design of decentralised adaptive sliding mode controllers for large scale systems with mismatched perturbations based on the lyapunov stability theorem a methodology for designing a decentralised adaptive sliding mode control scheme is proposed in this paper this scheme is implemented for a class of large scale systems with both matched and mismatched perturbations the perturbations and the interconnection terms are assumed to be norm bounded under certain mild conditions the decentralised sliding surfaces with adaptive mechanisms embedded are specially designed for each subsystem so that when each subsystem enters the sliding mode the mismatched perturbations and the effects of interconnections can be effectively overcome and achieve asymptotic stability the decentralised controller with embedded adaptive mechanisms is capable of driving the controlled state trajectories into the designated sliding surface in finite time this is also achieved without the knowledge of upper bounds of the perturbations except those of the uncertainties in the input channels a numerical example is included to demonstrate the feasibility of the proposed control scheme

multiple imputation in mixture models for nonignorable nonresponse with follow ups abstract one approach to inference for means or linear regression parameters when the outcome is subject to nonignorable nonresponse is mixture modeling mixture models assume separate parameters for respondents and nonrespondents implementation by multiple imputation consists of repeatedly filling in missing values for nonrespondents estimating parameters using the filled in data and then adjusting for variability between imputations we evaluated the performance of this scheme using simulated data with a 25 sample of nonrespondents followed up we conclude that it provides a generally satisfactory and robust approach to inference for means and regression parameters in this case although a greater number of imputations may be required for good performance compared to the number required for estimation when nonresponse is ignorable

generalization of gao s reaching law for higher relative degree sliding variables in this paper a generalized discrete time reaching law for sliding variables with arbitrary relative degree is proposed the reaching law is based on gao s strategy and ensures higher order switching type sliding motion which is a novel concept in the field of discrete time plants the controller synthesis process is simplified by performing a specific system state transformation it is demonstrated that the width of the layer to which the representative point of the system is driven decreases as the relative degree of the sliding variable gets bigger it has further been proven that achieving a narrower quasi sliding mode band width is reflected in reduced state error finally the reaching phase elimination for variables with arbitrary relative degree is considered this is obtained by implementing a time varying sliding surface in a finite number of initial time instants uniform ultimate boundedness of all state variables is guaranteed

design of continuous twisting algorithm abstract for the double integrator with matched lipschitz disturbances we propose a continuous homogeneous controller providing finite time stability of the origin the disturbance is compensated exactly in finite time using a discontinuous function through an integral action since the controller is dynamic the closed loop is a third order system that achieves a third order sliding mode in the steady state the stability and robustness properties of the controller are proven using a smooth and homogeneous strict lyapunov function lf in a first stage the gains of the controller and the lf are designed using a method based on polya s theorem in a second stage the controller s gains are adjusted through a sum of squares representation of the lf

unknown input and state estimation for unobservable systems the concept of strong detectability and its relation with the concept of invariant zeros is reviewed for strongly detectable systems which includes the strongly observable systems it is proposed a hierarchical design of a robust observer whose trajectories converge to those of the original state vector furthermore it is shown that neither left invertibility is a sufficient condition nor strong detectability is a necessary condition to estimate the unknown inputs it is shown that the necessary and sufficient condition for estimating the unknown inputs is that the set of the invariant zeros that do not belong to the set of unobservable modes be within the interior of the left half plane of the complex space this shows that the unknown inputs could be estimated even if it is impossible to estimate the entire state vector of the system two numerical examples illustrate the effectiveness of the proposed estimation schemes

almost gorenstein homogeneous rings and their h vectors abstract in this paper for the development of the study of almost gorenstein graded rings we discuss some relations between almost gorensteinness of cohen macaulay homogeneous rings and their h vectors concretely for a cohen macaulay homogeneous ring r we give a sufficient condition for r to be almost gorenstein in terms of the h vector of r theorem 3 1 and we also characterize almost gorenstein homogeneous domains with small socle degrees in terms of the h vector of r theorem 4 4 moreover we also provide the examples of almost gorenstein homogeneous domains arising from lattice polytopes

sliding order and sliding accuracy in sliding mode control the synthesis of a control algorithm that stirs a nonlinear system to a given manifold and keeps it within this constraint is considered usually what is called sliding mode is employed in such synthesis this sliding mode is characterized in practice by a high frequency switching of the control it turns out that the deviation of the system from its prescribed constraints sliding accuracy is proportional to the switching time delay a new class of sliding modes and algorithms is presented and the concept of sliding mode order is introduced these algorithms feature a bounded control continuously depending on time with discontinuities only in the control derivative it is also shown that the sliding accuracy is proportional to the square of the switching time delay

almost gorenstein hibi rings abstract in this paper we state criteria of a hibi ring to be level non gorenstein and almost gorenstein and to be non level and almost gorenstein in terms of the structure of the partially ordered set defining the hibi ring we also state a criterion of a ladder determinantal ring defined by 2 minors to be non gorenstein and almost gorenstein in terms of the shape of the ladder

adaptive sliding mode control design for a class of uncertain singularly perturbed nonlinear systems this paper addresses adaptive sliding mode control asmc of uncertain singularly perturbed nonlinear uspn systems with guaranteed h control performance first we use takagi sugeno t s fuzzy model to construct the uspn systems then the sliding surface can be determined via linear matrix inequality lmi design procedure second we propose neural network nn based asmc design to stabilise the uspn systems the proposed methods are based on the lyapunov stability theorem the adaptive law can reduce the effect of uncertainty the proposed nn based asmc will stabilise the uspn systems for all 0 simulation result reveals that the proposed nn based asmc scheme has better convergence time compared with the fuzzy control scheme li t h s lin k j 2004 stabilization of singularly perturbed fuzzy systems ieee transactions on fuzzy systems 12 579 595

almost gorenstein rings abstract barucci and froberg 1997 2 introduced the notion of an almost gorenstein ring in the case of an analytically unramified local ring r m in this work we provide a framework that allows us to generalize this notion to include the analytically ramified case as a consequence for any cohen macaulay local ring r m of dimension one we solve in full generality the problem of determining when the endomorphism algebra m m is gorenstein we also provide characterizations for rings to be almost gorenstein in connection with the principle of idealization several examples are explored as well

analysis of change in the presence of informative censoring application to a longitudinal clinical trial of progressive renal disease the rate of change in a continuous variable measured serially over time is often used as an outcome in longitudinal studies or clinical trials when patients terminate the study before the scheduled end of the study there is a potential for bias in estimation of rate of change using standard methods which ignore the missing data mechanism these methods include the use of unweighted generalized estimating equations methods and likelihood based methods assuming an ignorable missing data mechanism we present a model for analysis of informatively censored data based on an extension of the two stage linear random effects model where each subject s random intercept and slope are allowed to be associated with an underlying time to event the joint distribution of the continuous responses and the time to event variable are then estimated via maximum likelihood using the em algorithm and using the bootstrap to calculate standard errors we illustrate this methodology and compare it to simpler approaches and usual maximum likelihood using data from a multi centre study of the effects of diet and blood pressure control on progression of renal disease the modification of diet in renal disease mdrd study sensitivity analyses and simulations are used to evaluate the performance of this methodology in the context of the mdrd data under various scenarios where the drop out mechanism is ignorable as well as non ignorable

missing covariates in generalized linear models when the missing data mechanism is non ignorable we propose a method for estimating parameters in generalized linear models with missing covariates and a non ignorable missing data mechanism we use a multinomial model for the missing data indicators and propose a joint distribution for them which can be written as a sequence of one dimensional conditional distributions with each one dimensional conditional distribution consisting of a logistic regression we allow the covariates to be either categorical or continuous the joint covariate distribution is also modelled via a sequence of one dimensional conditional distributions and the response variable is assumed to be completely observed we derive the e and m steps of the em algorithm with non ignorable missing covariate data for categorical covariates we derive a closed form expression for the e and m steps of the em algorithm for obtaining the maximum likelihood estimates mles for continuous covariates we use a monte carlo version of the em algorithm to obtain the mles via the gibbs sampler computational techniques for gibbs sampling are proposed and implemented the parametric form of the assumed missing data mechanism itself is not testable from the data and thus the non ignorable modelling considered here can be viewed as a sensitivity analysis concerning a more complicated model therefore although a model may have passed the tests for a certain missing data mechanism this does not mean that we have captured even approximately the correct missing data mechanism hence model checking for the missing data mechanism and sensitivity analyses play an important role in this problem and are discussed in detail several simulations are given to demonstrate the methodology in addition a real data set from a melanoma cancer clinical trial is presented to illustrate the methods proposed

estimating and using propensity scores with partially missing data abstract investigators in observational studies have no control over treatment assignment as a result large differences can exist between the treatment and control groups on observed covariates which can lead to badly biased estimates of treatment effects propensity score methods are an increasingly popular method for balancing the distribution of the covariates in the two groups to reduce this bias for example using matching or subclassification sometimes in combination with model based adjustment to estimate propensity scores which are the conditional probabilities of being treated given a vector of observed covariates we must model the distribution of the treatment indicator given these observed covariates much work has been done in the case where covariates are fully observed we address the problem of calculating propensity scores when covariates can have missing values in such cases which commonly arise in practice the pattern of missing covariates can be prognostically important and

pattern mixture models for multivariate incomplete data with covariates pattern mixture models stratify incomplete data by the pattern of missing values and formulate distinct models within each stratum pattern mixture models are developed for analyzing a random sample on continuous variables y 1 y 2 when values of y 2 are nonrandomly missing methods for scalar y 1 and y 2 are here generalized to vector y 1 and y 2 with additional fixed covariates x parameters in these models are identified by alternative assumptions about the missing data mechanism models may be underidentified in which case additional assumptions are needed just identified or overidentified maximum likelihood and bayesian methods are developed for the latter two situations using the em and sem algorithms direct and interactive simulation methods the methods are illustrated on a data set involving alternative dosage regimens for the treatment of schizophrenia using haloperidol and on a regression example sensitivity to alternative assumptions about the missing data mechanism is assessed and the new methods are compared with complete case analysis and maximum likelihood for a probit selection model

an improved second order sliding mode control scheme robust against the measurement noise in this note the second order sliding mode control problem is tackled by explicitly taking into account the presence of measurement error with unknown upper bound spl delta under sensible assumptions regarding the uncertain dynamics of a broad class of nonlinear plants a new switching controller is proposed guaranteeing a sliding accuracy of order o spl delta simulations highlight the robustness and good performance of the proposed approach and confirm the expected precision order

tracking control of the uncertain heat and wave equation via power fractional and sliding mode techniques in the present paper preliminary results towards the generalization to the infinite dimensional setting of some well known robust finite dimensional control algorithms are illustrated more specifically we deal with the tracking problem for some classes of linear uncertain infinite dimensional systems evolving in hilbert spaces we design distributed variable structure stabilizers that are shown to be effective in the presence of external disturbances the main focus of the present paper is on the rejection of nonvanishing external disturbances the generalization to the infinite dimensional setting of the well known finite dimensional controllers namely the power fractional controller s p bhat and d s bernstein siam j control optim 38 2000 pp 751 766 and two second order sliding mode control algorithms the twisting and supertwisting algorithms l fridman and a levant higher order sliding modes as a natural phenomenon in control theory in robust control via variable structure and lyapunov techniques springer verlag berlin 1996 pp 107 133 a levant internat j control 58 1993 pp 1247 1263 is the main contribution of the present investigation first the distributed twisting control algorithm is developed to address the asymptotic state tracking of the perturbed wave equation next the finite time state tracking of the unperturbed heat equation is provided by means of a distributed power fractional controller finally the distributed supertwisting controller is suggested to address the asymptotic state tracking of the heat equation in spite of the presence of persistent disturbances constructive proofs of stability are developed via the lyapunov functional technique which leads to simple tuning rules for the controller parameters simulation results are discussed to verify the effectiveness of the proposed schemes

measuring the non gorenstein locus of hibi rings and normal affine semigroup rings abstract the trace of the canonical module of a cohen macaulay ring describes its non gorenstein locus we study the trace of the canonical module of a segre product of algebras and we apply our results to compute the non gorenstein locus of toric rings we provide several sufficient and necessary conditions for hibi rings and normal semigroup rings to be gorenstein on the punctured spectrum

homogeneity approach to high order sliding mode design it is shown that a general uncertain single input single output regulation problem is solvable only by means of discontinuous control laws giving rise to the so called high order sliding modes the homogeneity properties of the corresponding controllers yield a number of practically important features in particular finite time convergence is proved and asymptotic accuracy is calculated in a very general way in the presence of input noises discrete measurements and switching delays a robust homogeneous differentiator is included in the control structure thus yielding robust output feedback controllers with finite time convergence it is demonstrated that homogeneity features significantly simplify the design and investigation of a new family of high order sliding mode controllers finally simulation results are presented

reaching law for dsmc systems with relative degree 2 switching variable abstractthis paper presents the first attempt to design a reaching law based discrete time sliding mode controller with a relative degree 2 switching variable the current value of this variable is only affected by the control signal and disturbance generated two time instants ago it is demonstrated that the new reaching law based strategy offers a smaller quasi sliding mode band width when compared to a similar control scheme with relative degree 1 switching variable this in turn leads to reduced system output error in the control system proposed in the paper

super twisting algorithm in presence of time and state dependent perturbations abstractin this paper generalized super twisting algorithm is applied to a class of systems whose perturbation and uncertain control coefficient are time and state dependent a non smooth strict lyapunov function is used to obtain the conditions for the global finite time stability and to estimate the time of convergence

models for nonresponse in sample surveys abstract the literature on the analysis of incomplete data using models is reviewed in the context of nonresponse in sample surveys the modeling approach provides a large body of methods for handling unit and item nonresponse some of which cannot be derived from the randomization theory of inference for surveys key concepts from the literature on incomplete data such as factorizations of the likelihood for special data patterns the em algorithm for general data patterns and ignorability of the response mechanism are discussed within the survey context model based procedures are related to common methods for handling nonresponse in surveys such as weighting or imputation of means within subclasses of the population

multiple imputation after 18 years abstract multiple imputation was designed to handle the problem of missing data in public use data bases where the data base constructor and the ultimate user are distinct entities the objective is valid frequency inference for ultimate users who in general have access only to complete data software and possess limited knowledge of specific reasons and models for nonresponse for this situation and objective i believe that multiple imputation by the data base constructor is the method of choice this article first provides a description of the assumed context and objectives and second reviews the multiple imputation framework and its standard results these preliminary discussions are especially important because some recent commentaries on multiple imputation have reflected either misunderstandings of the practical objectives of multiple imputation or misunderstandings of fundamental theoretical results then criticisms of multiple imputation are considered and finally comparisons are made to alt

reduced order observer in the sliding mode control of nonlinear nonaffine systems the note considers the variable structure control of nonlinear known nonaffine systems when the state vector is not completely available and the use of observers is required the strategy of introducing integrators in the input channel is exploited to enlarge the class of tractable control systems a new reduced order observer is proposed and conditions are found under which it is proven the convergence to the unique ideal solution of both system and observer the control problem is solved by forcing a sliding regime for the observer while satisfying an exponential stability criterion for the observation error state equation

second order sliding fuzzy interval type 2 control for an uncertain system with real application a new second order sliding mode type 2 fuzzy controller for nonlinear uncertain perturbed systems is developed in this paper to overcome the constraint on the knowledge of the system model we have used local models that are related to some operating points to synthesize a type 2 nominal fuzzy global model the control is based on the super twisting algorithm which is among second order sliding mode controls smcs moreover two adaptive fuzzy type 2 systems have been introduced to generate the two super twisting signals to avoid both the chattering and the constraint on the knowledge of disturbances and uncertainties upper bounds these adaptive fuzzy type 2 systems are adjusted online by two adaptation laws that are deduced from the stability analysis in the lyapunov sense it has only one input i e the sliding surface and one output i e the optimal values of the gains control which are hard to compute with the original algorithm many results of the one link manipulator are obtained first by the simulation in order to compare the performances of the proposed method with that given by levant and then in a real time application in order to confirm the efficiency of the proposed approach the experimentation and simulation are done for the tracking control problem

bivariate binary data analysis with nonignorably missing outcomes one of the objectives in the northern manhattan stroke study is to investigate the impact of stroke subtype on the functional status 2 years after the first ischemic stroke a challenge in this analysis is that the functional status at 2 years after stroke is not completely observed in this paper we propose a method to handle nonignorably missing binary functional status when the baseline value and the covariates are completely observed the proposed method consists of fitting four separate binary regression models for the baseline outcome the outcome 2 years after the stroke the product of the previous two and finally the missingness indicator we then conduct a sensitivity analysis by varying the assumptions about the third and the fourth binary regression models our method belongs to an imputation paradigm and can be an alternative to the weighting method of rotnitzky and robins 1997 statistics in medicine 16 81 102 a jackknife variance estimate is proposed for the variance of the resulting estimate the proposed analysis can be implemented using statistical software such as sas

sliding sector control using new equivalent sector control this paper presents a variable structure control design method for linear and nonlinear continuous time systems with a special type of sliding sector using a new equivalent sector control the pr s

adaptive sliding mode control convergence and gain boundedness revisited abstractthis paper reviews the main adaptive sliding mode controller asmc designs for nonlinear systems with finite uncertainties of unknown bounds different statements of convergence referring to uniformly ultimate boundedness uub asymptotic convergence ac and finite time convergence ftc for asmc shown in recent papers are analysed weaknesses and incomplete proofs apropos ftc are pointed out thereafter a new approach is proposed to successfully demonstrate ftc of the so called sliding variable we identify a compensating phase and a reaching phase during the asmc process a new explicit form for estimating the upper bound reaching time is provided for any bounded perturbation an amended form of the real asmc is recalled showing improved accuracy and chattering reduction finally numerical and experimental applications are performed to convey the discussed results

control of nonlinear variable structure systems etude des systemes de commande decrits par des equations d etat extension de la theorie des systemes de commande discontinue a structure variable a quelques cas non lineaires

exponential stabilization of the uncertain wave equation via distributed dynamic input extension the wave equation is studied under the effect of a persistent external perturbation a dynamical distributed controller is suggested based on an infinite dimensional generalization of second order sliding mode control techniques that provides the exponential attainment of a sufficiently smooth arbitrary reference of the state trajectory the control system comprises of both feedforward and feedback parts the latter being a discontinuous term directly connected to the plant input through a dynamical filter that augments the system state smoothing out the discontinuity of the feedback control loop as a result a continuous input is applied to the plant a constructive lyapunov based proof of convergence of the proposed control algorithm is carried out and supporting numerical results are presented

multiple imputation for interval estimation from simple random samples with ignorable nonresponse abstract several multiple imputation techniques are described for simple random samples with ignorable nonresponse on a scalar outcome variable the methods are compared using both analytic and monte carlo results concerning coverages of the resulting intervals for the population mean using m 2 imputations per missing value gives accurate coverages in common cases and is clearly superior to single imputation m 1 in all cases the performances of the methods for various m can be predicted well by linear interpolation in 1 m 1 between the results for m 2 and m as a rough guide to assure coverages of interval estimates within 2 of the nominal level when using the preferred methods the number of imputations per missing value should increase from 2 to 3 as the nonresponse rate increases from 10 to 60

higher order super twisting for perturbed chains of integrators in this paper we present a generalization of the super twisting algorithm for perturbed chains of integrators of arbitrary order this higher order super twisting host controller is homogeneous with respect to a family of dilations and is continuous it is built as a dynamic controller with respect to the state variable of the chain of integrators and the convergence analysis is performed by the use of a homogeneous strict lyapunov function which is explicitly constructed the effectiveness of the controller is finally illustrated with simulations for a chain of integrators of order four first pure then perturbed where we compare the performances of two host controllers

design of decentralised sliding surfaces for a class of large scale systems with mismatched perturbations a novel design methodology of a decentralised sliding surface is proposed in this article for a class of large scale systems with mismatched perturbations and interconnections the main idea of this method is that the sliding surface function is obtained through a specially designed state transformation matrix this transformation matrix which includes the sliding surface coefficient function contains adaptive mechanisms with the capability of adapting some unknown constants embedded in the least upper bounds of mismatched perturbations as well as interconnections so that the perturbations mismatched and matched can be effectively overcome and asymptotical stability is guaranteed for each subsystem in the sliding mode in addition each decentralised controller also contains adaptive mechanisms to assure that the sliding mode is generated in a finite time without requiring the knowledge of upper bounds of the perturbations and interconnections a practical example is also demonstrated by simulation f

partial and latent ignorability in missing data problems summary when an assumption of missing at random is untenable it becomes necessary to model missingdata indicators which carry information about the parameters of the complete data population within a given application however researchers may believe that some aspects of missingness are ignorable but others are not we argue that there are two different ways to formalize the notion that only part of the missingness is ignorable these approaches correspond to assumptions that we call partially missing at random and latently missing at random we explain these concepts and apply them in a latent class analysis of survey questions with item nonresponse

imputation of missing values when the probability of response depends on the variable being imputed abstract a method is developed for imputing missing values when the probability of response depends upon the variable being imputed the missing data problem is viewed as one of parameter estimation in a regression model with stochastic censoring of the dependent variable the prediction approach to imputation is used to solve this estimation problem wages and salaries are imputed to non respondents in the current population survey and the results are compared to the nonrespondents irs wage and salary data the stochastic censoring approach gives improved results relative to a prediction approach that ignores the response mechanism

parameter estimation from incomplete data in binomial regression when the missing data mechanism is nonignorable we propose a method for estimating parameters in binomial regression models when the response variable is missing and the missing data mechanism is nonignorable we assume throughout that the covariates are fully observed using a logit model for the missing data mechanism we show how parameter estimation can be accomplished using the em algorithm by the method of weights proposed in ibrahim 1990 journal of the american statistical association 85 765 769 an example from the six cities study ware et al 1984 american review of respiratory diseases 129 366 374 is presented to illustrate the method

brief digital second order sliding mode control for uncertain nonlinear systems in this note we analyze the discrete time implementation of a second order sliding mode control 2 smc scheme the treatment is detailed for a simple class of feedback linearizable nonlinear systems expressed in the brunowsky normal form first it is shown that the direct discretization of a continuous time 2 smc scheme guarantees the finite time attainment of a motion in an o t 2 boundary layer of the sliding manifold t being the sampling period then a suitable iterative learning procedure that leads to the asymptotic reduction of the boundary layer to o t 3 is proposed simulation results are reported at the end of the paper

weighted estimating equations with nonignorably missing response data we propose weighted estimating equations for data with nonignorable nonresponse in order to reduce the bias that can occur with a complete case analysis a survey concerning medical practice guidelines malpractice litigation and settlement provides the framework the survey was sent to recipients in two waves those who responded on the first or second wave are used to estimate a nonignorable nonresponse model while the fraction of recipients who never responded is used to allow the percentage of missing data to change with each wave we use the structure of the gee of liang and zeger 1986 biometrika 73 13 22 adding weights equal to the inverse probability of being observed we present simulations demonstrating the bias that can occur with an unweighted analysis and use the survey data to illustrate the methods

modeling zero inflated count data using a covariate dependent random effect model in various medical related researches excessive zeros which make the standard poisson regression model inadequate often exist in count data we proposed a covariate dependent random effect model to accommodate the excess zeros and the heterogeneity in the population simultaneously this work is motivated by a data set from a survey on the dental health status of hong kong preschool children where the response variable is the number of decayed missing or filled teeth the random effect has a sound biological interpretation as the overall oral health status or other personal qualities of an individual child that is unobserved and unable to be quantified easily the overall measure of oral health status responsible for accommodating the excessive zeros and also the heterogeneity among the children is covariate dependent this covariate dependent random effect model allows one to distinguish whether a potential covariate has an effect on the conceived overall oral health condition of the children that is the random effect or has a direct effect on the magnitude of the counts or both we proposed a multiple imputation approach for estimation of the parameters we discussed the choice of the imputation size we evaluated the performance of the proposed estimation method through simulation studies and we applied the model and method to the dental data copyright 2012 john wiley sons ltd

singular perturbations and time scale methods in control theory survey 1976 1983 recent progress in the use of singular perturbation and two time scale methods of modeling and design for control systems is reviewed over 350 references are organized into major problem areas representative issues and results are discussed with a view to outlining research directions and indicating potential areas of application the survey is aimed at engineers and applied mathematicians interested in model order reduction separation of time scales and allied simplified methods of control system analysis and design the exposition does not assume prior knowledge of singular perturbation methods