual vibration separately.Based on a thorough investigation into the state-of-the-art DDVC methods,this survey has made the following efforts:1)Introducing the IS theory and typical input shapers;2)Categorizing recent progress of DDVC methods;3)Summarizing commonly adopted metrics for DDVC;and 4)Discussing the engineering applications and future trends of DDVC.By doing so,this study provides a systematic and comprehensive overview of existing DDVC methods from designing to optimizing perspectives,aiming at promoting future research regarding this emerging and vital issue.

Synchronization of nonlinear multi-agent systems using a non-fragile sampled data control approach and its application to circuit systems

The main aim of this work is to design a non-fragile sampled data control(NFSDC) scheme for the asymptotic synchronization criteria for interconnected coupled circuit systems(multi-agent systems, MASs). NFSDC is used to conduct synchronization analysis of the considered MASs in the presence of time-varying delays. By constructing suitable Lyapunov functions, sufficient conditions are derived in terms of linear matrix inequalities(LMIs) to ensure synchronization between the MAS leader and follower systems. Finally, two numerical examples are given to show the effectiveness of the proposed control scheme and less conservation of the proposed Lyapunov functions.

A call for enhanced data-driven insights into wind energy flow physics

With the increased availability of experimental measurements aiming at probing wind resources and wind turbine operations,machine learning(ML)models are poised to advance our understanding of the physics underpinning the interaction between the atmospheric boundary layer and wind turbine arrays,the generated wakes and their interactions,and wind energy harvesting.However,the majority of the existing ML models for predicting wind turbine wakes merely recreate Computational fluid dynamics(CFD)simulated data with analogous accuracy but reduced computational costs,thus providing surrogate models rather than enhanced data-enabled physics insights.Although ML-based surrogate models are useful to overcome current limitations associated with the high computational costs of CFD models,using ML to unveil processes from experimental data or enhance modeling capabilities is deemed a potential research direction to pursue.In this letter,we discuss recent achievements in the realm of ML modeling of wind turbine wakes and operations,along with new promising research strategies.

Enhancing Patients Outcomes and Infection Control through Smart Indoor Air Quality Monitoring Systems

Air pollution poses a critical threat to public health and environmental sustainability globally, and Nigeria is no exception. Despite significant economic growth and urban development, Nigeria faces substantial air quality challenges, particularly in urban centers. While outdoor air pollution has received considerable attention, the issue of indoor air quality remains underexplored yet equally critical. This study aims to develop a reliable, cost-effective, and user-friendly solution for continuous monitoring and reporting of indoor air quality, accessible from anywhere via a web interface. Addressing the urgent need for effective indoor air quality monitoring in urban hospitals, the research focuses on designing and implementing a smart indoor air quality monitoring system using Arduino technology. Employing an Arduino Uno, ESP8266 Wi-Fi module, and MQ135 gas sensor, the system collects real-time air quality data, transmits it to the ThingSpeak cloud platform, and visualizes it through a user-friendly web interface. This project offers a cost-effective, portable, and reliable solution for monitoring indoor air quality, aiming to mitigate health risks and promote a healthier living environment.

Data-Driven Model Identification and Control of the Inertial Systems

In the synthesis of the control algorithm for complex systems, we are often faced with imprecise or unknown mathematical models of the dynamical systems, or even with problems in finding a mathematical model of the system in the open loop. To tackle these difficulties, an approach of data-driven model identification and control algorithm design based on the maximum stability degree criterion is proposed in this paper. The data-driven model identification procedure supposes the finding of the mathematical model of the system based on the undamped transient response of the closed-loop system. The system is approximated with the inertial model, where the coefficients are calculated based on the values of the critical transfer coefficient, oscillation amplitude and period of the underdamped response of the closed-loop system. The data driven control design supposes that the tuning parameters of the controller are calculated based on the parameters obtained from the previous step of system identification and there are presented the expressions for the calculation of the tuning parameters. The obtained results of data-driven model identification and algorithm for synthesis the controller were verified by computer simulation.

基于ControlVAE的约束嵌入稠密时变阵列构建

在对探地雷达(Ground Penetrating Radar,GPR)数据进行三维建模与可视化分析过程中,其所依赖的三维时变阵列通常是由一维数据道或二维阵列间接生成的.由于采集到的数据往往比较稀疏,且存在不规则偏移,需要进行稠密处理,以获得高密度三维时变阵列.本文提出了一种基于可控变分自编码(Controllable Variational AutoEncoder,ControlVAE)的约束嵌入稠密时变阵列构建方法 .使用基于ControlVAE的时变数据重构网络,通过隐空间采样插值和深浅层特征信息的融合,生成接近真实分布的伪道数据来增加阵列密度.构建了基于尺度不变特征变换的数据配准模块,提取道间梯度特征与结构对称性特征,可以在时空域内完成数据配准.同时将浅层特征作为约束信息嵌入数据重构网络,以消除数据道偏移对阵列生成的影响.实验结果表明,本文方法仅使用单测线二维B-scan数据集即可重建稠密时变阵列,可以有效降低重构模型累积误差,提高面对复杂真实数据时的鲁棒性.

FLOATING QUANTIZATION EFFECTS ON MULTIRATE SAMPLED-DATA NONLINEAR CONTROL SYSTEMS

In this article, floating quantization effects on multirate sampled-data control systems are studied. It shows that the solutions of multirate digital feedback control systems with nonlinear plant and with floating quantization in the controller are uniformly ultimately bounded if the associated linear systems consisting of linearization of the plant and controller with no quantization are Schur stable. Moreover, it also shows that the difference between the response of multirate digital controllers without quantizers and the same plant with floating quantization in the controllers can be made as small as desired by selecting proper quantization level.

Robust Fuzzy Sampled-Data Control for Dynamic Positioning Ships

A robust H∞sampled-data stabilization problem for nonlinear dynamic positioning(DP) ships with Takagi-Sugeno(T-S) fuzzy models is discussed in this paper. Input delay approach is used to convert the sampleddata DP ship system to a fuzzy system with time-varying delay. Adequate conditions are derived to determine the system's asymptotical stability and achieve H∞performance via Lyapunov stability theorems. Then, the fuzzy sampled-data controller is obtained by analyzing the stabilization condition. Simulation result shows that the proposed method and the designed controller for a DP ship are effective so that the DP ship can maintain the desired position, heading and velocities in the existence of varying environment disturbances.

Random Stabilization of Sampled-data Control Systems with Nonuniform Sampling

For a sampled-data control system with nonuniform sampling, the sampling interval sequence, which is continuously distributed in a given interval, is described as a multiple independent and identically distributed (i.i.d.) process. With this process, the closed-loop system is transformed into an asynchronous dynamical impulsive model with input delays. Sufficient conditions for the closed-loop mean-square exponential stability are presented in terms of linear matrix inequalities (LMIs), in which the relation between the nonuniform sampling and the mean-square exponential stability of the closed-loop system is explicitly established. Based on the stability conditions, the controller design method is given, which is further formulated as a convex optimization problem with LMI constraints. Numerical examples and experiment results are given to show the effectiveness and the advantages of the theoretical results.

Blockchain-based data transmission control for Tactical Data Link

Tactical Data Link(TDL)is a communication system that utilizes a particular message format and a protocol to transmit data via wireless channels in an instant,automatic,and secure way.So far,TDL has shown its excellence in military applications.Current TDL adopts a distributed architecture to enhance anti-destruction capacity.However,It still faces a problem of data inconsistency and thus cannot well support cooperation across multiple militarily domains.To tackle this problem,we propose to leverage blockchain to build an automatic and adaptive data transmission control scheme for TDL.It achieves automatic data transmission and realizes information consistency among different TDL entities.Besides,applying smart contracts based on blockchain further enables adjusting data transmission policies automatically.Security analysis and experimental results based on simulations illustrate the effectiveness and efficiency of our proposed scheme.

Separate Dimming Controlling and Data Transmission for an Indoor Visible Light Communication System

Simultaneous dimming controlling and data transmission are usually required in a white LED based indoor visible light communication system.However,the diming controlling of LED normally interferes the data transmission due to the modulation nonlinearity of LED.In order to solve this problem,a scheme by separating the LEDs for the functions of dimming control and data transmission respectively is proposed in this paper.In the scheme,the LEDs used for dimming control function are driven by a dc amplified circuit,and the LEDs for data transmission are driven by a digital modulation circuit respectively.In this way,the modulation distortion to the data signal caused by the modulation nonlinearity can be avoided even if the dimming is at high level dc driven current.The proof-of-concept experiment of a 2.5Mbit/s visible light communication system demonstrates that the dimming controlling and data transmission can be realized simultaneously in a simple way,and the data transmission is not affected by the dimming controlling function.Compared to previous methods,the scheme in this paper is simpler and cost effective,and makes sense when high rate data is transmitted in a visible light communication system.

DEVELOPMENT OF A DATA MINING METHOD FOR LAND CONTROL

Land resources are facing crises of being misused,especially for an intersection area between town and country,and land control has to be enforced.This paper presents a development of data mining method for land control.A vector_match method for the prerequisite of data mining i.e., data cleaning is proposed,which deals with both character and numeric data via vectorizing character_string and matching number.A minimal decision algorithm of rough set is used to discover the knowledge hidden in the data warehouse.In order to monitor land use dynamically and accurately,it is suggested to set up a real_time land control system based on GPS,digital photogrammetry and online data mining.Finally,the means is applied in the intersection area between town and country of Wuhan city,and a set of knowledge about land control is discovered.

Sea surface temperature data from coastal observation stations:quality control and semidiurnal characteristics

Sea surface temperature(SST)data obtained from coastal stations in Jiangsu,China during 20102014 are quality controlled before analysis of their characteristic semidiurnal and seasonal cycles,including the correlation with the variation of the tide.Quality control of data includes the validation of extreme values and checking of hourly values based on temporally adjacent data points,with 0.15℃/h considered a suitable threshold for detecting abnormal values.The diurnal variation amplitude of the SST data is greater in spring and summer than in autumn and winter.The diurnal variation of SST has bimodal structure on most days,i.e.,SST has a significant semidiurnal cycle.Moreover,the semidiurnal cycle of SST is negatively correlated with the tidal data from March to August,but positively correlated with the tidal data from October to January.Little correlation is detected in the remaining months because of the weak coastal offshore SST gradients.The quality control and understanding of coastal SST data are particularly relevant with regard to the validation of indirect measurements such as satellite-derived data.

A PRELIMINARY STUDY ON THE QUALITY CONTROL METHOD FOR GUANGDONG GPS/PWV DATA AND ITS EFFECTS ON PRECIPITATION FORECASTS IN ITS ANNUALLY FIRST RAINING SEASON

We first analyzed GPS precipitable water vapor(GPS/PWV) available from a ground-based GPS observation network in Guangdong from 1 August 2009 to 27 August 2012 and then developed a method of quality control before GPS/PWV data is assimilated into the GRAPES 3DVAR system. This method can reject the outliers effectively. After establishing the criterion for quality control, we did three numerical experiments to investigate the impact on the precipitation forecast with and without the quality-controlled GPS/PWV data before they are assimilated into the system.In the numerical experiments, two precipitation cases(on 6 to 7 May, 2010 and 27 to 28 April, 2012 respectively) that occurred in the annually first raining season of Guangdong were selected. The results indicated that after quality control,only the GPS/PWV data that deviates little from the NCEP/PWV data can be assimilated into the system, has reasonable adjustment of the initial water vapor above Guangdong, and eventually improves the intensity and location of 24-h precipitation forecast significantly.