Stochastic bounded consensus tracking of leader-follower multi-agent systems with measurement noises based on sampled data with general sampling delay

In this paper we provide a unified framework for consensus tracking of leader-follower multi-agent systems with measurement noises based on sampled data with a general sampling delay. First, a stochastic bounded consensus tracking protocol based on sampled data with a general sampling delay is presented by employing the delay decomposition technique. Then, necessary and sufficient conditions are derived for guaranteeing leader-follower multi-agent systems with measurement noises and a time-varying reference state to achieve mean square bounded consensus tracking. The obtained results cover no sampling delay, a small sampling delay and a large sampling delay as three special cases. Last, simulations are provided to demonstrate the effectiveness of the theoretical results.

Active Disturbance Rejection Control for Uncertain Nonlinear Systems With Sporadic Measurements

This paper deals with the problem of active disturbance rejection control(ADRC)design for a class of uncertain nonlinear systems with sporadic measurements.A novel extended state observer(ESO)is designed in a cascade form consisting of a continuous time estimator,a continuous observation error predictor,and a reset compensator.The proposed ESO estimates not only the system state but also the total uncertainty,which may include the effects of the external perturbation,the parametric uncertainty,and the unknown nonlinear dynamics.Such a reset compensator,whose state is reset to zero whenever a new measurement arrives,is used to calibrate the predictor.Due to the cascade structure,the resulting error dynamics system is presented in a non-hybrid form,and accordingly,analyzed in a general sampled-data system framework.Based on the output of the ESO,a continuous ADRC law is then developed.The convergence of the resulting closed-loop system is proved under given conditions.Two numerical simulations demonstrate the effectiveness of the proposed control method.

Modeling of Non-uniformly Sampled Systems by Support Vector Regression with Applications

This paper presents a modeling method for a non-uniformly sampled system bused on support vector regression （ SVR ）. First, a lifted discrete-time state-space model for a non-uniformly sampled system is derived by using the lifting technique to reduce the modeling difficulty caused by multirate sampling. Then, the system is divided into several parallel subsystems and their input-output model is presented to satisfy the SVR model. Finally, an on-line SVR technique is utilized to establish the models of all subsystems to deal with uncertainty. Furthermore, the presented method is applied in a multichannel electrohydraulic force servo synchronous loading system to predict the system outputs over the control sample interval and the prediction mean absolute percentage error reaches 0. 092%. The results demonstrate that the presented method has a high modeling precision and the subsystems have the same level of prediction error.

Fuzzy Modeling of Non-Uniformly Sampling Nonlinear Systems Based on Clustering Method and Convergence Analysis

The studying motivation of this paper is that there exist many modeling issues of nonuniformly sampling nonlinear systems in industrial systems.Based on multi-model modeling principle,the corresponding model of non-uniformly sampling nonlinear systems is described by the nonlinear weighted combination of some linear models at local working points.Fuzzy modeling based on multimodel scheme is a common method to describe the dynamic process of non-linear systems.In this paper,the fuzzy modeling method of non-uniformly sampling nonlinear systems is studied.The premise structure of the fuzzy model is confirmed by GK fuzzy clustering,and the conclusion parameters of the fuzzy model are estimated by the recursive least squared algorithm.The convergence perfromance of the proposed identification algorithm is given by using lemmas and martingale theorem.Finally,the simulation example is given to demonstrate the effectiveness of the proposed method.

Process modeling and optimizing control based on sparse nonuniformly sampled data

In this paper, a process modeling and related optimizing control for nonuniformly sampled （NUS） systems are addressed. By using a proposed nonuniform integration filter and subspace method estimation, an identification method of NUS systems is developed, based on which either an output soft sensor or a hidden state estimator is developed. The optimizing control is implemented by replacing the sparsely-mea- sured/immeasurable variable with the estimated one. Examples of optimizing control problem are given. The proposed optimizing control strategy in the simulation examples is verified to be very effeetive.

Stability analysis and stabilization of networked linear systems with random packet losses

This paper is concerned with the stability analysis and stabilization of networked discrete-time and sampled-data linear systems with random packet losses. Asymptotic stability, mean-square stability, and stochastic stability are considered. For networked discrete-time linear systems, the packet loss period is assumed to be a finite-state Markov chain. We establish that the mean-square stability of a related discrete-time system which evolves in random time implies the mean-square stability of the system in deterministic time by using the equivalence of stability properties of Markovian jump linear systems in random time. We also establish the equivalence of asymptotic stability for the systems in deterministic discrete time and in random time. For networked sampled-data systems, a binary Markov chain is used to characterize the packet loss phenomenon of the network. In this case, the packet loss period between two transmission instants is driven by an identically independently distributed sequence assuming any positive values. Two approaches, namely the Markov jump linear system approach and randomly sampled system approach, are introduced. Based on the stability results derived, we present methods for stabilization of networked sampled-data systems in terms of matrix inequalities. Numerical examples are given to illustrate the design methods of stabilizing controllers.

Elements’important ranking of China drug safety management system:applying the non-structural fuzzy group decision method

Drug safety management is an important issue in China drug management system and attracts great attentions from the whole society.In order to reduce drug incident,this study discusses some important elements associated with China drug safety management system and analyzes the data collected by questionnaires.Besides,a methodology for rating the important elements is described and applied.The non-structural fuzzy group decision method not only considers the insufficient precise information but also combines the opinions of different kinds of respondents in China’s four municipalities.The results indicate that the sample systems are the most important in these important elements,and the order of importance is sampling systems,licensing systems,traceability systems,transaction models,pharmacovigilance and emergence management.This study not only points out the important ranking of the pivotal elements in China drug safety management but also gives some specific proposals about how to enhance drug safety management in China.

Maternal and neonatal outcomes in multiple pregnancy: A multicentre study in the Beijing population

Objective:To compare the adverse maternal and neonatal outcomes of multiple pregnancy and singleton pregnancy from multiple medical centers in Beijing.Methods:Data concerning maternal and neonatal adverse outcomes in multiple and singleton pregnancies were collected from 15 hospitals in Beijing by a systemic cluster sampling survey conducted from 20 June to 30 November 2013.The SPSS software (version 20.0) was used for data analysis.The x2 test was used tbr statistical analyses.Results:The rate of caesarean deliveries was much higher in women with multiple pregnancies (85.8％) than that in women with singleton pregnancies (42.6％,X2 =190.8,P ＜ 0.001).The incidences of anemia (X2 =40.023,P ＜ 0.001),preterm labor (X2 =1021.172,P ＜ 0.001),gestational diabetes mellitus (X2 =9.311,P ＜ 0.01),hypertensive disorders (X2 =122.708,P ＜ 0.001)and post-partum hemorrhage (X2-48.550,P ＜ 0.001) was significantly increased with multiple pregnancy.In addition,multiple pregnancy was associated with a significantly higher rate of small-for-gestational-age infants (X2 =92.602,P ＜ 0.001),low birth weight (X2 =1141.713,P ＜ 0.001),and neonatal intensive care unit (NICU) admission (X2 =340.129,P＜ 0.001).Conclusions:Multiple pregnancy is a significant risk factor for adverse maternal and neonatal outcomes in Beijing.Improving obstetric care for multiple pregnancy,particularly in reducing preterm labor,is required to reduce the risk to mothers and infants.