parameterization and adaptive control of multivariable noncanonical t s fuzzy systems this paper conducts a new study for adaptive takagi sugeno t s fuzzy approximation based control of multi input and multi output mimo noncanonical form nonlinear systems canonical form nonlinear systems have explicit relative degree structures whose approximation models can be directly used to derive desired parameterized controllers noncanonical form nonlinear systems usually do not have such a feature nor do their approximation models which are also in noncanonical forms this paper shows that it is desirable to reparameterize noncanonical form t s fuzzy system models with smooth membership functions for adaptive control and such system reparameterization can be realized using relative degrees a concept yet to be studied for mimo noncanonical form t s fuzzy systems this paper develops an adaptive feedback linearization scheme for control of such general system models with uncertain parameters by first deriving various relative degree structures and normal forms for such systems then a reparameterization procedure is developed for such system models based on which adaptive control designs are derived with desired stability and tracking properties analyzed a detailed example is presented with simulation results to show the new control design procedure and desired control system performance

on the interpretation and identification of dynamic takagi sugeno fuzzy models dynamic takagi sugeno fuzzy models are not always easy to interpret in particular when they are identified from experimental data it is shown that there exists a close relationship between dynamic takagi sugeno fuzzy models and dynamic linearization when using affine local model structures which suggests that a solution to the multiobjective identification problem exists however it is also shown that the affine local model structure is a highly sensitive parametrization when applied in transient operating regimes due to the multiobjective nature of the identification problem studied here special considerations must be made during model structure selection experiment design and identification in order to meet both objectives some guidelines for experiment design are suggested and some robust nonlinear identification algorithms are studied these include constrained and regularized identification and locally weighted identification their usefulness in the present context is illustrated by examples

the cardinality balanced multi target multi bernoulli filter and its implementations it is shown analytically that the multitarget multibernoulli member recursion proposed by mahler has a significant bias in the number of targets to reduce the cardinality bias a novel multibernoulli approximation to the multi target bayes recursion is derived under the same assumptions as the member recursion the proposed recursion is unbiased in addition a sequential monte carlo smc implementation for generic models and a gaussian mixture gm implementation for linear gaussian models are proposed the latter is also extended to accommodate mildly nonlinear models by linearization and the unscented transform

a consistent metric for performance evaluation of multi object filters the concept of a miss distance or error between a reference quantity and its estimated controlled value plays a fundamental role in any filtering control problem yet there is no satisfactory notion of a miss distance in the well established field of multi object filtering in this paper we outline the inconsistencies of existing metrics in the context of multi object miss distances for performance evaluation we then propose a new mathematically and intuitively consistent metric that addresses the drawbacks of current multi object performance evaluation metrics

adaptive control of mimo time varying systems with indicator function based parametrization abstract this paper studies a new solution framework for adaptive control of a class of mimo time varying systems with indicator function based parametrization motivated by a general discrete time mimo takagi sugeno t s fuzzy system model in an input output form with unknown parameters an indicator membership function based parametrization has some favorable capacity to deal with certain large parameter variations a new discrete time mimo system prediction model is derived for approximating a nonlinear dynamic system and its system properties are clarified an adaptive control scheme is developed with desired controller parametrization and stable parameter estimation for control of such uncertain mimo time varying systems a control singularity problem is addressed and the closed loop stability and output tracking properties are analyzed this work provides a new method for multivariable t s fuzzy system modeling and adaptive control an illustrative example and simulation results are presented to demonstrate the proposed novel concepts and to verify the desired adaptive control system performance

adaptive fuzzy logic control of discrete time dynamical systems the objective of this paper is to achieve tracking control of a class of unknown nonlinear dynamical systems using a discrete time fuzzy logic controller flc designing a discrete time flc is significant because almost all flcs are implemented on digital computers we present a repeatable design algorithm and a stability proof for an adaptive fuzzy logic controller that uses basis vectors based on the fuzzy system unlike most standard adaptive control approaches which use basis vectors depending on the unknown plant e g a tediously computed regression matrix an e modification sort of approach to adapt the fuzzy system parameters was selected with mild assumptions on the class of discrete time nonlinear systems this adaptive fuzzy logic controller guarantees uniform ultimate boundedness of the closed loop signals and that the controller achieves tracking in fact the fuzzy system designed is a model free universal fuzzy controller that works for a more general class of nonlinear systems some new passivity properties of fuzzy logic systems are introduced

global solutions of the fast diffusion equation with gradient absorption terms abstract we investigate the existence of nonnegative weak solutions to the problem u t u m u p in r n 0 with 1 2 n m 1 it will be proved that i when 1 p 2 if the initial datum u 0 d r n then there exists a solution ii when 1 p 2 m n n 1 if the initial datum u 0 x is a bounded and nonnegative measure then the solution exists iii when 2 m n n 1 p 2 if the initial datum is a dirac mass then the solution does not exist we also study the large time behavior of the l 1 norm of solutions for 1 p 2 m n n 1 and the large time behavior of t 1 u t e c t l for 2 m n n 1 p 2

mixed feedforward feedback based adaptive fuzzy control for a class of mimo nonlinear systems this paper proposes a mixed feedforward feedback ffb based adaptive fuzzy controller design for a class of multiple input multiple output mimo uncertain nonlinear systems by integrating both feedforward and feedback compensation we introduce the ffb based fuzzy controller composed of a feedforward fuzzy compensator and a robust error feedback compensator to achieve a forward compensation of uncertainties the feedforward fuzzy compensator takes the desired commands as premise variables of fuzzy rules and adaptively adjusts the consequent part from an error measure meanwhile the feedback controller part is constructed based on hinfin control techniques and nonlinear damping design then the attenuation of both disturbances and estimated fuzzy parametric errors is guaranteed from a linear matrix inequality lmi based gain design the main advantages are i a simpler architecture for implementation is provided and ii the typical boundedness of assumption on fuzzy universal approximation errors is not required finally an inverted pendulum system and a two link robot are taken as application examples to show the expected performance

essential orders and the non linear decoupling problem abstract the concept of essential orders has recently been introduced into linear system theory and has been shown to be useful in the solution of static and dynamic decoupling problems commauit et al 1986 de luca et al 1985 this paper presents a non trivial extension to the class of non linear affine systems of some of these definitions and results it is shown that these invariant integers give the minimal order for a regular dynamic compensator that decouples a right invertible system

a cphd approximation based on a discrete gamma cardinality model the cardinalized probability hypothesis density cphd filter has become one of the most acclaimed algorithms for multi target bayesian filtering due to its ability to accurately estimate the number of objects and the object states in tracking scenarios affected by clutter the cphd filter generalizes the probabilistic hypothesis density filter by jointly propagating the first order multi target moment intensity function along with the entire probability distribution on the number of targets cardinality distribution in general the cphd recursion is computationally intractable however successful approximations have been devised with reported computational complexity dominated by mathcal o m 3 operations per filtering iteration where m is the number of measurements room for improvement was originally acknowledged by mahler who conceived the idea of approximating the cardinality distribution by two parameter distributions in this paper we further explore this idea to provide an efficient approximation of the cphd filter where the cardinality distribution is modeled as a discretized gamma distribution experiments show that the resulting filter is less computationally complex than the standard implementation of the cphd filter but shows similar cardinality accuracy and variance

control of discrete time nonlinear systems with a time varying structure in this paper we present a control methodology for a class of discrete time nonlinear systems that depend on a possibly exogenous scheduling variable this class of systems consists of an interpolation of nonlinear dynamic equations in strict feedback form and it may represent systems with a time varying nonlinear structure moreover this class of systems is able to represent some cases of gain scheduling control takagi sugeno fuzzy systems as well as input output realizations of nonlinear systems which are approximated via localized linearizations we present two control theorems one using what we call a global approach akin to traditional backstepping and a local approach our main result where backstepping is again used but the control law is an interpolation of local control terms an aircraft wing rock regulation problem with varying angle of attack is used to illustrate and compare the two approaches

adaptive fuzzy output tracking control of mimo nonlinear uncertain systems in this paper the adaptive fuzzy tracking control problem is discussed for a class of uncertain multiple input multiple output mimo nonlinear systems with the block triangular structure the fuzzy logic systems are used to approximate the unknown nonlinear functions by using the backstepping technique the adaptive fuzzy tracking control design scheme is developed which has minimal learning parameterizations the adaptive fuzzy tracking controllers guarantee that the outputs of systems converge to a small neighborhood of the reference signals and all the signals in the closed loop system are semiglobally uniformly ultimately bounded two examples are used to show the effectiveness of the approach

analytic implementations of the cardinalized probability hypothesis density filter the probability hypothesis density phd recursion propagates the posterior intensity of the random finite set rfs of targets in time the cardinalized phd cphd recursion is a generalization of the phd recursion which jointly propagates the posterior intensity and the posterior cardinality distribution in general the cphd recursion is computationally intractable this paper proposes a closed form solution to the cphd recursion under linear gaussian assumptions on the target dynamics and birth process based on this solution an effective multitarget tracking algorithm is developed extensions of the proposed closed form recursion to accommodate nonlinear models are also given using linearization and unscented transform techniques the proposed cphd implementations not only sidestep the need to perform data association found in traditional methods but also dramatically improve the accuracy of individual state estimates as well as the variance of the estimated number of targets when compared to the standard phd filter our implementations only have a cubic complexity but simulations suggest favorable performance compared to the standard joint probabilistic data association jpda filter which has a nonpolynomial complexity

an approach to fuzzy control of nonlinear systems stability and design issues presents a design methodology for stabilization of a class of nonlinear systems first the authors represent a nonlinear plant with a takagi sugeno fuzzy model then a model based fuzzy controller design utilizing the concept of the so called parallel distributed compensation is employed the main idea of the controller design is to derive each control rule so as to compensate each rule of a fuzzy system the design procedure is conceptually simple and natural moreover the stability analysis and control design problems can be reduced to linear matrix inequality lmi problems therefore they can be solved efficiently in practice by convex programming techniques for lmis the design methodology is illustrated by application to the problem of balancing and swing up of an inverted pendulum on a cart

a lagrangian relaxation algorithm for multidimensional assignment problems arising from multitarget tracking the central problem in multitarget tracking is the data association problem of partitioning the observations into tracks in some optimal way so that an accurate estimate of the true tracks can be recovered this work considers what is perhaps the simplest multitarget tracking problem in a setting where the issues are easily delineated i e straight lines in two dimensional space time with an error component introduced into the observations a multidimensional assignment problem is formulated using gating techniques to introduce sparsity into the problem and filtering techniques to generate tracks which are then used to score each assignment of a collection of observations to a filtered track problem complexity is further reduced by decomposing the problem into disjoint components which can then be solved independently a recursive lagrangian relaxation algorithm is developed to obtain high quality suboptimal solutions in real time the algorithms are however applicable to a large class of sparse mul

note on a nonlinear diffusion system with convection the critical exponents are established for a nonlinear diffusion system with convection which are described clearly by the signs of two parameters solving the so called characteristic algebraic system it is proved that the convection plays an important role in determining the critical properties of solutions in the balance case this greatly improves the authors previous paper for the same model

joint detection and estimation of multiple objects from image observations the problem of jointly detecting multiple objects and estimating their states from image observations is formulated in a bayesian framework by modeling the collection of states as a random finite set analytic characterizations of the posterior distribution of this random finite set are derived for various prior distributions under the assumption that the regions of the observation influenced by individual objects do not overlap these results provide tractable means to jointly estimate the number of states and their values from image observations as an application we develop a multi object filter suitable for image observations with low signal to noise ratio snr a particle implementation of the multi object filter is proposed and demonstrated via simulations

very singular solutions to a nonlinear parabolic equation with absorption ii uniqueness we prove the uniqueness of the very singular solution to a hamilton jacobi equation with absorption we mention thatthe very singular solutions play an important role in the description of the large time behavior of the solutions

distributed fusion with multi bernoulli filter based on generalized covariance intersection in this paper we propose a distributed multiobject tracking algorithm through the use of multi bernoulli mb filter based on generalized covariance intersection g ci our analyses show that the g ci fusion with two mb posterior distributions does not admit an accurate closed form expression to solve this problem we first approximate the fused posterior as the unlabeled version of delta generalized labeled mb distribution referred to as generalized mb gmb distribution then to allow the subsequent fusion with another mb posterior distribution e g fusion with a third sensor node in the sensor network or fusion in the feedback working mode we further approximate the fused gmb posterior distribution as an mb distribution which matches its first order statistical moment the proposed fusion algorithm is implemented using sequential monte carlo technique and its performance is highlighted by numerical results

hierarchical dirichlet processes we consider problems involving groups of data where each observation within a group is a draw from a mixture model and where it is desirable to share mixture components between groups we assume that the number of mixture components is unknown a priori and is to be inferred from the data in this setting it is natural to consider sets of dirichlet processes one for each group where the well known clustering property of the dirichlet process provides a nonparametric prior for the number of mixture components within each group given our desire to tie the mixture models in the various groups we consider a hierarchical model specifically one in which the base measure for the child dirichlet processes is itself distributed according to a dirichlet process such a base measure being discrete the child dirichlet processes necessarily share atoms thus as desired the mixture models in the different groups necessarily share mixture components we discuss representations of hierarchical dirichlet processes

regional variance for multi object filtering recent progress in multi object filtering has led to algorithms that compute the first order moment of multi object distributions based on sensor measurements the number of targets in arbitrarily selected regions can be estimated using the first order moment in this work we introduce explicit formulae for the computation of the second order statistic on the target number the proposed concept of regional variance quantifies the level of confidence on target number estimates in arbitrary regions and facilitates information based decisions we provide algorithms for its computation for the probability hypothesis density phd and the cardinalized probability hypothesis density cphd filters we demonstrate the behaviour of the regional statistics through simulation examples

multiobjective identification of takagi sugeno fuzzy models the problem of identifying the parameters of the constituent local linear models of takagi sugeno fuzzy models is considered in order to address the tradeoff between global model accuracy and interpretability of the local models as linearizations of a nonlinear system two multiobjective identification algorithms are studied particular attention is paid to the analysis of conflicts between objectives and we show that such information can be easily computed from the solution of the multiobjective optimization this information is useful to diagnose the model and tune the weighting priorities of the multiobjective optimization moreover the result of the conflict analysis can be used as a constructive tool to modify the fuzzy model structure including membership functions in order to meet the multiple objectives simple illustrative examples as well as experimental results show the usefulness of the method

large time behaviour of the solutions of a semilinear parabolic equation in rn abstract the asymptotic behaviour as t tends to of the solution of u t u u u p 1 0 in r n r p 1 was studied it was proved that the behaviour depends strongly on the sign of n 2 n p and also on the rate of decay of the admissible initial data u 0 x as x tends to

adaptive control of a class of slowly time varying plants abstract an adaptive control system of the type considered earlier by the author which is designed to be stable under the assumption that the unknown plant parameters are constant is shown to maintain stability when the plant parameters are slowly time varying

stable multi input multi output adaptive fuzzy neural control in this letter stable direct and indirect adaptive controllers are presented that use takagi sugeno t s fuzzy systems 1985 conventional fuzzy systems or a class of neural networks to provide asymptotic tracking of a reference signal vector for a class of continuous time multi input multi output mimo square nonlinear plants with poorly understood dynamics the direct adaptive scheme allows for the inclusion of a priori knowledge about the control input in terms of exact mathematical equations or linguistics while the indirect adaptive controller permits the explicit use of equations to represent portions of the plant dynamics we prove that with or without such knowledge the adaptive schemes can learn how to control the plant provide for bounded internal signals and achieve asymptotically stable tracking of the reference inputs we do not impose any initialization conditions on the controllers and guarantee convergence of the tracking error to zero

source type solutions of the porous media equation with absorption the fast diffusion case in this paper we consider the cauchy problem u t u m u p on s t r n 0 t u x 0 0 x r n 0 where n 1 p 1 and 0 m 1

model reference adaptive and nonadaptive control of linear time varying plants we address the adaptive and nonadaptive model reference control problem for a class of multivariable linear time varying plants namely index invariant ones we show that under appropriate controllability and observability conditions this class of plants admits a fractional description in terms of polynomial differential operators and as such allows for a polynomial equation based controller design we also show that for a model reference control objective the controller can be designed by solving a set of algebraic equations further when the plant parameters are only partially known we employ a gradient based adaptive law with projection and normalization to update the controller parameters and establish the stability and tracking properties of adaptive closed loop plant finally we present a simple example to illustrate the design and realization of both the adaptive and nonadaptive control laws

general siso takagi sugeno fuzzy systems with linear rule consequent are universal approximators takagi sugeno ts fuzzy systems have been employed as fuzzy controllers and fuzzy models in successfully solving difficult control and modeling problems in practice virtually all the ts fuzzy systems use linear rule consequent at present there exist no results qualitative or quantitative to answer the fundamentally important question that is especially critical to ts fuzzy systems as fuzzy controllers and models are ts fuzzy systems with linear rule consequent universal approximators if the answer is yes then how can they be constructed to achieve prespecified approximation accuracy and what are the sufficient renditions on systems configuration in this paper we provide answers to these questions for a general class of single input single output siso fuzzy systems that use any type of continuous input fuzzy sets ts fuzzy rules with linear consequent and a generalized defuzzifier containing the widely used centroid defuzzifier as a special case we first constructively prove that this general class of siso ts fuzzy systems can uniformly approximate any polynomial arbitrarily well and then prove by utilizing the weierstrass approximation theorem that the general ts fuzzy systems can uniformly approximate any continuous function with arbitrarily high precision furthermore we have derived a formula as part of sufficient conditions for the fuzzy approximation that can compute the minimal upper bound on the number of input fuzzy sets and rules needed for any given continuous function and prespecified approximation error bound an illustrative numerical example is provided

adaptive control of time varying linear systems the authors present a global bounded input bounded state stability theory for a class of continuously adapting controllers applied to time varying linear systems this gives theoretical support to the application of many of the algorithms used in practice a key feature of the analysis is that no persistence of excitation requirement is needed

convergence results for the particle phd filter bayesian single target tracking techniques can be extended to a multiple target environment by viewing the multiple target state as a random finite set but evaluating the multiple target posterior distribution is currently computationally intractable for real time applications a practical alternative to the optimal bayes multitarget filter is the probability hypothesis density phd filter which propagates the first order moment of the multitarget posterior instead of the posterior distribution itself it has been shown that the phd is the best fit approximation of the multitarget posterior in an information theoretic sense the method avoids the need for explicit data association as the target states are viewed as a single global target state and the identities of the targets are not part of the tracking framework sequential monte carlo approximations of the phd using particle filter techniques have been implemented showing the potential of this technique for real time tracking applications this paper presents mathematical proofs of convergence for the particle filtering algorithm and gives bounds for the mean square error

void probabilities and cauchy schwarz divergence for generalized labeled multi bernoulli models the generalized labeled multi bernoulli glmb is a family of tractable models that alleviates the limitations of the poisson family in dynamic bayesian inference of point processes in this paper we derive closed form expressions for the void probability functional and the cauchy schwarz divergence for glmbs the proposed analytic void probability functional is a necessary and sufficient statistic that uniquely characterizes a glmb while the proposed analytic cauchy schwarz divergence provides a tractable measure of similarity between glmbs we demonstrate the use of both results on a partially observed markov decision process for glmbs with cauchy schwarz divergence based reward and void probability constraint

stable indirect adaptive control based on discrete time t s fuzzy model this paper presents an indirect adaptive fuzzy control scheme for uncertain nonlinear asymptotically stable plants a discrete time t s fuzzy input output model is employed to approximate the unknown plant dynamics the t s fuzzy model is fed with its own states which are indeed its past outputs rather than the measurements from the plants entirely based on this model a feedback linearization control law is designed by using the model structure model parameters and model states and then applied to control the model and the plant premise and consequent parameters of the model are updated on line by gradient descent algorithm and recursive least square estimation method using plant output measurements stability analysis shows that if there the model structure is accurate the adaptive controller achieves the bounded tracking error and boundedness of all the closed loop signals in the ideal case where the signals are persistently exciting during the model parameter adaptation a perfect asymptotic tracking is ensured the effectiveness of the method is verified by simulation application to siso and mimo example plants

the cauchy problem for ut δu u q with q a positive real number the nonlinear partial differential equation in the title of the paper arises in the study of the growth of surfaces in that context it is known as the generalized deterministic kpz equation the paper is concerned with the initial value problem for the equation under the assumption that the initial data function is bounded and continuous results on the existence uniqueness and regularity of solutions are obtained

continuous discrete multiple target filtering pmbm phd and cphd filter implementations this article develops models and algorithms for continuous discrete multiple target filtering in which the multi target system is modelled in continuous time and measurements are available at discrete time steps in order to do so this paper first proposes a statistical model for multi target appearance dynamics and disappearance in continuous time based on continuous time birth death processes and stochastic differential equations the multitarget state is observed at known time instants based on the standard measurement model and the objective is to compute the distribution of the multi target state at these time steps for the wiener velocity model we derive a closed form formula to obtain the best gaussian poisson point process fit to the birth density based on kullback leibler minimisation the resulting discretised model gives rise to the continuous discrete gaussian poisson multi bernoulli mixture pmbm filter the continuous discrete gaussian mixture probability hypothesis density phd filter and the continuous discrete gaussian mixture cardinality phd cphd filter the proposed filters are specially useful for multi target estimation when the time interval between measurements is non uniform

labeled random finite sets and multi object conjugate priors the objective of multi object estimation is to simultaneously estimate the number of objects and their states from a set of observations in the presence of data association uncertainty detection uncertainty false observations and noise this estimation problem can be formulated in a bayesian framework by modeling the hidden set of states and set of observations as random finite sets rfss that covers thinning markov shifts and superposition a prior for the hidden rfs together with the likelihood of the realization of the observed rfs gives the posterior distribution via the application of bayes rule we propose a new class of rfs distributions that is conjugate with respect to the multiobject observation likelihood and closed under the chapman kolmogorov equation this result is tested on a bayesian multi target tracking algorithm

model reference output feedback fuzzy tracking control design for nonlinear discrete time systems with time delay in this study a model reference fuzzy tracking control design for nonlinear discrete time systems with time delay is introduced first the takagi and sugeno ts fuzzy model is employed to approximate a nonlinear discrete time system with time delay next based on the fuzzy model a fuzzy observer based fuzzy controller is developed to reduce the tracking error as small as possible for all bounded reference inputs the advantage of proposed tracking control design is that only a simple fuzzy observer based controller is used in our approach without feedback linearization technique and complicated adaptive scheme by the proposed method the fuzzy tracking control design problem is parameterized in terms of a linear matrix inequality problem lmip the lmip can be efficiently solved using the convex optimization techniques simulation example is given to illustrate the design procedures and tracking performance of the proposed method

models for point processes observed with noise summary consider a pair of point processes x and y where x is regarded as a true point process and y is an imperfect observation of x for the transformation from x to y we consider a number of disturbance mechanisms covering random thinning displacement censoring of the displaced points and superposition of extra points we present the conditional likelihood of y given x when both point processes are observed the likelihood may be used for inference about the disturbance mechanisms the likelihood is a sum typically with very many terms and we discuss an approximation with a small number of terms the results are applied to an example where x denotes a set of true positions of tree tops and y denotes tree top positions estimated by template matching in a digital image obtained by high resolution aerial photography the parameters governing the various disturbance mechanisms are estimated from the conditional likelihood

approximation theory of fuzzy systems spl minus siso case in this paper the approximation properties of mimo fuzzy systems generated by the product inference are discussed we first give an analysis of fuzzy basic functions fbf s and present several properties of fbf s based on these properties of fbf s we obtain several basic approximation properties of fuzzy systems 1 basic approximation property which reveals the basic approximation mechanism of fuzzy systems 2 uniform approximation bounds which give the uniform approximation bounds between the desired control or decision functions and fuzzy systems 3 uniform convergent property which shows that fuzzy systems with defined approximation accuracy can always be obtained by dividing the input space into finer fuzzy regions and 4 universal approximation property which shows that fuzzy systems are universal approximators and extends some previous results on this aspect the similarity between fuzzy systems and mathematical approximation is discussed and an idea to improve approximation accuracy is suggested based on uniform approximation bounds

convergence analysis of the gaussian mixture phd filter the gaussian mixture probability hypothesis density phd filter was proposed recently for jointly estimating the time varying number of targets and their states from a sequence of sets of observations without the need for measurement to track data association it was shown that under linear gaussian assumptions the posterior intensity at any point in time is a gaussian mixture this paper proves uniform convergence of the errors in the algorithm and provides error bounds for the pruning and merging stages in addition uniform convergence results for the extended kalman phd filter are given and the unscented kalman phd filter implementation is discussed

the cauchy problem for ut δum when 0 m 1 on considere le probleme de cauchy pour l equation de diffusion non lineaire u t u u m 1 0 sur 0 r n u 0 u 0 quand 0 m 1 on montre qu il existe une solution temporelle globale pour une fonction localement integrable u 0 on montre l unicite dans cette classe

fuzzy adaptive controller for mimo nonlinear systems with known and unknown control direction this paper investigates fuzzy adaptive control schemes for a class of multi input multi output mimo unknown nonlinear systems with known and unknown sign of the control gain matrix three fuzzy adaptive control schemes are developed in the design of the second and third controller we will exploit a decomposition of the control gain matrix into a symmetric positive definite matrix a diagonal matrix with diagonal entries 1 or 1 and a unity upper triangular matrix the nussbaum type function is used to deal with the unknown control direction i e the unknown sign of the control gain matrix for updating the parameters of the fuzzy system an adaptation proportional integral pi law is proposed theoretical results are illustrated through two simulation examples

adaptive t s fuzzy neural modeling and control for general mimo unknown nonaffine nonlinear systems using projection update laws this paper describes a novel design of an on line takagi sugeno t s fuzzy neural controller for a class of general multiple input multiple output mimo systems with unknown nonlinear functions and external disturbances instead of modeling the unknown systems directly the t s fuzzy neural model approximates a virtual linearized system vls of a real system with modeling errors and external disturbances compared with previous approaches the main contribution of this paper is an investigation of more general mimo unknown systems using on line adaptive t s fuzzy neural controllers in this paper we also use projection update laws which generalize the projection algorithm to tune the adjustable parameters this prevents parameter drift and ensures that the parameter matrix is bounded away from singularity we prove that the closed loop system controlled by the proposed controller is robust stable and the effect of all the modeling errors and external disturbances on the tracking error can be attenuated finally two examples covering four cases are simulated in order to confirm the effectiveness and applicability of the proposed approach in this paper

labeled random finite sets and the bayes multi target tracking filter an analytic solution to the multi target bayes recursion known as the delta generalized labeled multi bernoulli delta glmb filter has been recently proposed by vo and vo in labeled random finite sets and multi object conjugate priors ieee trans signal process vol 61 no 13 pp 3460 3475 2014 as a sequel to that paper the present paper details efficient implementations of the delta glmb multi target tracking filter each iteration of this filter involves an update operation and a prediction operation both of which result in weighted sums of multi target exponentials with intractably large number of terms to truncate these sums the ranked assignment and k th shortest path algorithms are used in the update and prediction respectively to determine the most significant terms without exhaustively computing all of the terms in addition using tools derived from the same framework such as probability hypothesis density filtering we present inexpensive relative to the delta glmb filter look ahead strategies to reduce the number of computations characterization of the l 1 error in the multi target density arising from the truncation is presented

the gaussian mixture probability hypothesis density filter a new recursive algorithm is proposed for jointly estimating the time varying number of targets and their states from a sequence of observation sets in the presence of data association uncertainty detection uncertainty noise and false alarms the approach involves modelling the respective collections of targets and measurements as random finite sets and applying the probability hypothesis density phd recursion to propagate the posterior intensity which is a first order statistic of the random finite set of targets in time at present there is no closed form solution to the phd recursion this paper shows that under linear gaussian assumptions on the target dynamics and birth process the posterior intensity at any time step is a gaussian mixture more importantly closed form recursions for propagating the means covariances and weights of the constituent gaussian components of the posterior intensity are derived the proposed algorithm combines these recursions with a strategy for managing the number of gaussian components to increase efficiency this algorithm is extended to accommodate mildly nonlinear target dynamics using approximation strategies from the extended and unscented kalman filters

the labeled multi bernoulli filter this paper proposes a generalization of the multi bernoulli filter called the labeled multi bernoulli filter that outputs target tracks moreover the labeled multi bernoulli filter does not exhibit a cardinality bias due to a more accurate update approximation compared to the multi bernoulli filter by exploiting the conjugate prior form for labeled random finite sets the proposed filter can be interpreted as an efficient approximation of the delta generalized labeled multi bernoulli filter it inherits the advantages of the multi bernoulli filter in regards to particle implementation and state estimation it also inherits advantages of the delta generalized labeled multi bernoulli filter in that it outputs labeled target tracks and achieves better performance