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

morse theory and asymptotic linear hamiltonian system on considere l existence de solutions 2 periodiques des systemes hamiltoniens asymptotiquement quadratiques jz h z t ou j 0 i i 0 i in la matrice identite de n n

a study on h state estimation of static neural networks with time varying delays this paper studies the problem of h state estimation for static neural networks with time varying delay by construction of a suitable lyapunov krasovskii functional some improved delay dependent conditions are established such that the error system is globally exponentially stable with a decay rate and a prescribed h performance is guaranteed in order to get less conservative results of the state estimation condition zero equalities and reciprocally convex approach are employed the estimator gain matrix can be obtained in terms of the solution to linear matrix inequalities numerical examples are provided to illustrate the effectiveness and performance of the developed method

cone constraints for solutions of homogeneous bilinear systems abstract sufficient conditions are derived under which all solutions of a homogeneous bilinear system remain within a cone for both the case where an initial state is prescribed and the case where the initial state lies within another prescribed cone this situation generalizes the classical uncontrolled case in which solutions of a linear system are constrained to lie in the cone consisting of the positive orthant the conditions are given in terms of an associated linear system originally utilized in realization theory and a particular representation of its inverse system in the case where the associated linear system has single input and output channels the conditions are also shown to be necessary

state estimation for fuzzy cellular neural networks with time delay in the leakage term discrete and unbounded distributed delays this paper deals with the problem of state estimation for fuzzy cellular neural networks fcnns with time delay in the leakage term discrete and unbounded distributed delays in this paper leakage delay in the leakage term is used to unstable the neuron states it is challenging to develop a delay dependent condition to estimate the unstable neuron states through available output measurements such that the error state system is globally asymptotically stable by constructing the lyapunov krasovskii functional which contains a triple integral term an improved delay dependent stability criterion is derived in terms of linear matrix inequalities lmis however by using the free weighting matrices method a simple and efficient criterion is derived in terms of lmis for estimation the restriction such as the time varying delay which was required to be differentiable or even its time derivative which was assumed to be smaller than one are removed instead the time varying delay is only assumed to be bounded finally numerical examples and its simulations are given to demonstrate the effectiveness of the derived results

solutions of asymptotically linear operator equations via morse theory abstract in this paper we use the classical morse theory of critical points to study the existence and multiplicity of solutions of a class of asymptotically linear operator equations as special cases we include multiplicity results for semilinear elliptic bvp s as well as existence and multiplicity of periodic solutions of semilinear wave equation and of hamiltonian systems these results simplify and complement recent work of amann zehnder and castro lazer author

iteration inequalities of the maslov type index theory with applications abstract in this paper by using the index theory introduced by y long in 1999 pacific j math 187 113 149 in particular the splitting numbers maslov type mean index and the homotopy component of symplectic matrix we establish various inequalities of the maslov type index theory for iterations of symplectic paths starting from the identity as an application these results are used to study rabinowitz conjecture on the prescribed minimal period solution problem of nonlinear hamiltonian systems

multirate lqg control of continuous time stochastic systems abstract the lqg problem for stochastic continuous time systems subject to multirate sampling of both input and output variables is considered by restating the problem as a discrete time periodic lqg problem a sufficient condition for the existence of an optimal stabilizing regulator is given in terms of the structural properties of the original system and the cost function this improves on previous contributions where optimal control schemes were proposed without addressing existence and or stability issues the possibility of incorporating an integral action within the optimal lqg regulator is also briefly discussed

delay dependent stochastic stability criteria for markovian jumping neural networks with mode dependent time varying delays and partially known transition rates abstract in this paper the problem of stochastic stability criterion of markovian jumping neural networks with mode dependent time varying delays and partially known transition rates is considered some new delay dependent stability criteria are derived by choosing a new class of lyapunov functional the obtained criteria are less conservative because free weighting matrices method and a convex optimization approach are considered finally a numerical example is given to illustrate the effectiveness of the proposed method

an improved input delay approach to stabilization of fuzzy systems under variable sampling in this paper we investigate the problem of stabilization for sampled data fuzzy systems under variable sampling a novel lyapunov krasovskii functional lkf is defined to capture the characteristic of sampled data systems and an improved input delay approach is proposed by the use of an appropriate enlargement scheme new stability and stabilization criteria are obtained in terms of linear matrix inequalities lmis compared with the existing results the newly obtained ones contain less conservatism some illustrative examples are given to show the effectiveness of the proposed method and the significant improvement on the existing results

multistability of complex valued recurrent neural networks with real imaginary type activation functions this paper addresses the multistability problem of n dimensional complex valued recurrent neural networks with real imaginary type activation functions sufficient conditions are proposed for checking the existence of 2 1 2 1 n 1 equilibria under these conditions 1 1 n equilibria are locally exponentially stable and the others are unstable attractive basins of equilibria are also investigated complete attractive basins of equilibria in 1 dimensional complex valued recurrent neural networks are obtained the obtained stability results improve and extend the existing ones two numerical examples are given to illustrate the effectiveness of the obtained results

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

delay distribution dependent exponential stability criteria for discrete time recurrent neural networks with stochastic delay this brief is concerned with the analysis problem of global exponential stability in the mean square sense for a class of linear discrete time recurrent neural networks drnns with stochastic delay different from the prior research works the effects of both variation range and probability distribution of the time delay are involved in the proposed method first a modeling method is proposed by translating the probability distribution of the time delay into parameter matrices of the transformed drnn model where the delay is characterized by a stochastic binary distributed variable based on the new method the global exponential stability in the mean square sense for the drnns with stochastic delay is investigated by using the lyapunov krasovskii functional and exploiting some new analysis techniques a numerical example is provided to show the effectiveness and the applicability of the proposed method

controllability aspects of quantum dynamics a unified approach for closed and open systems knowledge about to what extent quantum dynamical systems can be steered by coherent controls is indispensable for future developments in quantum technology the purpose of this paper is to analyze such controllability aspects for finite dimensional bilinear quantum control systems we use a unified approach based on lie algebraic methods from nonlinear control theory to revisit known and to establish new results for closed and open quantum systems in particular we provide a simplified characterization of different notions of controllability for closed quantum systems described by the liouville von neumann equation we derive new necessary and sufficient conditions for accessibility of open quantum systems modelled by the lindblad kossakowski master equation to this end we exploit a well studied topic of differential geometry namely the classification of all matrix lie groups which act transitively on the grassmann manifold or the punctured euclidean space for the special case of coupled spin 1 2 systems we obtain a remarkably simple characterization of accessibility these accessibility results correct and refine previous statements in the quantum control literature

iteration inequalities of the maslov p index theory with applications abstract in this paper the p splitting numbers and p mean index for a symplectic path starting from the identity with p s p 2 n are defined first and then by using the maslov p index theory for a symplectic path various inequalities of the maslov p index theory for iterations of symplectic paths starting from the identity are established as an application these results are used to study the minimal period problem of p symmetric periodic solution of nonlinear autonomous hamiltonian systems

adaptive neural output feedback controller design with reduced order observer for a class of uncertain nonlinear siso systems an adaptive output feedback control is studied for uncertain nonlinear single input single output systems with partial unmeasured states in the scheme a reduced order observer roo is designed to estimate those unmeasured states by employing radial basis function neural networks and incorporating the roo into a new backstepping design an adaptive output feedback controller is constructively developed a prominent advantage is its ability to balance the control action between the state feedback and the output feedback in addition the scheme can be still implemented when all the states are not available the stability of the closed loop system is guaranteed in the sense that all the signals are semiglobal uniformly ultimately bounded and the system output tracks the reference signal to a bounded compact set a simulation example is given to validate the effectiveness of the proposed scheme

quantized saturated control for sampled data systems under noisy sampling intervals a confluent vandermonde matrix approach in this paper a unified framework is established to investigate both the quantized and the saturated control problems for a class of sampled data systems under noisy sampling intervals a random variable obeying the erlang distribution is used to describe the noisy sampling intervals in virtue of the matrix exponential the sampled data control system is transformed into an equivalent discrete time stochastic system and the aim of this paper is to design a quantized saturated sampled data controller such that the resulting discrete time stochastic system is stochastically stable when the sampling error follows the erlang distribution in order to deal with the case of multiple control inputs a confluent vandermonde matrix approach is proposed in the design process by using the kronecker product operation and the matrix inequality techniques the desired quantized saturated controller gains are designed in terms of the solution to certain matrix inequalities finally a simulation example is exploited to verify the effectiveness of the proposed design approach

robust sampled data control with random missing data scenario the problem of robust sampled data control for uncertain dynamic systems in the presence of missing data has been investigated by using stochastic variables with a bernoulli distributed white sequence to model missing data along with time varying norm bounded uncertainties and the input delay approach for sampled data systems two models for the considered sampled data control system are proposed as the consecutive missing data scenario moreover when data are missing the control signals are held as the last received data sufficient conditions for the existence of desired robust sampled data controllers are presented in the form of a linear matrix inequality a numerical example is given to illustrate the validity of the proposed methods and to compare the results between the two proposed models of the sampled data control system

controllability properties for finite dimensional quantum markovian master equations various notions from geometric control theory are used to characterize the behavior of the markovian master equation for n level quantum mechanical systems driven by unitary control and to describe the structure of the sets of reachable states it is shown that the system can be accessible but neither small time controllable nor controllable in finite time in particular if the generators of quantum dynamical semigroups are unital then the reachable sets admit easy characterizations as they monotonically grow in time the two level case is treated in detail

fuzzy model based sampled data control of chaotic systems a fuzzy time dependent lyapunov krasovskii functional approach this paper addresses the sampled data stabilization problem for chaotic systems represented by takagi sugeno t s fuzzy models if the upper bounds for the time derivative of membership functions are available combining the fuzzy blending for some quadratic functions together with the introduction of some new useful terms a novel fuzzy time dependent lyapunov krasovskii functional lkf is proposed to fully capture the available characteristics of the actual sampling pattern and membership functions simultaneously based on the proposed lkf a new criterion dependent on the upper bounds for the time derivative of membership functions is presented to guarantee the asymptotic stability of the whole closed loop system moreover a stability criterion independent of the upper bounds is also provided based on the corresponding common time dependent lkf then the designed fuzzy sampled data controller can be synthesized by analyzing the corresponding stabilization conditions moreover a search algorithm is provided to find the optimal tuning parameters finally one practical example of the lorenz system is given to illustrate that much less conservativeness can be achieved compared with the earlier results by using the corresponding common lkf and the results can be further improved when adopting the fuzzy time dependent lkf within large upper bounds

sampled data fuzzy control for a class of nonlinear systems with missing data and disturbances this paper considers the sampled data fuzzy control of nonlinear systems in strict feedback form with disturbances and random missing input data we propose a novel method in which a state observer and a disturbance observer are combined to construct a sampled data fuzzy output feedback controller the stochastic variables with a bernoulli distributed sequence are used to model missing input data fuzzy logic systems are applied to approximate nonlinearities without requiring prior knowledge the relation between observer gain and sampling period is established the output feedback controller designed guarantees that the nonlinear system is globally stable a simulation example of four degrees of freedom robotic arm is used to demonstrate the effectiveness and applicability of the proposed control scheme

design of state estimator for bam fuzzy cellular neural networks with leakage and unbounded distributed delays in this paper the state estimation problem is studied for the bidirectional associative memory bam fuzzy cellular neural networks fcnns with delays where the delays include leakage and unbounded distributed delays the problem addressed is to estimate the neuron states through available output measurements such that for all admissible leakage and unbounded distributed delays the dynamics of the estimation error is globally asymptotically stable a delay dependent linear matrix inequality lmi criterion for the existence of the estimator is proposed by using the lyapunovkrasovskii functional method in addition the unknown gain matrix is determined by solving a delay dependent lmi finally numerical examples and simulations are provided to illustrate the effectiveness of the theoretical results

h optimal sampled data control computation and design abstract h optimal control of sampled data systems is considered the h analysis or synthesis problem requires iteration on the achievable performance level for each iteration the sampled data problem is reduced to an equivalent discrete time problem in this paper a new set of formulas is obtained for the equivalent discrete time system in each iteration the formulas depend on computing only one matrix exponential function the formulas are applied to a design example yielding interesting results

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

modeling and control of ito stochastic networked control systems with random packet dropouts subject to time varying sampling in this note we discuss the modeling and control of a class of ito stochastic networked control systems ncss with packet dropouts that are subject to time varying sampling the system under consideration is first modeled as a continuous time system with input delay that is subject to double randomness via the input delay approach then by assuming the packet drop rate and recurring to the celebrated formula of total probability an equivalent model which takes full advantage of probability distribution characteristics of both packet dropouts and sampling periods is established in particular the probability distribution values of the stochastic delay taking values in two given intervals can be explicitly obtained which is of crucial importance to model and analyze the actual problem in ncss based on that robustly exponentially mean square stability of the system with an h infty performance is guaranteed an h infty controller design procedure is then proposed finally a numerical simulation example is exploited to show the effectiveness and applicability of the results derived and some less conservative results are obtained

global asymptotic stability of reaction diffusion cohen grossberg neural networks with continuously distributed delays this paper is concerned with the global asymptotic stability of a class of reaction diffusion cohen grossberg neural networks with continuously distributed delays under some suitable assumptions and using a matrix decomposition method we apply the linear matrix inequality lmi method to propose some new sufficient stability conditions for the reaction diffusion cohen grossberg neural networks with continuously distributed delays the obtained results are easy to check and improve upon the existing stability results some remarks are given to show the advantages of the obtained results over the previous results an example is also given to demonstrate the effectiveness of the obtained results

robust h sub spl infin filtering for stochastic time delay systems with missing measurements in this paper the robust hinfin filtering problem is studied for stochastic uncertain discrete time delay systems with missing measurements the missing measurements are described by a binary switching sequence satisfying a conditional probability distribution we aim to design filters such that for all possible missing observations and all admissible parameter uncertainties the filtering error system is exponentially mean square stable and the prescribed hinfin performance constraint is met in terms of certain linear matrix inequalities lmis sufficient conditions for the solvability of the addressed problem are obtained when these lmis are feasible an explicit expression of a desired robust hinfin filter is also given an optimization problem is subsequently formulated by optimizing the hinfin filtering performances finally a numerical example is provided to demonstrate the effectiveness and applicability of the proposed design approach

an efficient hierarchical identification method for general dual rate sampled data systems for the lifted input output representation of general dual rate sampled data systems this paper presents a decomposition based recursive least squares d ls identification algorithm using the hierarchical identification principle compared with the recursive least squares rls algorithm the proposed d ls algorithm does not require computing the covariance matrices with large sizes and matrix inverses in each recursion step and thus has a higher computational efficiency than the rls algorithm the performance analysis of the d ls algorithm indicates that the parameter estimates can converge to their true values a simulation example is given to confirm the convergence results

nonfragile exponential synchronization of delayed complex dynamical networks with memory sampled data control this paper considers nonfragile exponential synchronization for complex dynamical networks cdns with time varying coupling delay the sampled data feedback control which is assumed to allow norm bounded uncertainty and involves a constant signal transmission delay is constructed for the first time in this paper by constructing a suitable augmented lyapunov function and with the help of introduced integral inequalities and employing the convex combination technique a sufficient condition is developed such that the nonfragile exponential stability of the error system is guaranteed as a result for the case of sampled data control free of norm bound uncertainties some sufficient conditions of sampled data synchronization criteria for the cdns with time varying coupling delay are presented as the formulations are in the framework of linear matrix inequality these conditions can be easily solved and implemented two illustrative examples are presented to demonstrate the effectiveness and merits of the proposed feedback control

stochastic sampling algorithms for state estimation of jump markov linear systems jump markov linear systems are linear systems whose parameters evolve with time according to a finite state markov chain given a set of observations our aim is to estimate the states of the finite state markov chain and the continuous in space states of the linear system the computational cost in computing conditional mean or maximum a posteriori map state estimates of the markov chain or the state of the jump markov linear system grows exponentially in the number of observations we present three globally convergent algorithms based on stochastic sampling methods for state estimation of jump markov linear systems the cost per iteration is linear in the data length the first proposed algorithm is a data augmentation da scheme that yields conditional mean state estimates the second proposed scheme is a stochastic annealing sa version of da that computes the joint map sequence estimate of the finite and continuous states finally a metropolis hastings da scheme based on sa is designed to yield the map estimate of the finite state markov chain convergence results of the three above mentioned stochastic algorithms are obtained computer simulations are carried out to evaluate the performances of the proposed algorithms the problem of estimating a sparse signal developing from a neutron sensor based on a set of noisy data from a neutron sensor and the problem of narrow band interference suppression in spread spectrum code division multiple access cdma systems are considered

analysis on robust h performance and stability for linear systems with interval time varying state delays via some new augmented lyapunov krasovskii functional in this paper the problem of h performance and stability analysis for linear systems with interval time varying delays is considered first by constructing a newly augmented lyapunov krasovskii functional which has not been proposed yet an improved h performance criterion with the framework of linear matrix inequalities lmis is introduced next the result and method are extended to the problem of a delay dependent stability criterion finally numerical examples are given to show the superiority of proposed method

periodic solutions with prescribed minimal period for convex autonomous hamiltonian systems clarke has shown that the problem of findingt periodic solutions for a convex hamiltonian system is equivalent to the problem of finding critical points to a certain functional dual to the classical action functional in this paper we relate the morse index of the critical point to the minimal period of the correspondingt periodic solution in particular we show that if the critical point is obtained by the ambrosetti rabinowitz mountain pass theorem the corresponding solution has minimal periodt that is it cannot bet k periodic withk integer k 2 as a consequence we prove that if the hamiltonian is flat near an equilibrium and superquadratic near infinity then for anyt 0 the corresponding hamiltonian system has a periodic solution with minimal periodt

brief integral control by variable sampling based on steady state data in this paper we consider integral control algorithms with convergent adaptive sampling for multivariable infinite dimensional systems steady state gain information is used in choosing suitable integrator gains and we also consider robustness with respect to error in measuring the steady state gain

a note on stability of hybrid stochastic differential equations the method is different from those usual methods used in previous literature some harsh restrictions imposed on diffusion operators have been removed the differentiability of delays has been removed we studied the robustness of exponential stability respect to delays in this paper the pth moment stability of hybrid stochastic differential equations is investigated several new sufficient conditions are derived by constructing an auxiliary delayed differential equation and using the comparison principle the proposed criteria remove some harsh restrictions imposed on the diffusion operators and improve some previous related works numerical examples and simulations are given to illustrate the effectiveness of theoretical results

robust exponential stability of markovian jump impulsive stochastic cohen grossberg neural networks with mixed time delays this paper is concerned with the problem of exponential stability for a class of markovian jump impulsive stochastic cohen grossberg neural networks with mixed time delays and known or unknown parameters the jumping parameters are determined by a continuous time discrete state markov chain and the mixed time delays under consideration comprise both time varying delays and continuously distributed delays to the best of the authors knowledge till now the exponential stability problem for this class of generalized neural networks has not yet been solved since continuously distributed delays are considered in this paper the main objective of this paper is to fill this gap by constructing a novel lyapunov krasovskii functional and using some new approaches and techniques several novel sufficient conditions are obtained to ensure the exponential stability of the trivial solution in the mean square the results presented in this paper generalize and improve many known results finally two numerical examples and their simulations are given to show the effectiveness of the theoretical results

sampled data state estimation of markovian jump static neural networks with interval time varying delays abstract in this paper we consider the problem of sampled data state estimation of markovian jump delayed static neural networks by constructing a suitable lyapunov krasovskii functional with double and triple integral terms and using jensen inequality delay dependent criteria are presented so that the error system is asymptotically stable instead of the continuous measurement the sampled measurement is employed to estimate the neuron states it is further demonstrated that the configuration of the gain matrix of state estimator is changed to find a feasible solution of a linear matrix inequalities which is efficiently facilitated by available algorithms finally two numerical examples are given to illustrate the usefulness and effectiveness of the proposed theoretical results

singular trajectories and the contrast imaging problem in nuclear magnetic resonance in this article the contrast imaging problem in nuclear magnetic resonance is modeled as a mayer problem in optimal control whose solutions can be parameterized using pontryagin s maximum principle and analyzed using geometric optimal control in particular the optimal problem can be mainly reduced to the analysis of the hamiltonian dynamics describing the singular trajectories and encoding their optimality status

robust extended dissipative control for sampled data markov jump systems this paper investigates the problem of the sampled data extended dissipative control for uncertain markov jump systems the systems considered are transformed into markov jump systems with polytopic uncertainties and sawtooth delays by using an input delay approach the focus is on the design of a mode independent sampled data controller such that the resulting closed loop system is mean square exponentially stable with a given decay rate and extended dissipative a novel exponential stability criterion and an extended dissipativty condition are established by proposing a new integral inequality the reduced conservatism of the criteria is demonstrated by two numerical examples furthermore a sufficient condition for the existence of a desired mode independent sampled data controller is obtained by solving a convex optimisation problem finally a resistance inductance and capacitance rlc series circuit is employed to illustrate the effectiveness of the proposed approach

feedback and optimal sensitivity model reference transformations multiplicative seminorms and approximate inverses in this paper the problem of sensitivity reduction by feedback is formulated as an optimization problem and separated from the problem of stabilization stable feedback schemes obtainable from a given plant are parameterized salient properties of sensitivity reducing schemes are derived and it is shown that plant uncertainty reduces the ability of feedback to reduce sensitivity the theory is developed for input output systems in a general setting of banach algebras and then specialized to a class of multivariable time invariant systems characterized by n times n matrices of h infty frequency response functions either with or without zeros in the right half plane the approach is based on the use of a weighted seminorm on the algebra of operators to measure sensitivity and on the concept of an approximate inverse approximate invertibility of the plant is shown to be a necessary and sufficient condition for sensitivity reduction an indicator of approximate invertibility called a measure of singularity is introduced the measure of singularity of a linear time invariant plant is shown to be determined by the location of its right half plane zeros in the absence of plant uncertainty the sensitivity to output disturbances can be reduced to an optimal value approaching the singularity measure in particular if there are no right half plane zeros sensitivity can be made arbitrarily small the feedback schemes used in the optimization of sensitivity resemble the lead lag networks of classical control design some of their properties and methods of constructing them in special cases are presented

stabilization for sampled data systems under noisy sampling interval in engineering practice the sampling interval for a sampled data system often fluctuates around a nominal ideal value based on certain probability distributions that can be specified a priori through statistical tests in this paper a fundamental stabilization problem is investigated for a class of sampled data systems under noisy sampling interval the stochastic sampled data control system under consideration is first converted into a discrete time system whose system matrix is represented as an equivalent yet tractable form via the matrix exponential computation then by introducing a vandermonde matrix the mathematical expectation of the quadratic form of the system matrix is computed by recurring to the kronecker product operation the sampled data stabilization controller is designed such that the closed loop system is stochastically stable in the presence of noisy sampling interval subsequently a special case is considered where the sampling interval obeys the continuous uniform distribution and the corresponding stabilization controller is designed finally a numerical simulation example is provided to demonstrate the effectiveness of the proposed design approach

parameter dependent lyapunov functional for stability of time delay systems with polytopic type uncertainties this note concerns the problem of the robust stability of a linear system with a time varying delay and polytopic type uncertainties in order to construct a parameter dependent lyapunov functional for the system we first devised a new method of dealing with a time delay system without uncertainties in this method the derivative terms of the state which is in the derivative of the lyapunov functional are retained and some free weighting matrices are used to express the relationships among the system variables and among the terms in the leibniz newton formula as a result the lyapunov matrices are not involved in any product terms of the system matrices in the derivative of the lyapunov functional this method is then easily extended to a system with polytopic type uncertainties numerical examples demonstrate the validity of the proposed criteria

controllability of right invariant systems on real simple lie groups we deal with controllability of right invariant control systems on lie groups following the ideas of a series of papers by jurdjevic kupka gauthier bornard gauthier kupka sallet leite crouch el assoudi gauthier we obtain a generalization of these results in the general case of real semi simple lie groups

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

minimal period estimates of periodic solutions for superquadratic hamiltonian systems in this paper we study the minimal period problem for the first order hamiltonian systems which may not be strictly convex by using variational methods and the iteration formula of the maslov type index theory we obtain estimates of the minimal period of the corresponding nonconstant periodic solutions

robust h sub spl infin control for linear discrete time systems with norm bounded nonlinear uncertainties this paper studies the problem of robust control of a class of uncertain discrete time systems the class of uncertain systems is described by a state space model with linear nominal parts and norm bounded nonlinear uncertainties in the state and output equations the authors address the problem of robust h sub spl infin control in which both robust stability and a prescribed h sub spl infin performance are required to be achieved irrespective of the uncertainties it has been shown that instead of the nonlinear uncertain system one may only consider a related linear uncertain system and thus a linear static state feedback control law is designed which is in terms of a riccati inequality

a note on the minimal periodic solutions of nonconvex superlinear hamiltonian system in this paper we consider the multiplicity result of minimal periodic solutions for the first order hamiltonian system email protected t email protected h x t we point out a mistake in the literatures m girardi m matzeu solutions of minimal period for a class of nonconvex hamiltonian systems and applications to the fixed energy problem nonlinear anal t m a 10 4 1986 371 382 and tianqing an on the minimal periodic solutions of nonconvex superlinear hamiltonian systems j math anal appl 329 2007 1273 1284 when the potential is even under some stronger restricted conditions we give the correct proof and obtain more distinct nonconstant periodic solutions with minimal period t

optimal h state feedback sampled data control design for markov jump linear systems abstractthis paper is entirely devoted to analyse and solve the h optimal state feedback sampled data control design problem for continuous time markov jump linear systems this is an important un

nontrivial solutions of operator equations and morse indices of critical points of min max type on montre que si les hypotheses du theoreme de point selle sont satisfaites si dim y 2 et si f 1 c contient seulement des points critiques non degeneres alors f 1 c contient au moins un point critique u 0 tel que 2 indice de morse u 0 dim y

design of state estimator for neural networks with leakage discrete and distributed delays abstract in this paper the design problem of state estimator for neural networks with mixed time varying delays and leakage delays has been investigated by using appropriate model transformation that shifts the considered systems into the neutral type time delay systems adapting a new lyapunov krasovskii functional which takes into account the range of time delay and by making use of some inequality techniques delay dependent criteria are developed to estimate the neuron states through available output measurements such that the estimation error system is globally asymptotically stable the criteria are formulated in terms of a set of linear matrix inequalities lmis which can be checked efficiently by use of some standard numerical packages finally three numerical examples and its simulations are given to demonstrate the usefulness and effectiveness of the presented results

adaptive neural output feedback tracking control for a class of uncertain discrete time nonlinear systems this brief studies an adaptive neural output feedback tracking control of uncertain nonlinear multi input multi output mimo systems in the discrete time form the considered mimo systems are composed of n subsystems with the couplings of inputs and states among subsystems in order to solve the noncausal problem and decouple the couplings it needs to transform the systems into a predictor form the higher order neural networks are utilized to approximate the desired controllers by using lyapunov analysis it is proven that all the signals in the closed loop system is the semi globally uniformly ultimately bounded and the output errors converge to a compact set in contrast to the existing results the advantage of the scheme is that the number of the adjustable parameters is highly reduced the effectiveness of the scheme is verified by a simulation example

stochastic finite time boundedness for markovian jumping neural networks with time varying delays abstract in this paper a novel method is developed for the finite time boundedness of markovian jumping neural networks with time varying delays by introducing a newly augmented stochastic lyapunov krasovskii functional and novel activation function conditions sufficient condition for markovian jumping neural networks is presented and the state trajectory remains in a bounded region over a pre specified finite time interval finally numerical examples are given to illustrate the efficiency and less conservative of the proposed method

dissipativity based sampled data fuzzy control design and its application to truck trailer system this paper is concerned with the problem of dissipative control for takagi sugeno fuzzy systems under time varying sampling with a known upper bound on the sampling intervals based on the time dependent lyapunov krasovskii functional approach which makes full use of the available information about the actual sampling pattern a sufficient condition is established to guarantee the sampled data systems to be exponentially stable and strictly mathcal q mathcal s mathcal r gamma dissipative based on the criterion a design algorithm for the desired sampled data controller is proposed the effectiveness and benefits of the results developed in this paper is demonstrated by a controller design for a truck trailer system

essential critical points of linking type and solutions of minimal period to superquadratic hamiltonian systems in a previous paper 3 the authors stated a result about the existence of t periodic solutions for any t 0 of autonomous hamiltonian systems having minimal period t or t 2 in the case where the hamiltonian function h has a superquadratic behaviour of the type considered by ekeland and hofer in 2 but without a global convexity assumption which is replaced by a local convexity condition in this paper we use the notion of essentiality for critical points of linking type see i 4 71 for an exhaustive exposition in order to construct a t periodic solution having t as its minimal period we remark that this result really generalizes the main theorem by ekeland and hofer in 2 the solution is found through a riesz galerkin method as exhibited in 3 following the basic scheme introduced in 6 by rabinowitz and the use of the marino prodi perturbation method more precisely as a first step one finds a critical point of linking type 2 for the restriction f of f to a suitable finite dimensional space for any n e n then some suitable estimates for derived by the linking construction enable the use of the marino prodi theorem in order to approximate each 2 by a sequence zig in such a way that z is a nondegenerate critical point of a functional fim with ff f in the c2 norm as m oo indeed 2 can be chosen as a critical point of linking type satisfying the same estimates as 2 and verifying the further property of essentiality roughly speaking one says that a linking critical point of a functional is not essential or avoidable if by looking at its linking definition one can find a local deformation i e in the neighbourhood of w which enables the construction for any minimizing sequence of surfaces having infinitesimal distances from w of another minimizing sequence of surfaces which keep away from w the solution z of the hamiltonian system is found as the limit of a suitable diagonal subsequence 1 zi j the fact that the minimal period of z can be either t or t 2 derives from some arguments which were already exploited in 3 based on some estimates for the morse indexes of finite dimensional linking points see 7 as a final step one shows that t 2 cannot be a period for z hence the conclusion that t is the minimal period of z by showing that the property of t 2 periodicity for z should yield the avoidability of the points of a subsequence of 1 first of all one looks at the maximum negative eigenvalue uj of d2f zj

morse type index theory for flows and periodic solutions for hamiltonian equations an index theory for flows is presented which extends the classical morse theory for gradient flows on compact manifolds the theory is used to prove a morse type existence statement for periodic solutions of a time dependent periodic in time and asymptotically linear hamiltonian equation

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

event triggered state estimation for markovian jumping impulsive neural networks with interval time varying delays abstractthis paper investigates the event triggered state estimation problem of markovian jumping impulsive neural networks with interval time varying delays the purpose is to design a state estimator to estimate system states through available output measurements in the neural networks there are a set of modes which are determined by markov chain a markovian jumping time delay impulsive neural networks model is employed to describe the event triggered scheme and the network related behaviour such as transmission delay data package dropout and disorder the proposed event triggered scheme is used to determine whether the sampled state information should be transmitted the discrete delays are assumed to be time varying and belong to a given interval which means that the lower and upper bounds of interval time varying delays are available first we design a state observer to estimate the neuron states second based on a novel lyapunov krasovskii functional lkf with triple integral terms and us