reliable asynchronous sampled data filtering of t s fuzzy uncertain delayed neural networks with stochastic switched topologies abstract this paper investigates the issue of the reliable asynchronous sampled data filtering of takagi sugeno t s fuzzy delayed neural networks with stochastic intermittent faults randomly occurring time varying parameters uncertainties and controller gain fluctuation the asynchronous phenomenon occurs between the system modes and controller modes first in order to reduce the utilization rate of communication bandwidth a novel alterable sampled data terminal method is considered via variable sampling rates second based on the fuzzy model based control approach improved reciprocally convex inequality and new parameter time dependent discontinuous lyapunov approach several relaxed conditions are derived and compared with the existing work third the intermittent fault tolerance scheme is also taken into fully account in designing a reliable asynchronous sampled data controller which ensures such that the resultant neural networks is asymptotically stable finally two numerical examples are presented to illustrate the effectiveness and advantages of the theoretical results

state estimation of t s fuzzy delayed neural networks with markovian jumping parameters using sampled data control abstract in this paper we are concerned with the problem of state estimation of takagi sugeno t s fuzzy delayed neural networks with markovian jumping parameters via sampled data control based on the fuzzy model based control approach and linear matrix inequality lmi technique several novel conditions are derived to guarantee the stability of the suggested system a new class of lyapunov functional which contains integral terms is constructed to derive delay dependent stability criteria some characteristics of the sampling input delay are proposed based on the input delay approach numerical examples are given to illustrate the usefulness and effectiveness of the proposed theoretical results

robust adaptive control in the presence of bounded disturbances the model reference adaptive control of a linear plant subjected to bounded disturbances is considered by analyzing a set of nonlinear differential equations it is shown that the global behavior of the adaptive system depends upon the persistent excitation of the reference input as well as the amplitude of the external disturbances the principal contribution of the paper is the derivation of sufficient conditions on the persistent excitation of the reference input given the maximum amplitude of disturbance for the signals in the adaptive system to be globally bounded

singularity free adaptive pole placement using periodic controllers a globally convergent adaptive control scheme for nonminimum phase systems is presented the approach is based on a particular periodic pole placement controller an ad hoc parameter estimate correction procedure is used to prevent any possible singularities in the control law computation furthermore a lower bound on the estimated model controllability is ensured the a priori knowledge on the system is reduced to the process order

finite dimensional adaptive observers applied to distributed parameter systems the application of finite dimensional adaptive observers to distributed parameter systems is addressed ultimate boundedness of the parameter and state errors are proved in the case of residuals which have finite energy over a finite time interval provided the plant input is definitely exciting in the absence of definite excitation an error growth rate of proportional to t sup 1 2 is proved the results are obtained without assuming plant stability or input boundedness

interval type 2 fuzzy sampled data control of time delay systems abstract the problem of interval type 2 it2 fuzzy sampled data stabilization is addressed for nonlinear continuous time systems with time varying delay and parameter uncertainties the input delay approach together with free weighting and slack matrices is utilized to enforce the stability analysis and a sufficient condition is formulated based on the condition an it2 fuzzy sampled data controller is developed such that the closed loop system is asymptotically stable illustrative examples are used to manifest the merits of this design

a robust direct adaptive controller this paper proposes a new direct adaptive control algorithm which is robust with respect to additive and multiplicative plant unmodeled dynamics the algorithm is designed based on the reduced order plant which is assumed to be minimum phase and of known order and relative degree but is analyzed with respect to the overall plant which due to the unmodeled dynamics may be nonminimum phase and of unknown order and relative degree it is shown that if the unmodeled dynamics are sufficiently small in the low frequency range then the algorithm guarantees boundedness of all signals in the adaptive loop and small residual tracking errors for any bounded initial conditions in the absence of unmodeled dynamics the residual tracking error is shown to be zero

discrete time stochastic adaptive control this paper establishes global convergence of a stochastic adaptive control algorithm for discrete time linear systems it is shown that with probability one the algorithm will ensure the system inputs and outputs are sample mean square bounded and the conditional mean square output tracking error achieves its global minimum possible value for linear feedback control thus asymptotically the adaptive control algorithm achieves the same performance as could be achieved if the system parameters were known

stable adaptive regulation of arbitrary nth order plants this paper presents an algorithm for adaptively stabilizing and asymptotically regulating an arbitrary single input single output linear time invariant plant which is controllable and observable of known order n and has unknown parameters no further assumptions are made no external probing signal is required

adaptive regulation of mimo linear discrete time systems without requiring a persistent excitation an adaptive regulator which does not require a persistent excitation is developed for multiinput multioutput mimo linear discrete time systems the assumptions on the plant are 1 an upper bound on the system dimension is known 2 the unknown system parameters belong to known bounded intervals 3 the plant is stabilizable for all possible values of the unknown system parameters the stabilizability condition can be tested before the system is put into operation a recursive parameter estimation scheme is given which forces the parameter estimates to converge to the known intervals using this estimator an adaptive regulator is derived which results in a globally stable closed loop system

sampled data state estimation for markovian jumping fuzzy cellular neural networks with mode dependent probabilistic time varying delays in this paper the problem of state estimation for markovian jumping fuzzy cellular neural networks fcnns using sampled data with mode dependent probabilistic time varying delays is investigated by developing a delay decomposition approach the information of the delayed states can be taken into full consideration by introducing a stochastic variable with a bernoulli distribution the information of probability distribution of the time varying delay is considered and transformed into one with deterministic time varying delay the main purpose of this paper is to estimate the neuron states through available output measurements such that the dynamics of the estimation error is globally asymptotically stable in the mean square based on the lyapunov krasovskii functional including triple integral terms and decomposed integral intervals delay distribution dependent stability criteria are obtained in terms of linear matrix inequalities lmis finally two numerical examples are given to illustrate the effectiveness of the proposed theoretical results

sufficiency of excitation in the context of adaptive identification and adaptive control of linear systems the concept of a sufficiently exciting input play a key role in this note necessary and sufficient conditions are presented for an input function of a single input linear stationary system to be sufficiently exciting these conditions are independent of the stability properties of the linear system

a robust indirect adaptive control approach this paper considers the robust design of an indirect adaptive control approach which is applicable when the unknown parameters of a linear time invariant plant lie in a known convex set throughout which no unstable pole zero cancellation occurs in order to achieve the robustness the use of a relative dead zone in the adaptive law is proposed it is shown that with a suitably designed relative dead zone the adaptive control system is globally stable even in the presence of small unmodeled plant uncertainties

robust redesign of adaptive control effects of unmodeled high frequency dynamics on stability and performance of adaptive control schemes are analyzed in the regulation problem global stability properties are no longer guaranteed but a region of attraction exists for exact adaptive regulation the dependence of the region of attraction on unmodeled parasitics is examined first then the general case of model reference adaptive control is considered in which parasitics can destroy stability and boundedness properties a modified adaptive law is proposed guaranteeing the existence of a region of attraction from which all signals converge to a residual set which contains the equilibrium for exact tracking the size of this set depends on design parameters the frequency range of parasitics and the reference input signal characteristics

stability analysis for neural networks with time varying delay based on quadratic convex combination in this paper a novel method is developed for the stability problem of a class of neural networks with time varying delay new delay dependent stability criteria in terms of linear matrix inequalities for recurrent neural networks with time varying delay are derived by the newly proposed augmented simple lyapunov krasovski functional different from previous results by using the first order convex combination property our derivation applies the idea of second order convex combination and the property of quadratic convex function which is given in the form of a lemma without resorting to jensen s inequality a numerical example is provided to verify the effectiveness and superiority of the presented results

interval type 2 fuzzy tracking control for nonlinear systems via sampled data controller abstract this paper addresses an interval type 2 it2 fuzzy sampled data tracking control problem of nonlinear systems subject to uncertain parameters the controlled plant is modeled as takagi sugeno t s fuzzy structure where the parameter uncertainties are captured effectively by the lower and upper membership functions by means of the linear matrix inequalities lmis and free weighting matrices an it2 fuzzy sampled data control scheme is developed such that the system states are driven to track those of the reference model finally an inverted pendulum system and a mass spring damper mechanical system are used to show the efficiency of the suggested tracking control design

further results on state estimation for neural networks of neutral type with time varying delay in this paper further result on design problem of state estimator for a class of neural networks of neutral type is presented a delay dependent linear matrix inequality lmi criterion for existence of the estimator is derived a numerical simulation is given to show the effectiveness of proposed estimator

fuzzy adaptive output feedback control of mimo nonlinear systems with partial tracking errors constrained in this paper a partial tracking error constrained fuzzy output feedback dynamic surface control dsc scheme is proposed for a class of uncertain multi input and multi output mimo nonlinear systems the considered mimo nonlinear systems contain unknown functions and without the requirement of their states being available for the controller design with the help of fuzzy logic systems identifying the mimo unknown nonlinear systems a fuzzy adaptive observer is established to estimate the unmeasured states by transforming the tracking errors into new virtual error variables and based on the dsc backstepping recursive design technique a new adaptive fuzzy output feedback control method is developed it is proved that the proposed control approach can guarantee that all the signals of the resulting closed loop system are bounded and the partial state tracking errors are confined all times within the prescribed bounds the simulation results and comparisons with the previous control approaches confirm the effectiveness and utility of the proposed scheme

brief paper on the stabilizability condition in indirect adaptive control many recent studies on overall stability of adaptive control systems require among other conditions that the identified model be stabilizable at least asymptotically in fact the usual identification methods do not guarantee this property in the passive approach without exciting extra signals or if the model order is subject to over estimation in order to solve this problem it is shown in this paper that certain classical identification algorithms can be easily modified in order to ensure the above stabilizability condition without affecting the other properties

robust model reference adaptive control we propose a new model reference adaptive control algorithm and show that it provides the robust stability of the resulting closed loop adaptive control system with respect to unmodeled plant uncertainties the robustness is achieved by using a relative error signal in combination with a dead zone and a projection in the adaptive law the extra a priori information needed to design the adaptive law are bounds on the plant parameters and an exponential bound on the impulse response of the inverse plant transfer function

brief paper robust sampled data h control with stochastic sampling in this paper the problem of robust h control is investigated for sampled data systems with probabilistic sampling the parameter uncertainties are time varying norm bounded and appear in both the state and input matrices for the simplicity of technical development only two different sampling periods are considered whose occurrence probabilities are given constants and satisfy bernoulli distribution which can be further extended to the case with multiple stochastic sampling periods by applying an input delay approach the probabilistic sampling system is transformed into a continuous time delay system with stochastic parameters in the system matrices by linear matrix inequality lmi approach sufficient conditions are obtained which guarantee the robust mean square exponential stability of the system with an h performance moreover an h controller design procedure is then proposed an illustrative example is included to demonstrate the effectiveness of the proposed techniques

reduced rank models for nutrition knowledge assessment the dependence of multiple interrelated responses on a set of covariates can be efficiently and parsimoniously modeled using reduced rank methods two forms of reduced rank models are possible depending on assumptions about response error correlations when the response outcomes are binary a minimum distance estimator based on first stage marginal probit regressions can be used to estimate the reduced rank models the alternative models and the estimation procedure are illustrated using a data set on the dietary fats and cholesterol knowledge of a sample of u s household meal planners

adaptive t s fuzzy control design for fractional order systems with parametric uncertainty and input constraint abstract thanks to the superiority of fractional differential equations in modeling high technology dynamical real world systems the design and control of fractional order systems have been widely investigated in recent decades on the other hand supposing that dynamic equations of the fractional order systems usually include various parameters and nonlinear terms takagi sugeno t s fuzzy models with if then rules can used to characterize the systems dynamics also the control community knows that there are usually some nonlinearities such as dead zone backlash hysteresis and saturation in control actuators which can decrease the ideal prescribed performance of the fractional systems the saturation nonlinearity is perhaps the most commonly existed one in control applications of fractional nonlinear systems that should be compensated by a proper method so this article proposes a novel adaptive t s fuzzy variable structure control technique for nonlinear fractional order systems in spite of the saturating input and control fluctuations the parameters of the fractional system as well as those of the saturation function are presumed to be unknown accordingly update rules are extracted to estimate such fluctuations then simple adaptive linear like control rules are developed according to the t s fuzzy control theory the stability of the origin equilibrium point of the final closed loop system is assured by lyapunov stability theory at last comparative computer simulations are presented to highlight the accuracy and applicability of the proposed adaptive fuzzy saturated control methodology

on the robust stability of a clarke gawthrop type of self tuning controller in this paper the clarke gawthrop type of self tuning controller is modified and shown to be robust stable with respect to unmodelled plant uncertainties and bounded disturbances a self tuning controller is proposed here consisting of two steps the first an optimal control law is derived by means of minimizing a quadratic cost function of the clarke gawthrop type the second the optimal control law is modified by introducing an estimate of the modelling error as a feedback for the parameter estimation a modified least squares scheme with a relative dead zone is employed the robustness results are derived by neither requiring too much a priori knowledge of the plant parameters nor using any assumptions about the adaptive signals

paper adaptive controllers for discrete time systems with arbitrary zeros an overview the paper presents a comprehensive review of the problem of designing discrete time adaptive controllers for systems with arbitrary zeros minimum or non minimum phase systems being particular cases the authors have attempted to give a unified presentation of the various existing schemes by stripping away the technical details and highlighting their close interrelation through the use of a common framework both algorithmic and theoretical aspects are discussed with particular emphasis on the stability of the schemes

reduced order adaptive observation with nonspecific adaptive law the robustness of adaptive observers whose order is less than that of the plant is addressed it is shown that residuals unmodeled states with finite energy over a finite time interval together with a definitely exciting plant input are sufficient to insure ultimate boundedness of the parameter and state errors regardless of the plant stability or input boundedness the residuals are assumed to be strongly observable at the output

complete delay decomposing approach to asymptotic stability for neural networks with time varying delays this paper is concerned with the problem of stability of neural networks with time varying delays a novel lyapunov krasovskii functional decomposing the delays in all integral terms is proposed by exploiting all possible information and considering independent upper bounds of the delay derivative in various delay intervals some new generalized delay dependent stability criteria are established which are different from the existing ones and improve upon previous results numerical examples are finally given to demonstrate the effectiveness and the merits of the proposed method

delay slope dependent stability results of recurrent neural networks by using the fact that the neuron activation functions are sector bounded and nondecreasing this brief presents a new method named the delay slope dependent method for stability analysis of a class of recurrent neural networks with time varying delays this method includes more information on the slope of neuron activation functions and fewer matrix variables in the constructed lyapunov krasovskii functional then some improved delay dependent stability criteria with less computational burden and conservatism are obtained numerical examples are given to illustrate the effectiveness and the benefits of the proposed method

on adaptive state regulation an indirect adaptive state regulator consisting of an adaptive observer and an asymptotic feedback matrix synthesis is discussed in a rigorous analysis lyapunov stability of the resulting closed loop system without the need for external excitation possible global instabilities as well as global convergence subject to sufficient excitation are shown moreover the behavior of the closed loop system is analyzed for cases in which the true plant differs from the assumed linear time invariant model in that it is of higher order contains slight nonlinearities and time variation effects and is subject to plant and measurement disturbances

multivariate regression models for panel data abstract the paper examines the relationship between heterogeneity bias and strict exogeneity in a distributed lag regression of y on x the relationship is very strong when x is continuous weaker when x is discrete and non existent as the order of the distributed lag becomes infinite the individual specific random variables introduce nonlinearity and heteroskedasticity so the paper provides an appropriate framework for the estimation of multivariate linear predictors restrictions are imposed using a minimum distance estimator it is generally more efficient than the conventional estimators such as quasi maximum likelihood there are computationally simple generalizations of two and three stage least squares that achieve this efficiency gain some of these ideas are illustrated using the sample of young men in the national longitudinal survey the paper reports regressions on the leads and lags of variables measuring union coverage smsa and region the results indicate that the leads and lags could have been generated just by a random intercept this gives some support for analysis of covariance type estimates these estimates indicate a substantial heterogeneity bias in the union smsa and region coefficients

delay dependent stability analysis and control synthesis of fuzzy dynamic systems with time delay this paper focuses on the delay dependent stability analysis and stabilization for fuzzy control systems with time delay based on linear matrix inequality lmi approach a delay dependent stability criteria has been developed the design schemes of stabilization via state feedback is developed and then the result is extended to the case of observer based output feedback all the researching results are presented by means of lmis two illustrative examples are given to illustrate the validity of the proposed design procedures

a general structural equation model with dichotomous ordered categorical and continuous latent variable indicators a structural equation model is proposed with a generalized measurement part allowing for dichotomous and ordered categorical variables indicators in addition to continuous ones a computationally feasible three stage estimator is proposed for any combination of observed variable types this approach provides large sample chi square tests of fit and standard errors of estimates for situations not previously covered two multiple indicator modeling examples are given one is a simultaneous analysis of two groups with a structural equation model underlying skewed likert variables the second is a longitudinal model with a structural model for multivariate probit regressions

design of adaptive pole placement controllers for plants of unknown order by an indirect control approach an adaptive pole placement control problem is considered for a scalar discrete time linear plant assuming the knowledge of an upper bound of the plant order a class of models that can be regarded to be input output equivalent to the plant is first constructed based on the parameter estimate generated by a least squares type identification scheme a minimization problem is then derived that enables one to determine the model s coefficients and the pole placing feedback gains so that the desired closed loop poles are assigned in a limit

non fragile memory filtering of t s fuzzy delayed neural networks based on switched fuzzy sampled data control abstract this paper deals with the non fragile memory filtering issue of t s fuzzy delayed neural networks with randomly occurring time varying parameters uncertainties and variable sampling rates compared with existing sampled data control schemes an improved switched fuzzy memory sampled data control protocol is designed for the first time which involves not only a signal transmission delay but also switched topologies by developing some new terms and taking full advantage of the variable characteristics related to the actual sampling pattern a modified loose looped fuzzy membership functions fmfs dependent lyapunov krasovskii functional lkf is constructed based on the information of the time derivative of fmfs meanwhile some relaxed matrices chosen in lkf are not consequentially positive definite moreover with the lkf methodology and employing the developed estimation technique several optimized control algorithms with both a larger sampling period and upper bound of time varying delays for achieving the stabilization of the resultant t s fuzzy delayed neural networks are derived finally a numerical example is presented to demonstrate the superiority and applicability of the theoretical results

dynamic analysis of fractional order quaternion valued fuzzy memristive neural networks vector ordering approach abstract this paper addresses the problems of stabilization and synchronization control of the fractional order quaternion valued fuzzy memristive neural networks by means of the contraction mapping theorem the corresponding conditions are established for the existence and uniqueness of the equilibrium point ep subsequently the corresponding stability analysis are proposed by two suitable controllers what should be mentioned is that a vector ordering approach is developed to determine the magnitude of two different quaternions as a result the closed convex hull proposed by quaternion connections can be easily derived finally two simulation examples are utilized to illustrate the effectiveness of the proposed control method

stochastic linear quadratic differential games in a state feedback setting with sampled measurements abstract the problem of sampled data nash equilibrium strategy in a state feedback setting for a stochastic linear quadratic differential game is addressed it is assumed that the admissible strategies are constant on the interval between two measurements the original problem is converted into an equivalent one for a linear stochastic system with finite jumps this new formulation of the problem allows us to derive necessary and sufficient conditions for the existence of a sampled data nash equilibrium strategy in a state feedback form these conditions are expressed in terms of solvability of a system of interconnected matrix linear differential equations with finite jumps and subject to some algebraic constraints we provide explicit formulae of the gain matrices of the nash equilibrium strategy in the class of piece wise constant strategies in a state feedback form the gain matrices of the feedback nash equilibrium strategy are computed based on the solution of the considered system of matrix linear differential equations with finite jumps for the implementation of these strategies only measurements at discrete time instances of the states of the dynamical system are required finally we show that under some additional assumptions regarding the sign of the weights matrices in the performance criteria of the two players there exists a unique piecewise nash equilibrium strategy in a state feedback form if the maximal length of the sampling period is sufficiently small

global convergence for adaptive one step ahead optimal controllers based on input matching this paper establishes global convergence for adaptive one step ahead optimal controllers applied to a class of linear discrete time single input single output systems the class of systems includes all stable systems whether they are minimum phase or not all minimum phase systems whether they are stable or not and some unstable nonminimum phase systems the key substantive assumption is that the one step ahead optimal controller designed using the true system parameters leads to a stable closed loop system subject to this natural restriction it is shown that a simple adaptive control algorithm based on input matching is globally convergent in the sense that the system inputs and outputs remain bounded for all time and the input converges to the one step ahead optimal input both deterministic and stochastic cases are treated

a new adaptive law for robust adaptation without persistent excitation a new adaptive law motivated by the work of loannou and kokotovic 1983 is proposed for the robust adaptive control of plants with unknown parameters in this adaptive law the output error plays a dual role in the adjustment of the control parameter vector the advantages of using the adaptive law over others proposed in the literature are discussed in the ideal case the adaptive system has bounded solutions in addition the origin of the error equations is exponentially stable when the reference input is persistently exciting and has a sufficiently large amplitude the adaptive system is also shown to be robust under bounded external disturbances finally it is shown that by suitably modifying the adaptive law the overall system can be made robust in the presence of a class of unmodeled dynamics of the plant simulation results are presented throughout the paper to complement the theoretical developments

correlated binary regression with covariates specific to each binary observation regression methods are considered for the analysis of correlated binary data when each binary observation may have its own covariates it is argued that binary response models that condition on some or all binary responses in a given block are useful for studying certain types of dependencies but not for the estimation of marginal response probabilities or pairwise correlations fully parametric approaches to these latter problems appear to be unduly complicated except in such special cases as the analysis of paired binary data hence a generalized estimating equation approach is advocated for inference on response probabilities and correlations illustrations involving both small and large block sizes are provided

paper an approach to stable indirect adaptive control stable indirect adaptive control of minimum and nonminimum phase plants is established for cases where a priori bounds on the unknown plant parameters are known and where for each set of parameter values within these bounds the plant has no unstable pole zero cancellation by incorporating this partial parameter knowledge in the adaptive law and using a nonminimal representation of the plant it is shown that the adaptive closed loop control system can be written as an exponentially stable system driven by the identification error the stability of the adaptive control system is then shown using techniques similar to those known from the model reference adaptive control approach

stability analysis and design of fuzzy control systems abstract the stability analysis and the design technique of fuzzy control systems using fuzzy block diagrams are discussed first we show the concept of fuzzy blocks and consider the connection problems of fuzzy blocks diagrams we derive some theorems and corollaries with respect to two basic types of connections of fuzzy blocks in order to preserve some properties in a connection of fuzzy blocks continuous piecewise polynomial membership functions are defined secondly a sufficient condition which guarantees the stability of fuzzy systems is obtained in terms of lyapunov s direct method we give an important fact based on this condition thirdly we propose a new design technique of a fuzzy controller the fuzzy block diagrams and the stability analysis are applied to the design problems of a model based fuzzy controller

a reduced rank multivariate regression approach to aquatic joint toxicity experiments classical multivariate regression techniques can lead to an asymptotically efficient restricted estimator of the regression matrix if there is evidence that the regression matrix is of reduced rank a sublethal joint toxicity experiment involving two agents is used to illustrate this approach

decentralized adaptive control of interconnected systems the adaptive control of a class of large scale systems formed of an arbitrary interconnection of subsystems with unknown parameters nonlinearities and bounded disturbances is investigated it is first shown that no matter how weak the interconnections are a decentralized adaptive control scheme can become unstable approaches are then developed for stabilization and tracking using new decentralized adaptive controllers in the case where the relative degree n of the transfer function of each decoupled subsystem is less than or equal to two sufficient conditions are established which guarantee boundedness and exponential convergence of the state and parameter errors to bounded residual sets in the absence of disturbances and interconnections the decentralized adaptive control schemes guarantee exact convergence of the tracking errors to zero the effectiveness of the proposed adaptive schemes is demonstrated using a simple example

a combined backstepping and stochastic small gain approach to robust adaptive fuzzy output feedback control in this paper an adaptive fuzzy output feedback control approach is investigated for a class of stochastic nonlinear strict feedback systems without the requirement of states measurement the stochastic nonlinear system addressed in this paper is assumed to possess unstructured uncertainties unknown nonlinear functions and in the presence of unmodeled dynamics dynamics disturbances fuzzy logic systems are used to approximate the unstructured uncertainties and a fuzzy state observer is designed to estimate the unmeasured states by combining the backstepping design technique with the stochastic small gain approach a new adaptive fuzzy output feedback control approach is developed it is proved that the proposed control approach can guarantee that the closed loop system is input state practically stability isps in probability and the observer errors and the output of the system converge to a small neighborhood of the origin by appropriate choice of the design parameters simulation results are included to indicate that the proposed adaptive fuzzy control approach has a satisfactory control performance in addition the simulation comparisons with the previous methods show that the proposed adaptive fuzzy control approach has robustness to the dynamical uncertainties

adaptive state regulation in the presence of disturbances of known frequency range adaptive state regulation via adaptive observation and asymptotic feedback matrix synthesis in the presence of bounded disturbances of known frequency range is considered the adaptive observer uses a filtered output error to adapt parameter and state estimates the filter is designed to attenuate in the disturbance frequency range it is shown that with persistent excitation parameter and feedback gain errors are bounded by a quantity which is proportional directly to the attenuated disturbance magnitudes and inversely to the degree of persistent excitation closed loop stability is proven when the ratio of filtered disturbance magnitude to degree of persistent excitation is sufficiently small a computer simulation demonstrates the method

new results on pseudo almost periodic solutions of quaternion valued fuzzy cellular neural networks with delays abstract this article investigates quaternion valued fuzzy cellular neural networks with delays with the help of fixed point theory exponential dichotomy of linear equations and related inequalities some new sufficient conditions which guarantee the existence and global exponentially stability of pseudo almost periodic solutions to quaternion valued fuzzy cellular neural networks with delays are established computer simulations are carried out to check the practicability of the obtained main results so far no researchers have investigated this topic the derived results of this article are completely innovative and complement some earlier investigations to some degree

passivity based synchronization of markovian jump complex dynamical networks with time varying delays parameter uncertainties reaction diffusion terms and sampled data control abstract the problem of passivity analysis of reaction diffusion complex dynamical networks with time varying delays and markovian jumping parameters is considered in this article to reflect most of the dynamical behaviors of the system the parameter uncertainties are considered specially the time varying delays are taken into consideration to describe the sample data control of the system the control objective is that the trajectories of the system by designing suitable control schemes track the trajectories of the system with sample data control it is shown that through lyapunov stability theory the proposed sample data controllers are successful in ensuring the achievement of passivity based synchronization of complex dynamical networks even in the case of uncertainty and markovian jumping parameters by utilizing the lyapunov functional method jensen s inequality wirtinger s inequality and reciprocal convex techniques we establish a sufficient criterion such that for all admissible parameter uncertainties the complex dynamical network is passive the derived criteria are expressed in terms of linear matrix inequalities that can be easily checked by using the standard numerical software illustrative example is presented to demonstrate the effectiveness and usefulness of the proposed results

parameter identification in the presence of non parametric dynamic uncertainty abstract this paper provides identification laws which yield monotonic parameter error reduction in the absence of external noise inputs but in the presence of non parametric dynamic uncertainty unmodelled dynamics this paper considers two non parametric uncertainty sets motivated by robust control the weighted ball in h and the weighted ball in the induced l2 norm the non parametric uncertainty produces signals which corrupt the signals used in identification this paper shows that the corruption is non conservatively bounded in an l2 sense this l2 bound is then compared with the signal energy with the result used to enable disable the parameter adjustment of a gradient least squares adjustment mechanism it is proved that the resulting parameter adjustment yields non increasing parameter errors with strictly decreasing parameter errors whenever the parameter errors are distinguishable from zero given the system input and output histories

new delay dependent stability criteria for neural networks with time varying delay using delay decomposition approach this brief is concerned with the problem of asymptotic stability of neural networks with time varying delays the activation functions are monotone nondecreasing with known lower and upper bounds novel stability criteria are derived by employing new lyapunov krasovskii functional and the integral inequality the developed stability criteria have delay dependencies and the results are characterized by linear matrix inequalities new and less conservative solutions to the global stability problem are provided in terms of feasibility testing numerical examples are finally given to demonstrate the effectiveness of the proposed method

a stable adaptive implementation of the internal model principle an adaptive implementation of the internal model principle for linear time invariant systems that allows for rejection of unknown deterministically modeled disturbances is provided the minimal representation of the system model is used for parameter estimation and the global convergence and stability of the algorithm are investigated with unmodeled dynamics with proper modification of the parameter estimation algorithm global convergence and stability for the algorithm are obtained without the requirement of persistent excitation some simulation results are given to support the analysis

paper some design guidelines for discrete time adaptive controllers there have been many algorithms proposed for adaptive control which will provide globally asymptotically stable controllers if some stringent conditions on the plant are met the conditions on the plant cannot be met in practice as all plants will contain high frequency unmodeled dynamics this paper uses a linearization analysis of a nonlinear adaptive controller to demonstrate analytically some design guidelines which alleviate some of the problems associated with adaptive control in the presence of unmodeled dynamics the points made are further demonstrated by simulation results

design issues in adaptive control an integrated approach to the design of practical adaptive control algorithms is presented many existing ideas are brought together and the effect of various design parameters available to a user is explored the theory is extended by showing how the problem of stabilizability of the estimated model can be overcome by running parallel estimators it is shown how asymptotic tracking of deterministic set points can be achieved in the presence of unmodeled dynamics

reduced rank models for multiple time series summary this paper is concerned with the investigation of reduced rank coefficient models for multiple time series in particular autoregressive processes which have a structure to their coefficient matrices similar to that of classical multivariate reduced rank regression are studied in detail the estimation of parameters and associated asymptotic theory are derived the exact correspondence between the reduced rank regression procedure for multiple autoregressive processes and the canonical analysis of box tiao 1977 is briefly indicated to illustrate the methods u s hog data are considered

delay dependent robust h sub spl infin control for t s fuzzy systems with time delay this paper focuses on the problem of delay dependent robust fuzzy control for a class of nonlinear delay systems via state feedback the takagi sugeno t s fuzzy model is adopted for representing a nonlinear system with time delayed state a delay dependent stabilization criterion is first presented then the methods of robust stabilization and robust h sub spl infin control are developed which are dependent on the size of the delay and are based on the solutions of linear matrix inequalities lmis finally a design example of robust h sub spl infin controller for uncertain nonlinear systems is given to illustrate the effectiveness of the approaches proposed in this paper

robust adaptive control conditions for global stability an input output approach is presented for analyzing the global stability and robustness properties of adaptive controllers to unmodeled dynamics the concept of a tuned system is introduced i e the control system that could be obtained if the plant were known comparing the adaptive system to the tuned system results in the development of a generic adaptive error system passivity theory is used to derive conditions which guarantee global stability of the error system associated with the adaptive controller and ensure boundedness of the adaptive gains specific bounds are presented for certain significant signals in the control systems limitations of these global results are discussed particularly the requirement that a certain operator be strictly positive real spr a condition that is unlikely to hold due to unmodeled dynamics

l induced output feedback controller synthesis for positive nonlinear systems via t s fuzzy model approach abstract this paper investigates the l induced output feedback controller synthesis problem for positive takagi sugeno t s fuzzy systems for positive t s fuzzy systems a sufficient condition for stability and l induced performance is first put forward using a group of linear inequalities then on the basis of these inequalities the conditions on which static output feedback controllers exist are set up and to solve these conditions a method of iterative convex optimization is provided moreover a complete solution to the synthesis problem of the controller is proposed based on linear programming for positive t s fuzzy systems with single output or input finally two examples are presented to illustrate and verify the effective and applicable methods derived in this paper

paper instability analysis and improvement of robustness of adaptive control the effects of unmodeled high frequency dynamics and bounded disturbances on stability and performance of adaptive control schemes are analyzed five possible types of instability mechanisms parameter drift linear instability fast adaptation instability high frequency instability and throughput instability are analyzed using simple examples a procedure is used to construct lyapunov like functions for a modified adaptive controller applied to a dominant plant of relative degree one in the presence of parasitics and disturbances and obtain sufficient conditions under which none of the five types of instability can occur the modified scheme is robust in the sense that it guarantees the existence of a large region of attraction from which all the trajectories remain bounded and the state errors converge exponentially to a much smaller residual set the size of the region of attraction depends on the speed of parasitics in such a way that as the parasitics become infinitely fast the region of attraction becomes the whole space

a stable adaptive control design for discrete plants with unmodelled dynamics this paper presents a simple discrete model reference adaptive control design which maintains stability around the equilibrium of the tracking error in the presence of unmodelled dynamics only a weak a priori knowledge namely upper bounds of the absolute value of the unmodelled parameters and orders of the unmodelled dynamics is required for the implementation the adaptive controller has an additional parameter to protect the scheme against instability

improved stability criteria for time varying delayed t s fuzzy systems via delay partitioning approach this paper focuses on the stability analysis for uncertain takagi sugeno t s fuzzy systems with interval time varying delay the uncertainties of system parameter matrices are assumed to be time varying and norm bounded some new lyapunov krasovskii functionals lkfs are constructed by nonuniformly dividing the whole delay interval into multiple segments and choosing different lyapunov functionals to different segments in the lkfs by employing these lkfs some new delay derivative dependent stability criteria are established for the nominal and uncertain t s fuzzy systems in a convex way these stability criteria are derived that depend on both the upper and lower bounds of the time derivative of the delay by employing the new delay partitioning approach the obtained stability criteria are stated in terms of linear matrix inequality lmi they are equivalent or less conservative while involving less decision variables than the existing results finally numerical examples are given to illustrate the effectiveness and reduced conservatism of the proposed results

improved delay dependent stability criterion for neural networks with time varying delay in this paper the problem of delay dependent asymptotic stability criterion for neural networks with time varying delay has been considered a new class of lyapunov functional which contains a triple integral term is constructed to derive some new delay dependent stability criteria the obtained criteria are less conservative because free weighting matrices method and a convex optimization approach are considered finally numerical examples are given to illustrate the effectiveness of the proposed method

synchronization stability of riemann liouville fractional delay coupled complex neural networks abstract this paper focuses on discussing the synchronization stability problem of the riemann liouville fractional coupled complex interconnected delayed neural networks several globally asymptotic stability criteria for considered network systems are established in terms of lyapunov functional approach and linear matrix inequality technique the relationships between the stability of fractional order isolated neural networks and the synchronization of fractional order coupled complex neural networks are discussed in detail the main advantage is to avoid calculating the fractional derivative of lyapunov functional to discuss the synchronization stability conditions a numerical example is also performed to demonstrate the validity and feasibility of the proposed results

paper self tuning leader follower games the optimization of a class of linear discrete time stochastic systems with unknown parameters and multiple decision makers or controllers each having his own single stage quadratic objective is considered based on a centralized information pattern a steady state solution is obtained for the stochastic adaptive leader follower game problem it is shown that this class of leader follower game problems can be transformed to an equivalent class of multiple input multiple output self tuning control problems with a single decision maker the convergence of the multiple input multiple output self tuning controller is established and applied to the leader follower game a numerical simulation illustrates the proposed self tuning algorithm for an adaptive leader follower strategy

disturbance instabilities in an adaptive system two types of disturbance instability of simple adaptive schemes are analyzed a drift equation shows how stable equilibria are forced to infinity by infrequent but persistent switches of a constant disturbance the modification proposed by p ioannou counteracts these instabilities

adaptive control of nonminimum phase systems recent papers have established global convergence for a class of adaptive control algorithms for discrete time linear dynamic systems however in most cases studied to date it has been assumed that the system is stably invertible this assumption plays a major role in the proofs of convergence in this paper we consider the more general case in which it is not assumed that the system is either stable or stably invertible we establish local convergence for a class of adaptive control algorithms applied to general discrete deterministic linear time invariant systems by convergence in this context we mean that the system inputs and outputs remain bounded for all time and the closed loop poles are effectively assigned in the limit for a given desired trajectory

persistent excitation in adaptive systems the importance of the concept of persistent excitation pe in adaptive identification and control has been recognized for some time recently it has become evident that it also plays a central role in many questions related to the robustness of adaptive systems there is every reason to believe that arguments involving this concept will continue to feature prominently in the analysis of most of the important problems of adaptive control hence there is a real need for a deeper understanding of the concept the paper is written with three objectives the first which is tutorial in nature is to provide a general framework for the discussion of persistent excitation and to collect results in the area which are scattered throughout the adaptive literature the second objective is to present some new results related to the uniform asymptotic stability u a s and robustness of adaptive systems and the relation of pe to the stability properties of a class of non linear systems the final objective is to di

state estimation for delayed neural networks in this letter the state estimation problem is studied for neural networks with time varying delays the interconnection matrix and the activation functions are assumed to be norm bounded the problem addressed is to estimate the neuron states through available output measurements such that for all admissible time delays the dynamics of the estimation error is globally exponentially stable an effective linear matrix inequality approach is developed to solve the neuron state estimation problem in particular we derive the conditions for the existence of the desired estimators for the delayed neural networks we also parameterize the explicit expression of the set of desired estimators in terms of linear matrix inequalities lmis finally it is shown that the main results can be easily extended to cope with the traditional stability analysis problem for delayed neural networks numerical examples are included to illustrate the applicability of the proposed design method

stable pole placement direct adaptive control for systems with arbitrary zeros this paper extends a recently proposed adaptive regulation algorithm to the general case where a non zero bounded reference signal is allowed the desired control objectives are asymptotically satisfied in the sense that the estimated plant together with the adaptive controller yield a characteristic polynomial arbitrarily close to the desired one furthermore in the case of a zero reference signal the regulation objective is asymptotically achieved