a new nonlinear robust disturbance observer abstract this paper presents a new nonlinear robust disturbance observer by using only the input output information for minimum phase dynamical systems with arbitrarily relative degrees the model uncertainties the nonlinear parts of the system are merged into the disturbance term and are regarded as a part of the disturbance the disturbance is assumed bounded however the upper and lower bounds are assumed unknown the new disturbance observer is inspired by the vss control theory whereas the upper and lower bounds of the disturbance are adaptively updated the estimation error of the disturbance can be made as small as necessary by choosing the design parameters the attraction of the proposed method lies in its robustness to disturbance and easiness to be implemented example and simulation results show the effectiveness of the proposed algorithm

sliding mode state observation for non linear systems in this paper a class of non linear systems with uncertainty and a lipschitz part is considered the non linear part satisfies the lipschitz condition whilst the uncertain part is a bounded function a sliding mode observer is designed to yield feedforward compensation input to stabilize the error estimation system with and without a lipschitz non linearity sufficient conditions for stability of the thau observer are also proposed these conditions ensure the stability of the non linear observer by selecting a suitable observer gain matrix in general when the system contains unmatched uncertainty ultimate boundedness is obtained the ultimate boundedness radius depends upon the defined distance of the unmatched uncertainty some sufficient conditions may ensure the stability of a non linear observer for systems with uncertainties

online blind source separation using incremental nonnegative matrix factorization with volume constraint online blind source separation bss is proposed to overcome the high computational cost problem which limits the practical applications of traditional batch bss algorithms however the existing online bss methods are mainly used to separate independent or uncorrelated sources recently nonnegative matrix factorization nmf shows great potential to separate the correlative sources where some constraints are often imposed to overcome the non uniqueness of the factorization in this paper an incremental nmf with volume constraint is derived and utilized for solving online bss the volume constraint to the mixing matrix enhances the identifiability of the sources while the incremental learning mode reduces the computational cost the proposed method takes advantage of the natural gradient based multiplication updating rule and it performs especially well in the recovery of dependent sources simulations in bss for dual energy x ray images online encrypted speech signals and high correlative face images show the validity of the proposed method

a new design for model reference robust control using state space techniques the authors propose a model reference control mrc structure that is different from existing mrc structures the new approach that we discuss is based on state space techniques however it is not simply an alternative form of the input output transfer function approach the method we use to achieve our result is obtained through a concept often called filtered transformation the filtered transformation has the effect of transforming the plant into a big system that is composed of two subsystems one subsystem satisfies the standard matching condition which is not enjoyed by the original plant while all the unmatched and unmodelled dynamics are drawn into the other unmatched subsystem the control objective for the original plant is transferred into the control of the matched subsystem while also ensuring that the unmatched subsystem remains uniformly bounded a robust control approach and the backstepping method are used to guarantee that the overall states are globally uniformly bounded and that

an observability form for linear systems with unknown inputs in this paper is given a constructive algorithm that transforms a linear system with unknown inputs into a novel observability form this form is useful to derive finite time observers even if the system does not satisfy some matching conditions usually required for the design of unknown input observers the practical example of an aircraft subject to actuator faults is provided to show the efficiency of the approach

blind separation of mutually correlated sources using precoders this paper studies the problem of blind source separation bss from instantaneous mixtures with the assumption that the source signals are mutually correlated we propose a novel approach to bss by using precoders in transmitters we show that if the precoders are properly designed some cross correlation coefficients of the coded signals can be forced to be zero at certain time lags then the unique correlation properties of the coded signals can be exploited in receiver to achieve source separation based on the proposed precoders a subspace based algorithm is derived for the blind separation of mutually correlated sources the effectiveness of the algorithm is illustrated by simulation examples

single channel blind separation using pseudo stereo mixture and complex 2 d histogram a novel single channel blind source separation scbss algorithm is presented the proposed algorithm yields at least three benefits of the scbss solution 1 resemblance of a stereo signal concept given by one microphone 2 independent of initialization and a priori knowledge of the sources and 3 it does not require iterative optimization the separation process consists of two steps 1 estimation of source characteristics where the source signals are modeled by the autoregressive process and 2 construction of masks using only the single channel mixture a new pseudo stereo mixture is formulated by weighting and time shifting the original single channel mixture this creates an artificial mixing system whose parameters will be estimated through our proposed weighted complex 2 d histogram in this paper we derive the separability of the proposed mixture model conditions required for unique mask construction based on maximum likelihood are also identified finally experimental testing on both synthetic and real audio sources is conducted to verify that the proposed algorithm yields superior performance and is computationally very fast compared with existing methods

nonnegative blind source separation by sparse component analysis based on determinant measure the problem of nonnegative blind source separation nbss is addressed in this paper where both the sources and the mixing matrix are nonnegative because many real world signals are sparse we deal with nbss by sparse component analysis first a determinant based sparseness measure named d measure is introduced to gauge the temporal and spatial sparseness of signals based on this measure a new nbss model is derived and an iterative sparseness maximization ism approach is proposed to solve this model in the ism approach the nbss problem can be cast into row to row optimizations with respect to the unmixing matrix and then the quadratic programming qp technique is used to optimize each row furthermore we analyze the source identifiability and the computational complexity of the proposed ism qp method the new method requires relatively weak conditions on the sources and the mixing matrix has high computational efficiency and is easy to implement simulation results demonstrate the effectiveness of our method

a new blind method for separating m 1 sources from m mixtures in this paper we consider the problem of underdetermined blind source separation ubss i e there are more sources than mixtures by exploiting the time frequency tf sparsity of the source signals some tf ubss algorithms have recently been proposed in the literature these algorithms require that the number of active sources at any tf point should not exceed one or be strictly less than the number of mixtures in this paper we show that if the number of sources is greater than the number of mixtures by one the sparsity assumption can be further relaxed especially it is allowed to have as many sources as mixtures at any tf point in this case then we propose a new tf ubss method to recover the sources the relaxation on the maximum number of active sources at tf points ensures that the tf based methods can be used in a wider range of applications

maximum contrast analysis for nonnegative blind source separation in this paper we propose a maximum contrast analysis mca method for nonnegative blind source separation where both the mixing matrix and the source signals are nonnegative we first show that the contrast degree of the source signals is greater than that of the mixed signals motivated by this observation we propose an mca based cost function it is further shown that the separation matrix can be obtained by maximizing the proposed cost function then we derive an iterative determinant maximization algorithm for estimating the separation matrix in the case of two sources a closed form solution exists and is derived unlike most existing blind source separation methods the proposed mca method needs neither the independence assumption nor the sparseness requirement of the sources the effectiveness of the new method is illustrated by experiments using x ray images remote sensing images infrared spectral images and real world fluorescence microscopy images

on the development of discontinuous observers abstract a framework is proposed for the development and analysis of robust observers for uncertain dynamical systems a variable structure systems approach is used emphasis is placed upon the numerical tractability of the associated synthesis procedure a selected numerical example is used to illustrate the proposed algorithm

minimal realization and dynamic properties of optimal smoothers smoothing algorithms of various kinds have been around for several decades however some basic issues regarding the dynamical structure and the minimal dimension of the steady state algorithm are still poorly understood in this paper we derive a realization of minimal dimension of the optimal smoother for a signal admitting a state space description of dimension n it is shown that the dimension of the smoothing algorithm can vary from n to 2n depending on the zero structure of the signal model the dynamics pole structure of the steady state smoother is also characterized explicitly and is related to the zero structure of the model we use several recent ideas from stochastic realization theory in particular a minimal markovian representation of the smoother is derived which requires solving a nonsymmetric wiener hopf factorization problem in this way the smoother is naturally expressed as the cascade of a whitening filter and a linear filter of least possible dimension whose state space is a minimal markovian subspace containing the smoothed estimate x spl i cap this among other aspects affords a very simple calculation of the error covariance matrix of the smoother a reduced order two filter implementation of the type due to mayne 1966 and fraser 1967 is obtained by solving a riccati equation of reduced dimension which is in general smaller than the dimension of the riccati equations considered in the literature

a convex geometry based blind source separation method for separating nonnegative sources this paper presents a convex geometry cg based method for blind separation of nonnegative sources first the unaccessible source matrix is normalized to be column sum to one by mapping the available observation matrix then its zero samples are found by searching the facets of the convex hull spanned by the mapped observations considering these zero samples a quadratic cost function with respect to each row of the unmixing matrix together with a linear constraint in relation to the involved variables is proposed upon which an algorithm is presented to estimate the unmixing matrix by solving a classical convex optimization problem unlike the traditional blind source separation bss methods the cg based method does not require the independence assumption nor the uncorrelation assumption compared with the bss methods that are specifically designed to distinguish between nonnegative sources the proposed method requires a weaker sparsity condition provided simulation results illustrate the performance of our method

mixing matrix estimation from sparse mixtures with unknown number of sources in blind source separation many methods have been proposed to estimate the mixing matrix by exploiting sparsity however they often need to know the source number a priori which is very inconvenient in practice in this paper a new method namely nonlinear projection and column masking npcm is proposed to estimate the mixing matrix a major advantage of npcm is that it does not need any knowledge of the source number in npcm the objective function is based on a nonlinear projection and its maxima just correspond to the columns of the mixing matrix thus a column can be estimated first by locating a maximum and then deflated by a masking operation this procedure is repeated until the evaluation of the objective function decreases to zero dramatically thus the mixing matrix and the number of sources are estimated simultaneously because the masking procedure may result in some small and useless local maxima particle swarm optimization pso is introduced to optimize the objective function feasibility and efficiency of pso are also discussed comparative experimental results show the efficiency of npcm especially in the cases where the number of sources is unknown and the sources are relatively less sparse

sliding mode estimators for a class of non linear systems affected by bounded disturbances the problem of state estimation for a class of non linear systems with lipschitz non linearities is addressed using sliding mode estimators stability conditions have been found to guarantee asymptotic convergence to zero of the estimation error in the absence of noise and non divergence if the state perturbations and measurement noise are bounded a method is proposed to find a suitable solution to the algebraic riccati equation on which the design of the estimator is based the performance of the resulting sliding mode filter minimizes an upper bound on the asymptotic estimation error based on such an approach a sliding mode estimator may be designed so as to outperform the extended kalman filter in practical applications with models affected by uncertainty and strong possibly unknown non linearities as shown by means of simulations

on the design of a nonlinear adaptive variable structure derivative estimator develops a derivative estimator using variable structure system vss and adaptive techniques the proposed nonlinear adaptive variable structure derivative estimator requires little knowledge about the observed signal source and is able to handle the observed signals that are the outputs of either linear or nonlinear dynamic systems with uncertainties in the absence of system noise the uniform boundedness of the proposed derivative estimation is guaranteed by means of the adaptive control approach by virtue of vss the new derivative estimator is capable of adapting itself to the noise environment especially the unpredictable changes in system noise

adaptive control of linear time varying plants a new model reference controller structure the problem of developing a control law which can force the output of a linear time varying plant to track the output of a stable linear time invariant reference model is discussed it is shown that the standard model reference controller used for linear time invariant plants cannot guarantee zero tracking error in general when the plant is time varying a new model reference controller is proposed which guarantees stability and zero tracking error for a general class of linear time varying plants with known parameters when the time varying plant parameters are unknown but vary slowly with time it is shown that the new controller can be combined with a suitable adaptive law so that all the signals in the closed loop remain bounded for any bounded initial conditions and the tracking error is small in the mean the assumption of slow parameter variations in the adaptive case can be relaxed if some information about the frequency or the form of the fast varying parameters is available a priori such information can be incorporated in an appropriately designed adaptive law so that stability and improved tracking performance is guaranteed for a class of plants with fast varying parameters

brief paper nonlinear multivariable adaptive control using multiple models and neural networks in this paper a multivariable adaptive control approach is proposed for a class of unknown nonlinear multivariable discrete time dynamical systems by introducing a k difference operator the nonlinear terms of the system are not required to be globally bounded the proposed adaptive control scheme is composed of a linear adaptive controller a neural network based nonlinear adaptive controller and a switching mechanism the linear controller can assure boundedness of the input and output signals and the neural network nonlinear controller can improve performance of the system by using the switching scheme between the linear and nonlinear controllers it is demonstrated that improved performance and stability can be achieved simultaneously theory analysis and simulation results are presented to show the effectiveness of the proposed method

stability and accuracy of spatial approximations for wave equation tidal models amplitude and phase characteristics for numerical approximations to the shallow water wave equation are obtained for linear and quadratic finite elements for finite difference approximations for non constant bathemetry and for uneven node spacing stability is shown to require non zero friction as well as satisfaction of a courant constraint lumping is shown to reduce the courant constraint for stability while higher order and quadratic finite element approximations require a more restrictive constraint than their second order and linear finite element counterparts the amplitude of the propagation factor for stable schemes and propagating waves is seen to be independent of the courant number and type of numerical approximation although the higher order and quadratic schemes provide better propagation of the low and moderate frequency waves the highest frequency waves 2 x are better propagated by low order numerical methods

interpolating sliding mode observer for a ball and beam system abstracta principle of interpolating sliding mode observer is introduced in this paper the observer incorporates multiple linear observers through interpolation of multiple estimates which is treated as a type of adaptation the principle is then applied to the ball and beam system for observation of the slope of the beam from the measurement of the ball position the linearised model of the ball and beam system using multiple linearisation points is developed the observer dynamics implemented in matlab simulink real time workshop environment experiments conducted on the ball and beam experimental setup demonstrate excellent performance of the designed novel interpolating adaptive observer

combining beamforming and orthogonal space time block coding multiple transmit and receive antennas can be used in wireless systems to achieve high data rate communication efficient space time codes have been developed that utilize a large portion of the available capacity these codes are designed under the assumption that the transmitter has no knowledge about the channel in this work on the other hand we consider the case when the transmitter has partial but not perfect knowledge about the channel and how to improve a predetermined code so that this fact is taken into account a performance criterion is derived for a frequency nonselective fading channel and then utilized to optimize a linear transformation of the predetermined code the resulting optimization problem turns out to be convex and can thus be efficiently solved using standard methods in addition a particularly efficient solution method is developed for the special case of independently fading channel coefficients the proposed transmission scheme combines the benefits of conventional beamforming with those given by orthogonal space time block coding simulation results for a narrow band system with multiple transmit antennas and one or more receive antennas demonstrate significant gains over conventional methods in a scenario with nonperfect channel knowledge

time frequency approach to underdetermined blind source separation this paper presents a new time frequency tf underdetermined blind source separation approach based on wigner ville distribution wvd and khatri rao product to separate n non stationary sources from m m n mixtures first an improved method is proposed for estimating the mixing matrix where the negative value of the auto wvd of the sources is fully considered then after extracting all the auto term tf points the auto wvd value of the sources at every auto term tf point can be found out exactly with the proposed approach no matter how many active sources there are as long as n 2m 1 further discussion about the extraction of auto term tf points is made and finally the numerical simulation results are presented to show the superiority of the proposed algorithm by comparing it with the existing ones

lyapunov based adaptive state estimation for a class of nonlinear stochastic systems this paper is concerned with an adaptive state estimation problem for a class of nonlinear stochastic systems with unknown constant parameters these nonlinear systems have a linear in parameter structure and the nonlinearity is assumed to be bounded in a lipschitz like manner using stochastic counterparts of lyapunov stability theory we present adaptive state and parameter estimators with ultimately exponentially bounded estimator errors in the sense of mean square for both continuous time and discrete time nonlinear stochastic systems sufficient conditions are given in terms of the solvability of lmis moreover we also introduce a suboptimal design approach to optimizing the upper bound of the mean square error of parameter estimation this suboptimal design procedure is also realized by lmi computations by a martingale method we also show that the related lyapunov function has a non negative lyapunov exponent

observer matrix gain optimization for stochastic continuous time nonlinear systems it is shown that for a class of stationary stochastic nonlinear systems satisfying a global lipschitz condition the high gain observer with a constant gain matrix may guarantee an upper bound for the averaged quadratic error of state estimation the nonlinearity is assumed to be a priory known the main contribution of this paper consists in designing of a numerical procedure for the optimal gain matrix minimizing this upper bound the convergence analysis of this procedure is presented as well as an example illustrating its finite steps workability it is shown that within a neighborhood of the optimal matrix gain the others provide lower estimation performance

weighted rule based adaptive algorithm for simultaneously extracting generalized eigenvectors in this brief we consider extracting generalized eigenvectors in parallel for the generalized eigendecomposition problem the problem is formulated as an optimization problem of minimizing an unconstrained quartic cost function based on the weighted rule it is shown that the proposed weighted cost function has a unique global minimum which corresponds to the principal generalized eigenvectors in order to estimate the principal generalized eigenvector matrix efficiently we simplify the quartic cost function as a quadric one by making an appropriate approximation and then derive a fast algorithm for extracting the principal generalized eigenvector in parallel we also show the application of the proposed algorithm in blind source separation numerical simulations are performed and the results demonstrate the performance of the proposed algorithm

blind separation of instantaneous mixtures of nonstationary sources most source separation algorithms are based on a model of stationary sources however it is a simple matter to take advantage of possible nonstationarities of the sources to achieve separation this paper develops novel approaches in this direction based on the principles of maximum likelihood and minimum mutual information these principles are exploited by efficient algorithms in both the off line case via a new joint diagonalization procedure and in the on line case via a newton like procedure some experiments showing the good performance of our algorithms and evidencing an interesting feature of our methods are presented their ability to achieve a kind of super efficiency the paper concludes with a discussion contrasting separating methods for non gaussian and nonstationary models and emphasizing that as a matter of fact what makes the algorithms work is strictly speaking not the nonstationarity itself but rather the property that each realization of the source signals has a time varying envelope

group decorrelation enhanced subspace method for identifying fir mimo channels driven by unknown uncorrelated colored sources identification of finite impulse response fir and multiple input multiple output mimo channels driven by unknown uncorrelated colored sources is a challenging problem in this paper a group decorrelation enhanced subspace gdes method is presented the gdes method uses the idea of subspace decomposition and signal decorrelation more effectively than the joint diagonalization enhanced subspace jdes method previously reported in the literature the gdes method has a much better performance than the jdes method the correctness of the gdes method is proved assuming that 1 the channel matrix is irreducible and column reduced and 2 the source spectral matrix has distinct diagonal functions however the gdes method has an inherent ability to trade off between the required condition on the channel matrix and that on the source spectral matrix simulations show that the gdes method yields good results even when the channel matrix is not irreducible which is not possible at all for the jdes method

the vss approach to the model reference control of nonminimum phase linear plants a nontrivial application of the discontinuous control system theory based on filippov s solution concept is presented the problem addressed is the model reference output feedback control of linear plants with non hurwitzian numerator in the transfer function a solution in the case of known systems is presented first an error state equation whose state is accessible is then derived for the case in which the plant parameters are unknown in the case in which some bounds on the plant characteristics are supposed known a pair of discontinuous signals acting on both the plant and the model is chosen causing the error state to vanish asymptotically

robust control for nonlinear systems using passivity based robust right coprime factorization the robust control for nonlinear feedback system with unknown perturbations is discussed based on the robust right coprime factorization and passivity property firstly based on passivity property the right factorization of the perturbed plant is realized secondly a robust control scheme is proposed considering the tracking property for the nonlinear system with nominal plant and perturbed plant finally we show the connection between the passivity and robust right coprime factorization for the obtained nonlinear system a numerical example is given to show the effectiveness of the proposed design scheme

variable structure model reference adaptive control vs mrac using only input and output measurements the general case a globally asymptotically stable vs mrac for the general case of plants with arbitrary relative degree is presented it is shown that the vs approach leads to general adaptive controllers with outstanding performance the controllers use only i o measurements and are free of differentiators the results indicate that several of the usual drawbacks of conventional mracs e g unsatisfactory transient behavior lack of robustness difficulties in designing controllers for time varying or nonlinear plants can be circumvented

brief paper robust adaptive observer for nonlinear systems with unmodeled dynamics unmodeled dynamics exist in almost all applications of observers due to the impossibility of using exact and detailed models it is highly desired that the observers can dominate the effects of unmodeled dynamics independently to prevent the state estimations from diverging and to get the precise estimations based on adaptive nonlinear damping this paper presents a robust adaptive observer for multiple input multiple output nonlinear systems with unknown parameters uncertain nonlinearities disturbances and unmodeled dynamics the observer only has one adaptive parameter no matter how high the order of the system is and how many unknown parameters there are with the proposed observer neither estimating the unknown parameters or solving linear matrix inequalities is needed it is shown that the state estimation error is uniformly bounded and can be made arbitrarily small

structure identification of fuzzy model abstract the problems of structure identification of a fuzzy model are formulated a criterion for the verification of a structure is discussed using the criterion an algorithm for identifying a structure is suggested further a successive identification algorithm of the parameters is suggested the proposed methods are applied to an example

adaptive fuzzy output feedback tracking backstepping control of strict feedback nonlinear systems with unknown dead zones in this paper an adaptive fuzzy backstepping control approach is considered for a class of nonlinear strict feedback systems with unknown functions unknown dead zones and immeasurable states fuzzy logic systems are utilized to approximate the unknown nonlinear functions and a fuzzy filters state observer is designed to estimate the immeasurable states by using the adaptive backstepping recursive design technique and constructing the dead zone inverse a new adaptive fuzzy backstepping output feedback control approach is developed it is mathematically proved that all the signals of the resulting closed loop adaptive control system are semiglobally uniformly ultimately bounded and the tracking error converges to a small neighborhood of the origin by appropriate choice of design parameters the proposed approach cannot only solve the problem of the dead zones but also cancel the restrictive assumption in the previous literature that the states are all available for measurement two simulation examples are provided to show the effectiveness of the proposed approach

adaptive nn control for a class of discrete time non linear systems in this paper adaptive neural network nn control is investigated for a class of single input single output siso discrete time unknown non linear systems with general relative degree in the presence of bounded disturbances firstly the systems are transformed into a causal state space description adaptive state feedback nn control is presented based on lyapunov s stability theory then by converting the systems into a causal input output representation adaptive output feedback nn control is given finally adaptive nn observer design and observer based adaptive control are presented under the assumption of persistent excitation pe all the control schemes avoid the so called controller singularity problem in adaptive control by suitably choosing the design parameters the closed loop systems are proven to be semi globally uniformly ultimately bounded sguub simulation studies show the effectiveness of the newly proposed schemes

blind source separation using second order cyclostationary statistics this paper studies the blind source separation bss problem with the assumption that the source signals are cyclostationary identifiability and separability criteria based on second order cyclostationary statistics socs alone are derived the identifiability condition is used to define an appropriate contrast function an iterative algorithm ath2 is derived to minimize this contrast function this algorithm separates the sources even when they do not have distinct cycle frequencies

an approximate inverse system for nonminimum phase systems and its application to disturbance observer this paper proposes an approximate inverse system for nonminimum phase dynamical systems based on the least square approximation method the nonminimum phase systems are approximated by minimum phase systems the proposed formulation is applied to the disturbance observation problems for multivariable nonminimum phase systems with arbitrary relative degrees the disturbances which are assumed bounded are the combination of the external disturbances the nonlinearities and the model uncertainties of the system the estimation error of the disturbances is controlled by the design parameters furthermore the accuracy of the estimation also depends on the frequencies of the disturbances simulation results show the effectiveness of the proposed method

output feedback stabilization of uncertain dynamical systems given an uncertain dynamical system with a linear nominal part matched uncertainties and a measured output a sufficient condition is given for the existence of an output feedback stabilizing controller for the general multiinput multioutput case checking the sufficient condition involves the selection of a matrix f for the special class of single input single output systems f is a positive or negative scalar and it is shown that if the nominal system transfer function is strictly positive real then an output feedback stabilizing controller does indeed exist

solution of the shallow water equations by least squares collocation a method of solution of the shallow water equations in their primitive form that yield solutions free of numerical oscillations is presented the method is based on the least squares collocation concept the focus is on the accuracy of the solution procedure comparison of the numerical solution with analytic solutions for problems of irregular geometry are presented key features are the ability of the method to solve the equations in an irregular domain with a completely orthogonal computational mesh and the inherent nondissipative noise control of the temporal and spatial approximations

some response characteristics of finite element tidal models abstract small scale error can arise in finite element tidal models from different aspects of the spatial discretization one source is incompatibility in smoothness of interpolation of the pressure and velocity fields noise of this type can be eliminated completely by using stokes helmholtz potentials to represent the velocity and interpolating these potentials with the same smoothness as is given to the pressure another source is inhomogeneous triangulation in which the number of links is not the same at all nodes noise of this type can be suppressed by using a high resolution grid the resolution in question being that of the response rather than of the domain these points are illustrated by examining the response characteristics of idealized models it is found that in the models considered small scale noise can be ascribed to resonance anomalies associated with the method of spatial discretization in this analysis the time is continuous but some remarks are appended on the effect of time discretization and a formal proof is given of the time stepping stability of a general discretized form of laplace s tidal equations

a novel estimation algorithm based on data and low order models for virtual unmodeled dynamics in this paper the challenging issue of estimating virtual unmodeled dynamics is addressed a novel estimation algorithm based on historical data and the output of low order approximation models for virtual un modeled dynamics is presented in particular the virtual un modeled dynamics are decomposed into known and unknown parts where only the unknown part is to be estimated the method effectively avoids the need to use the unknown control input directly and enables the estimation of the un modeled dynamics with a relatively simple algorithm moreover it is shown that the proposed algorithm overcomes the difficulty in obtaining the control solutions caused by the fact that the controller input is embedded in un modeled dynamics finally simulation studies are presented to demonstrate the effectiveness of the proposed method

matrix group algorithm via improved whitening process for extracting statistically independent sources from array signals this paper addresses the problem of blind separation of multiple independent sources from observed array output signals the main contributions in this paper include an improved whitening scheme for estimation of signal subspace a novel biquadratic contrast function for extraction of independent sources and an efficient alterative method for joint implementation of a set of approximate diagonalization structural matrices specifically an improved whitening scheme is first developed by estimating the signal subspace jointly from a set of diagonalization structural matrices based on the proposed cyclic maximizer of an interesting cost function moreover the globally asymptotical convergence of the proposed cyclic maximizer is analyzed and proved next a novel biquadratic contrast function is proposed for extracting one single independent component from a slice matrix group of any order cumulant of the array signals in the presence of temporally white noise a fast fixed point algorithm that is a cyclic minimizer is constructed for searching a minimum point of the proposed contrast function the globally asymptotical convergence of the proposed fixed point algorithm is analyzed then multiple independent components are obtained by using repeatedly the proposed fixed point algorithm for extracting one single independent component and the orthogonality among them is achieved by the well known qr factorization the performance of the proposed algorithms is illustrated by simulation results and is compared with three related blind source separation algorithms

a novel sliding mode observer for state and fault estimation in systems not satisfying matching and minimum phase conditions abstract in this paper a novel sliding mode observer is proposed for systems that do not satisfy common conditions required for fault estimation in particular the so called matching condition and minimum phase condition other works attempt to circumvent the matching condition by using multiple observers in cascade or higher order sliding modes whilst the minimum phase condition can be relaxed at the cost of some faults and the states being not estimated or the fault estimation being corrupted by the fault time derivative or other dynamics for the proposed scheme in this paper only one observer is required the minimum phase condition is relaxed to detectability and the matching condition is greatly simplified to a condition related to the system dimensions which makes the scheme also applicable to the case where there are less outputs than faults finally a simulation example is conducted to verify the effectiveness of the proposed observer

a blind source separation technique using second order statistics separation of sources consists of recovering a set of signals of which only instantaneous linear mixtures are observed in many situations no a priori information on the mixing matrix is available the linear mixture should be blindly processed this typically occurs in narrowband array processing applications when the array manifold is unknown or distorted this paper introduces a new source separation technique exploiting the time coherence of the source signals in contrast with other previously reported techniques the proposed approach relies only on stationary second order statistics that are based on a joint diagonalization of a set of covariance matrices asymptotic performance analysis of this method is carried out some numerical simulations are provided to illustrate the effectiveness of the proposed method

equivariant adaptive source separation source separation consists of recovering a set of independent signals when only mixtures with unknown coefficients are observed this paper introduces a class of adaptive algorithms for source separation that implements an adaptive version of equivariant estimation and is henceforth called equivariant adaptive separation via independence easi the easi algorithms are based on the idea of serial updating this specific form of matrix updates systematically yields algorithms with a simple structure for both real and complex mixtures most importantly the performance of an easi algorithm does not depend on the mixing matrix in particular convergence rates stability conditions and interference rejection levels depend only on the normalized distributions of the source signals closed form expressions of these quantities are given via an asymptotic performance analysis the theme of equivariance is stressed throughout the paper the source separation problem has an underlying multiplicative structure the parameter space forms a matrix multiplicative group we explore the favorable consequences of this fact on implementation performance and optimization of easi algorithms

projection pursuit based method for blind separation of nonnegative sources this paper presents a projection pursuit pp based method for blind separation of nonnegative sources first the available observation matrix is mapped to construct a new mixing model in which the inaccessible source matrix is normalized to be column sum to 1 then the pp method is proposed to solve this new model where the mixing matrix is estimated column by column through tracing the projections to the mapped observations in specified directions which leads to the recovery of the sources the proposed method is much faster than chan s method which has similar assumptions to ours due to the usage of optimal projection it is also more advantageous in separating cross correlated sources than the independence and uncorrelation based methods as it does not employ any statistical information of the sources furthermore the new method does not require the mixing matrix to be nonnegative simulation results demonstrate the superior performance of our method

a sliding mode observer for infinitely unobservable descriptor systems in existing work of sliding mode observers smos for descriptor systems a necessary condition is that the system must be infinitely observable this paper presents a scheme that circumvents that condition by reformulating the system as a reduced order system where certain structures in the system matrix are manipulated and certain states are treated as unknown inputs following that an smo is implemented on the reduced order system where state and fault estimation can be achieved necessary and sufficient conditions of this scheme are also presented finally a simulation example shows the effectiveness of the proposed scheme

stochastic stability of the discrete time extended kalman filter the authors analyze the error behavior for the discrete time extended kalman filter for general nonlinear systems in a stochastic framework in particular it is shown that the estimation error remains bounded if the system satisfies the nonlinear observability rank condition and the initial estimation error as well as the disturbing noise terms are small enough this result is verified by numerical simulations for an example system

an improved estimation method for unmodeled dynamics based on anfis and its application to controller design by representing nonlinear systems as a combination of linear part and unmodeled dynamics in this paper an improved estimation algorithm using an adaptive neuro fuzzy inference system anfis for unmodeled dynamics is presented at first the unmodeled dynamics is divided into two parts using the differential expansion of the control input at the last time instant then the two parts are estimated by the anfis it has been shown that the proposed algorithm overcomes the problem that the unknown control input is embedded in unmodeled dynamics which makes the true value of unmodeled dynamics difficult obtain moreover the method improves the precision of the estimation of unmodeled dynamics second under the assumption that the growth rate of unmodeled dynamics does not exceed its input vector the one to one mapping and regularization technique are adopted to deal with the input and output data and the unmodeled dynamics respectively as a result the data vector can be guaranteed to lie inside a compact set which ensures the use of the universal approximation property of the anfis on the other hand it has been shown that datum of a system can be fully used to obtain the parameters centers widths in membership functions and the network connection weights in the anfis by offline training these parameters are tuned online to improve the estimation convergence rate of the unmodeled dynamics the effectiveness of the proposed estimation method is illustrated by comparing it with the simulation results that are obtained from the other existing methods finally the proposed estimation method is applied to the nonlinear switching control design both simulation and theoretical analysis have confirmed that the nonlinear switching control which adopts the proposed estimation method cannot only guarantee the stability and convergence of the system but can exhibit a desired dynamic performance for the closed loop system as well

an lmi approach for designing sliding mode observers this paper presents a method to design sliding mode observers for a class of uncertain systems using linear matrix inequalities the objective is to exploit the degrees of freedom available in the design which have hitherto been ignored because of the lack of a tractable solution framework the relationship between the linear component of the sliding mode observer and a particular sub optimal observer arising from classical linear quadratic gaussian lqg theory is demonstrated this helps motivate how the design weighting matrices inherent in the method may be chosen in practice it will also be shown how the weighting matrices affect the dynamics of the sliding motion furthermore using pole placement and pole clustering methods the poles of the sliding motion can be forced to lie in a specified region in the complex plane so that the performance of the sliding motion can be tuned this paper will also present a more general solution where the eigenvalues of the linear part of the observer are forced

detection and estimation in sensor arrays using weighted subspace fitting the problem of signal parameter estimation of narrowband emitter signals impinging on an array of sensors is addressed a multidimensional estimation procedure that applies to arbitrary array structures and signal correlation is proposed the method is based on the recently introduced weighted subspace fitting wsf criterion and includes schemes for both detecting the number of sources and estimating the signal parameters a gauss newton type method is presented for solving the multidimensional wsf and maximum likelihood optimization problems the global and local properties of the search procedure are investigated through computer simulations most methods require knowledge of the number of coherent noncoherent signals present a scheme for consistently estimating this is proposed based on an asymptotic analysis of the wsf cost function the performance of the detection scheme is also investigated through simulations

parallelotopic and practical observers for non linear uncertain systems for a class of dynamical systems with uncertain non linear terms considered as unknown inputs we give suffcient conditions for observability we show also that there does not exist any exact observer independent of the unknown inputs under the additional assumption that the uncertainty is bounded we build practical observers whose error converges exponentially towards an arbitrarily small neighbourhood of the origin under the hypothesis that bounds are available for the uncertain terms we build parallelotopic observers providing time varying bounds for the state variables even when the system is not observable for unknown inputs these results are illustrated with a biological model of a structured population

optimal transmitter eigen beamforming and space time block coding based on channel correlations optimal transmitter designs obeying the water filling principle are well documented and widely applied when the propagation channel is deterministically known and regularly updated at the transmitter because channel state information csi may be costly or impossible to acquire in rapidly varying wireless environments we develop in this paper statistical water filling approaches for stationary random fading channels these approaches require only knowledge of the channel correlations that do not necessitate frequent updates and can be easily acquired applied to a multiple transmit antenna paradigm our optimal transmitter design turns out to be an eigen beamformer with multiple beams pointing to orthogonal directions along the eigenvectors of the channel s correlation matrix and with proper power loading across the beams the optimality pertains to minimizing a tight bound on the symbol error rate the resulting loaded eigen beamforming outperforms not only the equal power allocation across all antennas but also the conventional beamformer that transmits the available power along the strongest direction coupled with orthogonal space time block codes two dimensional 2 d eigen beamforming emerges as a more attractive choice than conventional one dimensional 1 d beamforming with uniformly better performance without rate reduction and without complexity increase

comparative study of non linear state observation techniques abstract this paper contains a comparative study of four techniques for observing the slates of non linear systems the first technique examined is inspired by bestle and zeitz and icrener and respondek in this method a non linear transformation is found that brings the system into a canonical form from where observer design is facilitated the second technique is attributable to thau in this method the error between the system s true state and the output of the observer is shown to be asymptotically convergent to zero provided that an additional assumption is valid the third technique is due to baumann and rugh in this method extended or pseudolinearization of the error differential equation about a family of equilibrium points results in an observer design such that the eigenvalues of the linearized error equation are locally invariant finally techniques from variable structure systems are utilized to design an observer that yields an exponentially decaying error like thau s observer but unl

a combined direct indirect and variable structure method for robust adaptive control direct methods and indirect methods for the adaptive control of linear plants with unknown parameters are currently well known methods for combining direct and indirect methods have also been suggested in the paper the latter method is combined with the variable structure method new adaptive laws are suggested which overcome the principal drawbacks of the variable structure method while the direct and indirect components of the new method assure stability the variable structure component improves the transient response of the system simulation results are presented to show that the overall system is asymptotically stable in the ideal case and exhibits robustness and improved performance in the presence of perturbations

a receding horizon kalman fir filter for linear continuous time systems a receding horizon kalman finite impulse response fir filter is suggested for continuous time systems combining the kalman filter with the receding horizon strategy in the suggested filter the horizon initial state is assumed to be unknown it can always be obtained irrespective of unknown information on the horizon initial state the filter may be the first stochastic fir form for continuous time systems that may have many good inherent properties the suggested filter can be represented in an iterative form and also in a standard fir form the suggested filter turns out to be a remarkable deadbeat observer the validity of the suggested filter is illustrated by numerical examples

perturbed nonlinear systems control using extended robust right coprime factorisation abstractin this paper extended operator based robust right coprime factorisation is investigated for dealing with a class of nonlinear systems with unknown bounded perturbations first a new kind of operator is introduced by which operator based right coprime factorisation approach is extended to consider the perturbed nonlinear systems by regulating the exponent of the proposed operator a broader class of nonlinear systems can be handled using the extended right coprime factorisation approach second for guaranteeing robust stability of the perturbed nonlinear systems feasible design schemes based on some sufficient conditions are discussed which can reduce complicated calculation in control processing using a different unimodular operator finally a simulation example is involved to illustrate the proposed design scheme for confirming the effectiveness of the proposed method

smoothing neural network for constrained non lipschitz optimization with applications in this paper a smoothing neural network snn is proposed for a class of constrained non lipschitz optimization problems where the objective function is the sum of a nonsmooth nonconvex function and a non lipschitz function and the feasible set is a closed convex subset of using the smoothing approximate techniques the proposed neural network is modeled by a differential equation which can be implemented easily under the level bounded condition on the objective function in the feasible set we prove the global existence and uniform boundedness of the solutions of the snn with any initial point in the feasible set the uniqueness of the solution of the snn is provided under the lipschitz property of smoothing functions we show that any accumulation point of the solutions of the snn is a stationary point of the optimization problem numerical results including image restoration blind source separation variable selection and minimizing condition number are presented to illustrate the theoretical results and show the efficiency of the snn comparisons with some existing algorithms show the advantages of the snn

a new state observer for perspective systems in this note we consider the problem of estimating the state of a class of perspective systems the problem can be converted into the observation of a dynamical system with nonlinearities a new discontinuous state observer which is motivated by the sliding mode control method and adaptive techniques is proposed for the obtained dynamical system the assumptions are reasonable and the convergence conditions are intuitive and have apparently physical interpretations the attraction of the new method is that the algorithm is very simple and easy to be implemented and it is robust to measurement noises further minor a priori knowledge of the system is required in the new formulation simulation results show the superiority of the new method to the traditional ones

a discrete least squares collocation method for two dimensional nonlinear time dependent partial differential equations abstract in this paper we develop regularized discrete least squares collocation and finite volume methods for solving two dimensional nonlinear time dependent partial differential equations on irregular domains the solution is approximated using tensor product cubic spline basis functions defined on a background rectangular interpolation mesh which leads to high spatial accuracy and straightforward implementation and establishes a solid base for extending the computational framework to three dimensional problems a semi implicit time stepping method is employed to transform the nonlinear partial differential equation into a linear boundary value problem a key finding of our study is that the newly proposed mesh free finite volume method based on circular control volumes reduces to the collocation method as the radius limits to zero both methods produce a large constrained least squares problem that must be solved at each time step in the advancement of the solution we have found that regularization yields a relatively well conditioned system that can be solved accurately using qr factorization an extensive numerical investigation is performed to illustrate the effectiveness of the present methods including the application of the new method to a coupled system of time fractional partial differential equations having different fractional indices in different irregularly shaped regions of the solution domain

brief robustness of high gain observer based nonlinear controllers to unmodeled actuators and sensors we investigate the robust stabilization of a class of nonlinear systems in the presence of unmodeled actuator and sensor dynamics we show that given any globally bounded stabilizing state feedback control the closed loop system performance can be recovered by a sufficiently fast high gain observer in the presence of sufficiently fast actuator and sensor dynamics the performance recovery includes recovery of exponential stability of the origin the region of attraction and state trajectories moreover it is shown that the sensor dynamics should be sufficiently faster than the observer dynamics a restriction that does not apply to the actuator dynamics