control synthesis for discrete time t s fuzzy systems based on membership function dependent h_ infty performance this article focuses on disturbance attenuation problem for discrete time takagi sugeno t s fuzzy systems first a membership function dependent h infty performance index is proposed by which different performance can be achieved for local subsystems then the better performance may be obtained for the t s fuzzy system that works on a particular local subsystem most of the time it may guarantee better control effects in practical engineering applications with the help of the newly defined performance index and fuzzy lyapunov functions an observer based feedback control scheme is presented in the form of linear matrix inequalities in simulation the performance index obtained by this article can be improved by about 10 than that obtained by the traditional h infty control method

observer based adaptive fuzzy tracking control for a class of uncertain nonlinear mimo systems this paper addresses the adaptive fuzzy tracking control problem for a class of uncertain nonlinear mimo systems with the external disturbances the adaptive fuzzy controllers are designed under the constraint that only system output is available for measurement then it is needed to design a state observer to estimate the unmeasured states in the observer design procedure two prominent advantages are that it does not require the sign of the control gain coefficient to be known and only two parameters need to be adjusted on line for each subsystem by using lyapunov analysis method it is proven that all the signals in the closed loop system are guaranteed to be bounded and the system outputs track the reference signals to a bounded compact set the feasibility of the proposed approach is validated by using two simulation examples

fuzzy approximation adaptive fault tolerant control for nonlinear pure feedback systems with unknown control directions and sensor failures abstract in this paper the problem of adaptive fuzzy fault tolerant control ftc is investigated for a class of nonlinear pure feedback systems with unknown control directions and sensor failures by utilizing the mean value theorem the considered nonlinear pure feedback system is transformed into a strict feedback structure the unavailability of state variables caused by sensor faults is dealt with by using parameter separation and regrouping technique based on cancelation and decoupled backstepping technique an adaptive fuzzy fault tolerant control method is developed it is proved that the proposed adaptive ftc scheme can guarantee the exponential stabilization of the resulting closed loop system furthermore all the signals of the system remain semi global uniformly ultimately bound sguub the simulation results are provided to illustrate the effectiveness of the proposed control scheme

low rank based multi input multi output takagi sugeno fuzzy modeling for prediction of molten iron quality in blast furnace abstract for complex blast furnace smelting systems with large time delay accurate prediction of molten iron quality indicators plays an important guiding role in blast furnace control recently some data driven multi input multi output mimo modeling methods have been proposed to model multiple molten iron quality indicators including molten iron temperature mit silicon content si phosphorus content p and sulfur content s however those data driven mimo models do not consider the inter indicator correlation which leads to the suboptimal model for the estimation of multiple molten iron quality indicators this paper proposed a novel mimo takagi sugeno t s fuzzy model with taking full account of the inter indicator correlation in the novel method the inter indicator correlation was explicitly modeled by a low rank learning in a latent space that overcame the great challenge of jointly determining the fuzzy rules of mimo t s model and the inter indicator correlation for the corresponding optimization problem an effective alternating optimization algorithm is presented the validity of the proposed method is verified by simulation and comparison with some related methods on real blast furnace data

output feedback control of nonlinear systems using direct adaptive fuzzy neural controller in this paper a direct adaptive fuzzy neural output feedback controller dafoc for a class of uncertain nonlinear systems is developed under the constraint that only the system output is available for measurement an output feedback control law and an update law are derived for on line tuning the weighting factors of the dafoc by using strictly positive real lyapunov theory the stability of the closed loop system compensated by the dafoc can be verified moreover the proposed overall control scheme guarantees that all signals involved are bounded and the output of the closed loop system asymptotically tracks the desired output trajectory to demonstrate the effectiveness of the proposed method simulation results are illustrated in this paper

adaptive control of a simple nonlinear system without a priori information on the plant parameters the authors present an adaptive control scheme for nonlinear systems of the form x c sup t f x b u where f x is lipschitz c is a constant vector and b is a constant scalar the control scheme achieves asymptotical model matching without a priori knowledge of the sign of the b gain the adaptive scheme is free from singularities in the sense that the estimate of b entering in the denominator of the control law is bounded away from zero the singularity has been overcome through a suitable modification of the parameter estimates which is based on standard least squares covariance matrix properties

a survey of adaptive fuzzy controllers nonlinearities and classifications adaptive fuzzy controllers afcs i e fuzzy controllers using heuristic expert knowledge and equipped with adaption ability are widely used in the industry in the present days as they are capable of dealing with ill formulated nonlinear systems where exact mathematical formulas are not known or partially described nonlinear systems afcs have been widely deployed to control many nonlinear systems with different types of critical nonlinearities over the last two decades many different approaches toward adaptive fuzzy control have been reported in this paper we recapitulate the latest research works related to afcs and provide an up to date survey on the latest development made in adaptive fuzzy control theory and applications based on different types of fuzzy systems considered and nonlinearities associated with the nonlinear system to be controlled we categorize the different approaches based on different types of nonlinearities and consider basic nonlinear systems nonlinear systems with external disturbances nonlinear systems with some special type of nonlinearities nonaffine nonlinear systems and strict feedback nonlinear systems it turns out that many existing methods are subject to various limitations and none of them is capable of controlling a general nonlinear systems hence it is still an open problem to design and develop a universal afc for a general class of nonlinear systems

adaptive fuzzy backstepping output feedback tracking control of mimo stochastic pure feedback nonlinear systems with input saturation abstract in this paper the adaptive fuzzy backstepping output feedback tracking control problem is considered for a class of uncertain stochastic multi input and multi output mimo nonlinear systems in pure feedback form the stochastic mimo nonlinear systems under study have unknown nonlinear functions input saturation and immeasurable states by using fuzzy logic systems to identify the uncertain nonlinear system and a smooth function to approximate the input saturation a fuzzy state observer is designed and the estimations of the immeasurable states are obtained based on the backstepping recursive design technique an adaptive fuzzy output feedback tracking control approach is developed it is shown that the proposed control approach guarantees that all the signals of the resulting closed loop system are semi globally uniformly ultimately bounded sguub in mean square in the sense of probability and the observer errors and tracking errors can be regulated to a small neighborhood of the origin by choosing design parameters appropriately a simulation example is provided to show the effectiveness of the proposed approach

robust adaptive neural control for a class of perturbed strict feedback nonlinear systems this paper presents a robust adaptive neural control design for a class of perturbed strict feedback nonlinear system with both completely unknown virtual control coefficients and unknown nonlinearities the unknown nonlinearities comprise two types of nonlinear functions one naturally satisfies the triangularity condition and can be approximated by linearly parameterized neural networks while the other is assumed to be partially known and consists of parametric uncertainties and known bounding functions with the utilization of iterative lyapunov design and neural networks the proposed design procedure expands the class of nonlinear systems for which robust adaptive control approaches have been studied the design method does not require a priori knowledge of the signs of the unknown virtual control coefficients leakage terms are incorporated into the adaptive laws to prevent parameter drifts due to the inherent neural network approximation errors it is proved that the proposed robust adaptive scheme can guarantee the uniform ultimate boundedness of the closed loop system signals the control performance can be guaranteed by an appropriate choice of the design parameters simulation studies are included to illustrate the effectiveness of the proposed approach

direct adaptive control and robust analysis for unknown multivariable nonlinear systems with fuzzy logic systems in this paper we are dealing with the problem of controlling an unknown nonlinear dynamical system modeled by fuzzy logic systems in the ideal case of complete model matching convergence of the state to zero plus boundedness of all signals in the closed loop system is ensured moreover the robustness of the controller designed in the ideal case is given in the framework of singular perturbation theory

a novel robust adaptive fuzzy tracking control for a class of nonlinearmulti input multi output systems robust adaptive fuzzy tracking control of a class of uncertain multi input multi output nonlinear systems with coupled interconnections is considered in this paper takagi sugeno t s fuzzy systems are used to approximate the unknown system functions a novel adaptive control scheme is developed on the basis of the so called dynamic surface control and minimal learning parameters techniques the proposed scheme has following two key features first the number of parameters updated online for each subsystem is reduced to one and both problems of curse of dimension for high dimensional systems and explosion of complexity inherent in the conventional backstepping methods are circumvented second the potential controller singularity problem in some of the existing adaptive control schemes with feedback linearization techniques is overcome it is shown via lyapunov theory that all the signals in the closed loop system are semiglobally uniformly ultimately bounded finally simulation results via two examples are presented to demonstrate the effectiveness and advantages of the proposed scheme

adaptive neural control of mimo nonlinear state time varying delay systems with unknown dead zones and gain signs in this paper adaptive neural control is proposed for a class of uncertain multi input multi output mimo nonlinear state time varying delay systems in a triangular control structure with unknown nonlinear dead zones and gain signs the design is based on the principle of sliding mode control and the use of nussbaum type functions in solving the problem of the completely unknown control directions the unknown time varying delays are compensated for using appropriate lyapunov krasovskii functionals in the design the approach removes the assumption of linear functions outside the deadband as an added contribution by utilizing the integral lyapunov function and introducing an adaptive compensation term for the upper bound of the residual and optimal approximation error as well as the dead zone disturbance the closed loop control system is proved to be semi globally uniformly ultimately bounded simulation results demonstrate the effectiveness of the approach

observer based adaptive neural network controller for uncertain nonlinear systems with unknown control directions subject to input time delay and saturation abstract this paper addresses the design of an observer based adaptive neural controller for a class of strict feedback nonlinear uncertain systems subject to input delay saturation and unknown direction the input delay has been handled using an integral compensator term in the controller design a neural network observer has been developed to estimate the unmeasured states in the observer design the lipschitz condition has been relaxed to solve the problem of unknown control directions the nussbaum gain function has been applied in the backstepping controller design the explosion of complexity occurred in the traditional backstepping technique has been avoided utilizing the dynamic surface control dsc technique and the designed controller is singularity free it has been shown that all closed loop signals are semi globally uniformly ultimately bounded sguub and the output tracking error converges to a small neighborhood of the origin by choosing the design parameters appropriately the numerical examples illustrate the effectiveness of the proposed control scheme

adaptive fuzzy output feedback control for mimo switched nonlinear systems with prescribed performances abstract this paper proposes an adaptive fuzzy output feedback control approach for a class of uncertain multiple input and multiple output mimo switched nonlinear systems with prescribed performance and immeasurable states in this research fuzzy logic systems are used to identify the unknown nonlinear functions a fuzzy switched state observer is established to observe the unmeasured states based on backstepping technique and the prescribed performance theory a new adaptive fuzzy control method with prescribed performance is developed it is proved that the proposed control approach can ensure that all the signals of the resulting closed loop system are bounded and the tracking errors are within the prescribed performance bounds for all times simulation studies illustrate the effectiveness of the proposed approach

fuzzy adaptive output feedback control for mimo nonlinear systems in this paper two observer based adaptive fuzzy output feedback control schemes are presented for a class of uncertain continuous time multi input multi output mimo nonlinear dynamics systems whose states are not available within these schemes fuzzy logic systems are employed to approximate the plant s unknown nonlinear functions and then the state observer is designed for estimating the states of the plant upon which a fuzzy adaptive output feedback controller is firstly investigated in order to overcome the controller singularity problem and relax the requirement of bounding parameter values a second modified fuzzy adaptive output feedback controller is proposed by using a regularized inverse and a robustifying control term all parameter adaptive laws and robustifying control terms are derived based on lyapunov stability analysis so that convergence to zero of tracking errors and boundedness of all signals in the closed loop system can be guaranteed simulations performed on a two link robot manipulator illustrate the approach and exhibit its performance

stable adaptive fuzzy control for mimo nonlinear systems in this paper an indirect adaptive fuzzy control scheme is presented for a class of multi input and multi output mimo nonlinear systems whose dynamics are poorly understood within this scheme fuzzy systems are employed to approximate the plant s unknown dynamics in order to overcome the controller singularity problem the estimated gain matrix is decomposed into the product of one diagonal matrix and two orthogonal matrices a robustifying control term is used to compensate for the lumped errors and all parameter adaptive laws and robustifying control term are derived based on lyapunov stability analysis the proposed scheme guarantees that all the signals in the resulting closed loop system are uniformly ultimately bounded uub moreover the tracking errors can be made small enough if the designed parameter is chosen to be sufficiently large a simulation example is used to demonstrate the effectiveness of the proposed control scheme

a note on tests for exponentiality by deshpande summary recently deshpande 1983 has proposed a class of simple tests for testing exponentiality against the increasing failure rate average class of nonexponential probability distributions these tests have a number of desirable properties however they suffer from some omissions the object of this note is to provide the missing arguments so that the paper can be more useful

stable adaptive fuzzy control of nonlinear systems a direct adaptive fuzzy controller that does not require an accurate mathematical model of the system under control is capable of incorporating fuzzy if then control rules directly into the controllers and guarantees the global stability of the resulting closed loop system in the sense that all signals involved are uniformly bounded is developed the specific formula for the bounds is provided so that controller designers can determine the bounds based on their requirements the direct adaptive fuzzy controller is used to regulate an unstable system to the origin and to control the duffing chaotic system to track a trajectory the simulation results show that the controller worked without using any fuzzy control rules and that after fuzzy control rules were incorporated the adaptation speed became much faster it is shown explicitly how the supervisory control forces the state to remain within the constraint set and how the adaptive fuzzy controller learns to regain control

output feedback control of uncertain nonlinear systems using a self structuring adaptive fuzzy observer this paper describes the design of an output feedback controller based on an adaptive fuzzy observer for uncertain single input single output nonlinear dynamical systems we have focused on the realization of a minimal dynamic order for the adaptive fuzzy observer for this purpose we adopt an adaptive fuzzy observer in which no strictly positive real spr condition is needed and we combine a self structuring scheme with an on line estimation of fuzzy parameters by using this proposed scheme we can reduce the dynamic order of the adaptive output feedback fuzzy control system the lyapunov synthesis approach is used to guarantee a global uniform ultimate boundedness property of the state estimation error and tracking error as well as all other signals in the closed loop system no a priori knowledge of an upper limits on the uncertainties including optimal parameters and modeling errors is required the theoretical results are illustrated through simulation examples

adaptive fuzzy decentralized control for a class of nonlinear systems with different performance constraints abstract this paper investigates an adaptive fuzzy decentralized control approach for a class of nonlinear systems with unknown control direction and different performance constraints in the control design the different performance constraints that are the prescribed performance error constraints for some subsystems and the asymmetric time varying output constraints for the others can be unified as one form by selecting proper performance functions the fuzzy systems are employed to approximate the unknown nonlinear functions and an adaptive fuzzy decentralized controller loaded with the nussbaum type function is developed to resolve the unknown control direction issues it is proved that all the signals in the resulting closed loop systems are bounded and that the different performance constraints for the subsystems can be achieved simultaneously the performance of the proposed scheme is highlighted through the simulation example

schurian sp representation finite right peak pi rings and their indecomposable socle projective modules such that soc r is an essential right ideal in r and it is isomorphic to a direct sum of finitely many copies of p called a right peak of r here f f are local rings f f is a division ring imj is an fi fj bimodule and the multiplication in r is given by fi f bimodule maps cii imj im jm satisfying the natural associativity conditions we denote by p p p the right indecomposable row ideals of r

observer based direct adaptive fuzzy neural control for nonaffine nonlinear systems in this paper an observer based direct adaptive fuzzy neural control scheme is presented for nonaffine nonlinear systems in the presence of unknown structure of nonlinearities a direct adaptive fuzzy neural controller and a class of generalized nonlinear systems which are called nonaffine nonlinear systems are instead of the indirect one and affine nonlinear systems given by leu et al by using implicit function theorem and taylor series expansion the observer based control law and the weight update law of the fuzzy neural controller are derived for the nonaffine nonlinear systems based on strictly positive real spr lyapunov theory the stability of the closed loop system can be verified moreover the overall adaptive scheme guarantees that all signals involved are bounded and the output of the closed loop system will asymptotically track the desired output trajectory to demonstrate the effectiveness of the proposed method simulation results are illustrated in this paper

noninteracting adaptive control of pmsm using interval type 2 fuzzy logic systems based on the type 2 fuzzy logic systems and adaptive control theory this paper develops a decentralized adaptive fuzzy controller for a permanent magnet synchronous motor pmsm the adaptive law is designed to compensate for the interconnection effects and the reconstruction errors using a sliding mode term the type 2 fuzzy logic systems are used to model the unknown functions based on lyapunov s stability theorem the stability of closed loop systems can be verified moreover the proposed overall control schemes guarantee that all the signals that are involved are bounded to demonstrate the effectiveness of the proposed methods simulation results are illustrated in this paper

adaptive fuzzy output feedback control of mimo nonlinear systems with unknown dead zone inputs this paper is concerned with the problem of adaptive fuzzy tracking control for a class of multi input and multi output mimo strict feedback nonlinear systems with both unknown nonsymmetric dead zone inputs and immeasurable states in this research fuzzy logic systems are utilized to evaluate the unknown nonlinear functions and a fuzzy adaptive state observer is established to estimate the unmeasured states based on the information of the bounds of the dead zone slopes as well as treating the time varying inputs coefficients as a system uncertainty a new adaptive fuzzy output feedback control approach is developed via the backstepping recursive design technique it is shown that the proposed control approach can assure that all the signals of the resulting closed loop system are semiglobally uniformly ultimately bounded it is also shown that the observer and tracking errors converge to a small neighborhood of the origin by selecting appropriate design parameters simulation examples are also provided to illustrate the effectiveness of the proposed approach

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

stability analysis and design of takagi sugeno fuzzy systems this work presents stable composite control criteria for multivariable takagi sugeno t s fuzzy systems on the basis of the linear matrix inequality lmi control strategy and parametric optimization the composite fuzzy control algorithms are derived unlike earlier studies of fuzzy control systems on an lmi framework this investigation develops a supervisory control approach such that a fuzzy controller can be synthesized more efficiently moreover a robust control scheme is applied to the t s fuzzy model with parametric uncertainties the sufficient conditions are deduced in the form of reduced lmis and adaptive tuning rules finally numeric simulations are given to validate the proposed approach

adaptive fuzzy terminal sliding mode control for a class of mimo uncertain nonlinear systems this paper presents a new adaptive terminal sliding mode tracking control design for a class of nonlinear systems using fuzzy logic system the terminal sliding mode control tsm was developed to provide faster convergence and higher precision control than the linear hyperplane based sliding control however the original tsm encountered singularity problem with discontinuous control action moreover a prior knowledge about the plant to be controlled is required the proposed controller combines a continuous non singular tsm with an adaptive learning algorithm and fuzzy logic system to estimate the dynamics of the controlled plant so that closed loop stability and finite time convergence of tracking errors can be guaranteed the performance of the proposed control strategy is evaluated through the control of a two link rigid robotic manipulator finally the effectiveness of the proposed scheme is demonstrated through the control of the ankle and knee movements in paraplegic subjects using functional electrical stimulation simulation and experimental results verify that the proposed control strategy can achieve favorable control performance with regard to system parameter variations and external disturbances 2011 elsevier b v all rights reserved

observer based adaptive fuzzy control for siso nonlinear systems abstract the observer based indirect and direct adaptive fuzzy controllers are developed for a class of siso uncertain nonlinear systems the proposed approaches do not need the availability of the state variables by designing the state observer the adaptive fuzzy systems which are used to model the unknown functions can be constructed using the state estimations thus a new hybrid adaptive fuzzy control method is proposed by combining the above adaptive fuzzy system with the h control technique based on lyapunov stability theorem the proposed adaptive fuzzy control system can guarantee the stability of the whole closed loop systems and obtain good tracking performance as well the proposed methods are applied to an inverted pendulum system and a chaotic system and achieve satisfactory simulation results

a class of tests for exponentiality against increasing failure rate average alternatives a class of tests is proposed for testing exponentiality against the increasing failure rate average class of nonexponential probability distributions some of these tests are consistent as well for the larger class new better than used compared to its competitors these statistics are remarkably simple they are u statistics and hence asymptotically normally distributed the asymptotic relative efficiency with respect to the hollanderproschan statistic and the cumulative total time on test statistic are reasonably high

adaptive fuzzy control of a class of mimo nonlinear systems this paper presents two indirect adaptive fuzzy control schemes for a class of uncertain continuous time multi input multi output nonlinear dynamic systems within these schemes fuzzy systems are employed to approximate the plant s unknown nonlinear functions and robustifying control terms are used to compensate for approximation errors by using a regularized matrix inverse a stable well defined adaptive controller is firstly investigated then in order to obtain an adaptive controller not depending upon any parameter initialization conditions and to relax the requirement of bounding parameter values a second adaptive controller is proposed all parameter adaptive laws and robustifying control terms are derived based on lyapunov stability analysis so that under appropriate assumptions semi global stability and asymptotic convergence to zero of tracking errors can be guaranteed simulations performed on a two link robot manipulator illustrate the approach and exhibit its performance

observer based fuzzy adaptive control for strict feedback nonlinear systems in this paper a new fuzzy adaptive control approach is developed for a class of siso strict feedback nonlinear systems with unmeasured states using fuzzy logic systems to approximate the unknown nonlinear functions a fuzzy adaptive observer is introduced for state estimation as well as system identification under the framework of the backstepping design fuzzy adaptive output feedback control is constructed recursively it is proven that the proposed fuzzy adaptive control approach guarantees the semi global boundedness property for all the signals and the tracking error to a small neighborhood of the origin simulation studies are included to illustrate the effectiveness of the proposed approach

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

perspectives of fuzzy systems and control although fuzzy control was initially introduced as a model free control design method based on the knowledge of a human operator current research is almost exclusively devoted to model based fuzzy control methods that can guarantee stability and robustness of the closed loop system state of the art techniques for identifying fuzzy models and designing model based controllers are reviewed in this article attention is also paid to the role of fuzzy systems in higher levels of the control hierarchy such as expert control supervision and diagnostic systems open issues are highlighted and an attempt is made to give some directions for future research

observer based fuzzy adaptive control for multi input multi output nonlinear systems with a nonsymmetric control gain matrix and unknown control direction in this study we present an observer based fuzzy adaptive control scheme for a class of uncertain multi input multi output nonlinear systems with a nonsymmetric control gain matrix unknown control direction and unmeasured states in this scheme fuzzy systems are used to approximate the unknown nonlinear functions and an adaptive fuzzy state observer is designed to estimate the unmeasured states the parameter adaptive laws are designed by fuzzy basis functions rather than by its filtering and a robust control term is used to compensate for the lumped errors the proposed method solves the problem of the nonsymmetric control gain matrix and it requires no a priori knowledge of the control direction for the states that are unmeasured we prove that all of the signals in the resulting closed loop systems are bounded and that the tracking errors converge asymptotically to zero two simulation examples are used to demonstrate the effectiveness of the proposed scheme

fuzzy adaptive control of multivariable nonlinear systems abstract the robust fuzzy adaptive control scheme is developed for a class of unknown nonlinear mimo systems two types of uncertainties are considered i e the matched uncertainty and the unmatched uncertainties in the control procedure fuzzy logic systems are implemented to estimate the unknown functions and robust compensators are designed in h sense for attenuating the unmatched uncertainties it is shown that the proposed control is continuous guarantees global stability and the h performance index extensive simulations on the tracking control of a two link rigid robotics manipulator verify the effectiveness of the proposed algorithms

observer based adaptive fuzzy output constrained control for mimo nonlinear systems with unknown control directions in this paper an adaptive fuzzy output feedback control approach is proposed for a class of multi input and multi output mimo uncertain nonlinear systems with unmeasured states and unknown control directions in the control design by using fuzzy logic systems to approximate the unknown nonlinear functions a fuzzy adaptive state observer is designed for state estimation as well as system identification and a nussbaum gain function is introduced into the control design to solve the unknown control direction problem by applying the backstepping design techniques a fuzzy adaptive output feedback control is constructed recursively to address the problems of output constraint and explosion of complexity the barrier lyapunov function method and dynamic surface control technique are employed respectively it is proved that the proposed control approach can guarantee the semi globally uniform ultimate boundedness for all the signals and the tracking errors to a small neighborhood of the origin simulation studies illustrate the effectiveness of the proposed approach

a matrix problem over a central quadratic skew field extension abstract let f g f u be a central quadratic skew field extension such that the generator u is central in g and w g f g a natural g g bimodule we deal with the matrix problem on finding a canonical form for rectangular matrices over w with help of left elementary transformations of their rows and right elementary transformations of columns over g we solve this problem reducing it in the separable resp inseparable case to the semilinear resp pseudolinear pencil problem

on distribution free goodness of fit testing of exponentiality there is a need to test the hypothesis of exponentiality against a wide variety of alternative hypotheses across many areas of economics and finance local or contiguous alternatives are the closest alternatives against which it is still possible to have some power hence goodness of fit tests should have some power against all or a huge majority of local alternatives such tests are often based on nonlinear statistics with a complicated asymptotic null distribution thus a second desirable property of a goodness of fit test is that its statistic will be asymptotically distribution free we suggest a whole class of goodness of fit tests with both of these properties by constructing a new version of empirical process that weakly converges to a standard brownian motion under the hypothesis of exponentiality all statistics based on this process will asymptotically behave as statistics from a standard brownian motion and so will be asymptotically distribution free we show the form of transformation is especially simple in the case of exponentiality surprisingly there are only two asymptotically distribution free versions of empirical process for this problem and only this one has a convenient limit distribution many tests of exponentiality have been suggested based on asymptotically linear functionals from the empirical process we illustrate none of these can be used as goodness of fit tests contrary to some previous recommendations of considerable interest is that a selection of well known statistics all lead to the same test asymptotically with negligible asymptotic power against a great majority of local alternatives finally we present an extension of our approach that solves the problem of multiple testing both for exponentiality and for other more general hypotheses

how to design a fuzzy adaptive controller based on observers for uncertain affine nonlinear systems this paper focuses on the construction of a fuzzy adaptive output feedback control based on any observer high gain hg observer sliding mode like observer etc for a class of single input single output siso uncertain or ill defined affine nonlinear systems indeed the corrective term of the proposed observer involves a well defined design function which is shown to be satisfied by the commonly used hg based observers namely for the usual hg observers and the sliding mode observers together with their implementable versions the design of the underlying update law as well as the robust control term is based on an appropriate filtering of the output tracking error this particularly allows to overcome the output observation error filtering or the necessity of the famous strictly positive real spr condition

an extended direct scheme for robust adaptive nonlinear control abstract the proposed adaptive scheme achieves regulation for a class of nonlinear systems with unknown constant parameters and unmodeled dynamics the scheme does not employ overparametrization and does not restrict the class of nonlinearities by any growth conditions instead the dependence on the unknown parameters is restricted by an extended matching condition which however is satisfied in many systems of practical importance such as most types of electric motors

fuzzy adaptive quantized output feedback tracking control for switched nonlinear systems with input quantization in this paper the problem of adaptive fuzzy quantized output feedback control is investigated for a class of uncertain switched nonlinear systems in strict feedback form the considered switched systems contain unknown nonlinearities hysteretic quantized input and without requiring the system states being available for measurement fuzzy logic systems are utilized to approximate the unknown nonlinear functions and a switched fuzzy state observer is designed to estimate the unmeasured states the hysteretic quantized input is implemented to avoid the oscillation caused by logarithmic quantizer and decomposed into two bounded nonlinear functions based on the estimated states and using the backstepping design principle an adaptive fuzzy quantized output feedback control scheme is developed it is proved that whole adaptive fuzzy quantized control scheme can guarantee that all the variables in the closed loop system are bounded under a class of switching signals with average dwell time and also that the system output can track a given reference signal as closely as possible the simulation results are given to check the effectiveness of the proposed approach

fuzzy adaptive observer backstepping control for mimo nonlinear systems in this paper a fuzzy adaptive backstepping output feedback control approach is developed for a class of multi input and multi output mimo nonlinear systems with unmeasured states using fuzzy logic systems to approximate the unknown nonlinear functions a fuzzy adaptive observer is designed for state estimation as well as system identification combining with the backstepping design techniques a fuzzy adaptive output feedback control is constructed recursively it is proved that the proposed fuzzy adaptive control approach can guarantee the semi global uniform ultimate boundedness for all the signals and the tracking error to a small neighborhood of the origin simulation studies illustrate the effectiveness of the proposed approach

command filter based adaptive fuzzy backstepping control for a class of switched nonlinear systems abstract this paper considers the adaptive fuzzy output feedback control design problem for a class of switched nonlinear systems the considered switched nonlinear systems contain unknown nonlinearities the switching signals with average dwell time and without requiring that state variables are available for measurement fuzzy logic systems are used to approximate the unknown nonlinearities and a fuzzy switched state observer is designed to estimate the immeasurable states in the framework of observer based output feedback control design technique and by introducing a command filter into the backstepping design algorithm a command filter based adaptive fuzzy backstepping output feedback control scheme is developed the stability of the closed loop system and the convergence of the tracking error are proved via average dwell time and multiple lyapunov function a numerical example is provided to illustrate the effectiveness of the proposed approach

a canonical form for matrices under consimilarity abstract square complex matrices a b are said to be consimilar if a sb s 1 for some nonsingular matrix s consimilarity is an equivalence relation that is a natural matrix generalization of rotation of scalars in the complex plane we survey the known forms to which a given complex matrix may be reduced by unitary consimilarity and describe a canonical form to which it may be reduced by a general consimilarity we derive a useful criterion for two matrices to be consimilar and show that every matrix is consimilar to its own conjugate transpose and adjoint to a real matrix and to a hermitian matrix

three equipped posets and their representations and corepresentations inseparable case abstract we define three equipped posets i e partially ordered sets equipped with three kinds of binary relations and their representations and corepresentations over an inseparable cubic field extension f g in characteristic 3 these representations and corepresentations lead to some matrix problems of mixed type over the pair of fields f g through these problems of linear algebra we completely describe in evident matrix form the indecomposables for some critical three equipped posets reducing the task for the rest of the critical sets to some matrix problems containing the pseudolinear pencil problem as a subproblem

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

representations of generalized equipped posets and posets with automorphisms over galois field extensions abstract generalized equipped posets and their representations over a galois field extension k l are introduced and studied an equipment of a poset p is a pair s consisting of a finite group and a collection s x y x y of its non empty subsets such that x y y z x z for any chain x y z in p equipped posets in the old sense of our former papers correspond to the case 2 with some additional restriction since the category of representations of a strictly equipped poset p s is isomorphic to the category of representations of its evolvent q which is a poset with automorphisms we study in fact representations of posets with automorphisms over the pair k l in the case where gal l k we define the complexification functor c rep k l q rep l q and the realification functor r rep l q rep k l q and show that they induce reciprocal bijections between the isoclasses of indecomposables of the category rep k l q and the orbits of isoclasses of indecomposables of the category rep l q in this way the problem on classification of indecomposables of equipped posets and posets with automorphisms over k l actually is reduced to that one for representations of ordinary posets over l

fuzzy adaptive decentralized output feedback control for large scale nonlinear systems with dynamical uncertainties in this paper an adaptive fuzzy decentralized robust output feedback control approach is proposed for a class of large scale strict feedback nonlinear systems with the unmeasured states the large scale nonlinear systems in this paper are assumed to possess the unstructured uncertainties unmodeled dynamics and unknown high frequency gain sign fuzzy logic systems are used to approximate the unstructured uncertainties k filters are designed to estimate the unmeasured states and a dynamical signal and a special nussbaum gain function are introduced into the control design to solve the problem of unknown high frequency gain sign and dominate unmodeled uncertainties respectively based on the backstepping design and adaptive fuzzy control methods an adaptive fuzzy decentralized robust output feedback control scheme is developed it is proved that the proposed adaptive fuzzy control approach can guarantee that all the signals in the closed loop system are uniformly and ultimately bounded and the tracking errors converge to a small neighborhood of the origin the effectiveness of the proposed approach is illustrated by using simulation results

adaptive control of robot manipulators using fuzzy logic systems under actuator constraints in this paper a stable fuzzy adaptive controller for trajectory tracking is developed for robot manipulators without velocity measurements taking into account the actuator constraints the controller is based on structural knowledge of the dynamics of the robot and measurements of link positions only the gravity torque including system uncertainty like payload variation etc is estimated by a fuzzy logic system fls the adaptive controller represents an amalgamation of a filtering technique to eliminate velocity measurements and the theory of function approximation using fls to estimate the gravity torque the proposed controller ensures the local asymptotic stability and the convergence of the position error to zero the proposed controller is robust not only to structured uncertainty such as payload parameter variation but also to unstructured one such as disturbances the validity of the control scheme is shown by simulations on a two link robot manipulator

a hierarchical structure of observer based adaptive fuzzy neural controller for mimo systems an observer based adaptive controller developed from a hierarchical fuzzy neural network hfnn is employed to solve the controller time delay problem for a class of multi input multi output mimo non affine nonlinear systems under the constraint that only system outputs are available for measurement by using the implicit function theorem and taylor series expansion the observer based control law and the weight update law of the hfnn adaptive controller are derived according to the design of the hfnn hierarchical fuzzy neural network the observer based adaptive controller can alleviate the online computation burden moreover the common adaptive controller is utilized to control all the mimo subsystems hence the number of adjusted parameters of the hfnn can be further reduced in this paper we prove that the proposed observer based adaptive controller can guarantee that all signals involved are bounded and that the outputs of the closed loop system track asymptotically the desired output trajectories

on the kronecker problem and related problems of linear algebra abstract we consider some classification problems of linear algebra related closely to the classical kronecker problem on pairs of linear maps between two finite dimensional vector spaces as shown by djokovic and sergeichuk the kronecker s solution is extended to the cases of pairs of semilinear maps and more generally pseudolinear bundles respectively our objective is to deal with the semilinear case of the kronecker problem especially with its applications it is given a new short solution both to this case and to its contragredient variant the biquadratic matrix problem is investigated and reduced in the homogeneous case in characteristic 2 to the semilinear kronecker problem the integer matrix sequence n and transformation of polynomials are introduced and studied to get a simplified canonical form of indecomposables for the mentioned homogeneous problem some applications to the representation theory of posets with additional structures are presented

adaptive fuzzy control for feedback linearizable mimo nonlinear systems with prescribed performance abstract in this paper a new adaptive fuzzy control scheme with prescribed performance is developed for a class of feedback linearizable uncertain mimo nonlinear systems with unknown control direction and external disturbances the fuzzy systems are employed to approximate the unknown nonlinear functions and a nussbaum type function is applied to resolve the unknown control direction problems by using the prescribed performance bounds an adaptive fuzzy controller equipped with the nussbaum type gain function is developed the proposed design scheme guarantees that all the signals in the closed loop systems are bounded and that the tracking errors converge to a prescribed performance bounds by guaranteeing the convergence of the filtered tracking error to a predefine performance bounds two simulation examples are used to demonstrate the effectiveness of the proposed scheme