quantum bit commitment with cheat sensitive binding and approximate sealing this paper proposes a cheat sensitive quantum bit commitment scheme based on single photons in which alice commits a bit to bob here bob s probability of success at cheating as obtains the committed bit before the opening phase becomes close to just like performing a guess as the number of single photons used is increased and if alice alters her committed bit after the commitment phase her cheating will be detected with a probability that becomes close to 1 as the number of single photons used is increased the scheme is easy to realize with present day technology

tracking control of a manipulator under uncertainty by fuzzy p id controller one of the most important manipulator operations is the control of the manipulator to track a given trajectory most commercial robot systems currently are equipped with conventional pid controllers due to their simplicity in structure and ease of design using pid control however it is di cult to achieve a desired tracking control performance since the dynamic equations of a mechanical manipulator are tightly coupled highly nonlinear and uncertain in order to improve the tracking control performance under uncertainty this paper presents a new hybrid control scheme for the manipulator which consists of a fuzzy logic proportional controller and a conventional integral and derivative controller fuzzy p id in comparison with a conventional pid controller only one additional parameter has to be adjusted to tune the fuzzy p id controller the proposed control scheme has been implemented on a direct drive two link manipulator the outlined experimental results on tracking control under inertial parameters changes demonstrate the eaectiveness and the robustness of the new fuzzy p id controller c 2001 elsevier science b v all rights reserved

two step stability analysis for general polynomial fuzzy model based control systems this paper investigates the stability of a polynomial fuzzy model based pfmb control system formed by a nonlinear plant represented by a polynomial fuzzy model and a polynomial fuzzy controller connected in a closed loop three cases of polynomial fuzzy controllers are proposed for the control process with the consideration of a matched mismatched number of rules and or premise membership functions which demonstrate different levels of controller complexity design flexibility and stability analysis results a general polynomial lyapunov function candidate is proposed to investigate the system stability unlike the published work there is no constraint on the polynomial lyapunov function candidate which is independent of the form of the polynomial fuzzy model thus it can be applied to a wider class of pfmb control systems and potentially produces more relaxed stability analysis results two step stability conditions in terms of sum of squares sos are obtained to numerically find a feasible solution to facilitate the stability analysis and relax the stability analysis result the boundary information of membership functions is taken into account in the stability analysis and incorporated into the sos based stability conditions simulation examples are given to illustrate the effectiveness of the proposed approach

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

quantum data hiding we expand on our work on quantum data hiding hiding classical data among parties who are restricted to performing only local quantum operations and classical communication locc we review our scheme that hides one bit between two parties using bell 1964 states and we derive upper and lower bounds on the secrecy of the hiding scheme we provide an explicit bound showing that multiple bits can be hidden bitwise with our scheme we give a preparation of the hiding states as an efficient quantum computation that uses at most one ebit of entanglement a candidate data hiding scheme that does not use entanglement is presented we show how our scheme for quantum data hiding can be used in a conditionally secure quantum bit commitment scheme

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

dispersion operators and resistant second order functional data analysis inferences related to the second order properties of functional data as expressed by covariance structure can become unreliable when the data are non gaussian or contain unusual observations in the functional setting it is often difficult to identify atypical observations as their distinguishing characteristics can be manifold but subtle in this paper we introduce the notion of a dispersion operator investigate its use in probing the second order structure of functional data and develop a test for comparing the second order characteristics of two functional samples that is resistant to atypical observations and departures from normality the proposed test is a regularized m test based on a spectrally truncated version of the hilbert schmidt norm of a score operator defined via the dispersion operator we derive the asymptotic distribution of the test statistic investigate the behaviour of the test in a simulation study and illustrate the method on a structural biology dataset

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

a reinforcement learning adaptive fuzzy controller for robots in this paper a new reinforcement learning scheme is developed for a class of serial link robot arms traditional reinforcement learning is the problem faced by an agent that must learn behavior through trial and error interactions with a dynamic environment in the proposed reinforcement learning scheme an agent is employed to collect signals from a fixed gain controller an adaptive critic element and a fuzzy action generating element the action generating element is a fuzzy approximator with a set of tunable parameters and the performance measurement mechanism sends an error metric to the adaptive critic element for generating and transferring a reinforcement learning signal to the agent moreover a tuning algorithm of the proposed scheme that can guarantee both tracking performance and stability is derived from the lyapunov stability theory therefore a combination of adaptive fuzzy control and reinforcement learning scheme is also concerned with algorithms for eliminating a sequence of decisions from experience simulations of the proposed reinforcement adaptive fuzzy control scheme on the cart pole balancing problem and a two degree of freedom 2dof manipulator scara robot arm verify the effectiveness of our approach

fuzzy controller for a sensor guided robot manipulator sensor guided robots require according to nonlinear characteristics of certain interaction processes between robot effector and object appropriate robust control algorithms for the external sensory control loop this paper deals with a fuzzy controller which bases on linguistic production rules following the pattern of the suboptimal control method its structure is available in a large field of applications simulations and real time experiments concerning the contour tracking problem show compared with a conventional pd controller the high tracking quality of the controller

why quantum bit commitment and ideal quantum coin tossing are impossible abstract there had been well known claims of unconditionally secure quantum protocols for bit commitment however we and independently mayers showed that all proposed quantum bit commitment schemes are in principle insecure because the sender alice can almost always cheat successfully by using an einstein podolsky rosen epr type of attack and delaying her measurements one might wonder if secure quantum bit commitment protocols exist at all we answer this question by showing that the same type of attack by alice will in principle break any bit commitment scheme the cheating strategy generally requires a quantum computer we emphasize the generality of this no go theorem unconditionally secure bit commitment schemes based on quantum mechanics fully quantum classical or quantum but with measurements are all ruled out by this result since bit commitment is a useful primitive for building up more sophisticated protocols such as zero knowledge proofs our results cast very serious doubt on the security of quantum cryptography in the so called post cold war applications we also show that ideal quantum coin tossing is impossible because of the epr attack this no go theorem for ideal quantum coin tossing may help to shed some lights on the possibility of non ideal protocols

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

implications of superstrong non locality for cryptography non local boxes are hypothetical machines that give rise to superstrong non local correlations leading to a stronger violation of bell clauser horne shimony holt inequalities than is possible within the framework of quantum mechanics we show how non local boxes can be used to perform any two party secure computation we first construct a protocol for bit commitment and then show how to achieve oblivious transfer using non local boxes both have been shown to be impossible using quantum mechanics alone

adaptive control of robot manipulator using fuzzy compensator this paper presents two kinds of adaptive control schemes for robot manipulator which has the parametric uncertainties in order to compensate these uncertainties we use the fls fuzzy logic system that has the capability to approximate any nonlinear function over the compact input space in the proposed control schemes we need not derive the linear formulation of robot dynamic equation and tune the parameters we also suggest the robust adaptive control laws in all proposed schemes for decreasing the effect of approximation error to reduce the number of fuzzy rules of the fls we consider the properties of robot dynamics and the decomposition of the uncertainty function the proposed controllers are robust not only to the structured uncertainty such as payload parameter but also to the unstructured one such as friction model and disturbance the validity of the control scheme is shown by computer simulations of a two link planar robot manipulator

robust fuzzy model following control of robot manipulators a robust fuzzy model following control system is proposed for the control of robot manipulators the application field to n link robot manipulators with torque disturbance and measurement noise is addressed the control objective is obtained by tailoring a nominal adaptation process of parameters to implement appropriate function approximation and facilitating a self tuning mechanism on the consequent membership functions to overcome the equivalent uncertainty a novel fuzzy system with self tuning mechanism provides robust property and the rule base in the form of if situation then the control input the proposed multilayer fuzzy logic controller can improve both transient and stability margins without a priori knowledge about the dynamic model or parameters of the robotic system using the lyapunov stability method the uniform ultimate boundedness of tracking error has been proved the performance is demonstrated by simulating the control of a two link robot in various situations

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

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

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

state observer based adaptive fuzzy output feedback control for a class of uncertain nonlinear systems this paper aims to develop state observer based adaptive fuzzy control techniques for controlling a class of uncertain nonlinear systems with bounded external disturbances an adaptive fuzzy observer is proposed to estimate the system state variables it is shown that the observation errors obtained from the observer are uniformly ultimately bounded applying the estimated system state for design of an output feedback controller the uniformly ultimate boundedness of the tracking errors for the resulting closed loop system can be guaranteed a typical robot arm system is employed in our simulation studies and the results demonstrate the usefulness and effectiveness of the proposed techniques for controlling nonlinear systems with bounded external disturbances

quantum key distribution based on orthogonal states allows secure quantum bit commitment for more than a decade it was believed that unconditionally secure quantum bit commitment qbc is impossible but based on a previously proposed quantum key distribution scheme using orthogonal states here we build a qbc protocol in which the density matrices of the quantum states encoding the commitment do not satisfy a crucial condition on which the no go proofs of qbc are based thus the no go proofs could be evaded our protocol is fault tolerant and very feasible with currently available technology it reopens the venue for other post cold war multi party cryptographic protocols e g quantum bit string commitment and quantum strong coin tossing with an arbitrarily small bias this result also has a strong influence on the clifton bub halvorson theorem which suggests that quantum theory could be characterized in terms of information theoretic constraints

unbalanced repeated measures models with structured covariance matrices the question of how to analyze unbalanced or incomplete repeated measures data is a common problem facing analysts we address this problem through maximum likelihood analysis using a general linear model for expected responses and arbitrary structural models for the within subject covariances models that can be fit include standard univariate and multivariate models with incomplete data random effects models and models with time series and factor analytic error structures we describe newton raphson and fisher scoring algorithms for computing maximum likelihood estimates and generalized em algorithms for computing restricted and unrestricted maximum likelihood estimates an example fitting several models to a set of growth data is included

robot control via robust estimated state feedback the problem of controlling a robot manipulator without needing the precise knowledge of the robot model physical parameters and by using only angular position feedback is considered sliding observers are used to estimate the link velocities this observer and a nonlinear control law are modified according to the leitmann procedure in order to face uncertainties on the robot model parameters and to yield locally exponentially closed loop systems the use of discontinuous functions seems to be necessary to reach exponential stability for this particular problem formulation the same type of results would be difficult if not impossible to obtain using only nonlinear smooth functions because the switching terms generate a special kind of dynamics into the sliding patch which is created only by the sign functions

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

adaptive fuzzy control of a class of siso nonaffine nonlinear systems this paper presents a direct adaptive fuzzy control scheme for a class of uncertain continuous time single input single output siso nonaffine nonlinear dynamic systems based on the implicit function theory the existence of an ideal controller that can achieve control objectives is firstly shown since the implicit function theory guarantees only the existence of the ideal controller and does not provide a way for constructing it a fuzzy system is employed to approximate this unknown ideal control law the adjustable parameters in the used fuzzy system are updated using a gradient descent adaptation algorithm this algorithm is designed in order to minimize a quadratic cost function of the error between the unknown ideal implicit controller and the used fuzzy control law the stability analysis of the closed loop system is performed using a lyapunov approach in particular it is shown that the tracking error converges to a neighborhood of zero the effectiveness of the proposed adaptive control scheme is demonstrated through the simulation of a simple nonaffine nonlinear system

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

robust methods for testing the pattern of a single covariance matrix summary patterns for a single covariance matrix may be studied using likelihood ratio or quadratic form test statistics typically these tests have an asymptotic chi squared distribution when the data are normally distributed but the asymptotic distribution is sensitive to nonnormality this paper suggests two ways to robustify inference for patterned matrices i estimate the critical values of normal theory test statistics using a new bootstrap procedure which takes advantage of certain invariances under ho and ii modify the statistics so that an asymptotic chi squared distribution holds even under nonnormality

bootstrap methods for testing homogeneity of variances this article describes the use of bootstrap methods for the problem of testing homogeneity of variances when means are not assumed equal or known the methods are new in this context and allow the use of normal theory test statistics such as f s 2 1 s 2 2 without the normality assumption that is crucial for validity of critical values obtained from the f distribution both asymptotic analysis and monte carlo sampling show that the new resampling procedures compare favorably with older methods in terms of test validity and power

biometrika centenary nonparametrics summary contributions in biometrika to nonparametric statistics are surveyed with particular emphasis on more recent work it is argued that even in the journal s very early years it had interests in smoothing methods for density estimation and in techniques for nonparametric regression in addition to its more obvious focus on classical nonparametric statistics such as ranking methods biometrika s early involvement with permutation testing has links to its contemporary work on bootstrap methods emphases on different types of nonparametric statistics have evolved over time however a rapidly increasing proportion of the journal s pages is devoted to papers that include some use of nonparametric ideas or methods

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 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

fuzzy basis functions universal approximation and orthogonal least squares learning fuzzy systems are represented as series expansions of fuzzy basis functions which are algebraic superpositions of fuzzy membership functions using the stone weierstrass theorem it is proved that linear combinations of the fuzzy basis functions are capable of uniformly approximating any real continuous function on a compact set to arbitrary accuracy based on the fuzzy basis function representations an orthogonal least squares ols learning algorithm is developed for designing fuzzy systems based on given input output pairs then the ols algorithm is used to select significant fuzzy basis functions which are used to construct the final fuzzy system the fuzzy basis function expansion is used to approximate a controller for the nonlinear ball and beam system and the simulation results show that the control performance is improved by incorporating some common sense fuzzy control rules

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

bartlett adjustments to the likelihood ratio statistic and the distribution of the maximum likelihood estimator for rather general parametric models a simple connection is established between the bartlett adjustment factor of the log likelihood ratio statistic and the normalizing constant c of the formula c i i 1 2l for the conditional distribution of a maximum likelihood estimator as applied to the full model and the model of the hypothesis tested this leads to a relatively simple demonstration that division of the likelihood ratio statistic by a suitable constant or estimated factor improves the chi squared approximation to its distribution various expressions for these quantities are discussed in particular for the case of a one dimensional parameter an approximation to the constants involved is derived which does not require integration over the sample space

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

quantum bit string commitment a bit string commitment protocol securely commits n classical bits so that the recipient can extract only m n bits of information about the string classical reasoning suggests that bit string commitment implies bit commitment and hence given the mayers lo chau theorem that nonrelativistic quantum bit string commitment is impossible not so there exist nonrelativistic quantum bit string commitment protocols with security parameters epsilon and m that allow a to commit n n m epsilon bits to b so that a s probability of successfully cheating when revealing any bit and b s probability of extracting more than n n m bits of information about the n bit string before revelation are both less than epsilon with a restrictive definition of security against a n can be taken to be o exp cn for a positive constant c i briefly discuss possible applications

remote preparation of arbitrary ensembles and quantum bit commitment the hughston jozsa wootters theorem shows that any finite ensemble of quantum states can be prepared at a distance and it has been used to demonstrate the insecurity of all bit commitment protocols based on finite quantum systems without superselection rules in this paper we prove a generalized hjw theorem for arbitrary ensembles of states on a c algebra we then use this result to demonstrate the insecurity of bit commitment protocols based on infinite quantum systems and quantum systems with abelian superselection rules

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 approximate disturbance decoupling of mimo nonlinear systems by backstepping and application to chemical processes fuzzy approximate disturbance decoupling concept is introduced for a class of multiple input multiple output mimo nonlinear systems with completely unknown nonlinearities based on backstepping technique a fuzzy almost disturbance decoupling control scheme is proposed the fuzzy controllers guarantee internal uniform ultimate boundedness of the closed loop adaptive systems and render a bounded approximate l sub 2 gain from the disturbance input to the output the developed design scheme is applied to control a two continuous stirred tank reactor process the simulation results illustrate the effectiveness of the method proposed in this paper

fuzzy model reference adaptive control this paper investigates a fuzzy model reference adaptive controller fmrac for continuous time multiple input multiple output mimo nonlinear systems the proposed adaptive scheme uses a takagi seguno ts fuzzy adaptive system which allows for the inclusion of a priori information in terms of qualitative knowledge about the plant operating points or analytical regulators e g state feedback for those operating points a proportional integral update law is used to obtain a fast parameters adaptation stability and robustness of this adaptive scheme are established using lyapunov stability tools the simulation results for a two link robot confirm the performance of the proposed approach

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

a computed torque controller for uncertain robotic manipulator systems fuzzy approach computed torque control ctc is an effective motion control strategy for robotic manipulator systems which can ensure globally asymptotic stability however ctc scheme requires precise dynamical models of robotic manipulators to handle this impossibility in this paper a new approach combing ctc and fuzzy control fc is developed for trajectory tracking problems of robotic manipulators with structured uncertainty and or unstructured uncertainty fuzzy part with a set of tunable parameters is employed to approximate lumped uncertainty due to parameters variations unmodeled dynamics and so on in robotic manipulators based on lyapunov stability theorem it is shown that the proposed controller can guarantee stability of closed loop systems and satisfactory tracking performances the proposed approach indicates that ctc method is also valid for controlling uncertain robotic manipulators as long as compensative controller is appropriately designed finally computer simulation results on a two link elbow planar robotic manipulator are presented to show tracking capability and effectiveness of the proposed scheme

brief intelligent optimal control of robotic manipulators using neural networks the paper is concerned with the application of quadratic optimization for motion control to feedback control of robotic systems using neural networks explicit solutions to the hamilton jacobi bellman h j b equation for optimal control of robotic systems are found by solving an algebraic riccati equation it is shown how neural networks can cope with nonlinearities through optimization with no preliminary off line learning phase required the adaptive learning algorithm is derived from lyapunov stability analysis so that both system tracking stability and error convergence can be guaranteed in the closed loop system the filtered tracking error or critic gain and the lyapunov function for the nonlinear analysis are derived from the user input in terms of a specified quadratic performance index simulation results on a two link robot manipulator show the satisfactory performance of the proposed control schemes even in the presence of large modeling uncertainties and external disturbances

robust fuzzy logic control of mechanical systems an approach for the design of fuzzy control laws for tracking control of a large class of mechanical systems is proposed the approach employs the framework of lyapunov s stability theory to formulate a class of control laws that guarantee convergence of the tracking errors to within specification limits in presence of bounded parameter uncertainties and input disturbances the proposed control laws possess a large number of parameters and functional relationships to be chosen by the designer according to a methodology developed in the paper the large number of design degrees of freedom makes the approach suitable for fuzzy logic implementation a number of fuzzy implementations of the proposed control methodology are provided all implementations guarantee tracking error convergence to within prespecified performance limits an extensive simulation study using a model of a two degree of freedom robot manipulator was conducted fuzzy and non fuzzy implementations of the proposed methodology were compared to control laws designed using other design methods simulation study results indicate a superiority of the proposed control methodology compared to other approaches the study also demonstrates better performance of the fuzzy control implementation compared to its non fuzzy counterpart

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

an observer based approach to controlling time delay chaotic systems via takagi sugeno fuzzy model this study presents a fuzzy model based control criterion for time delay chaotic systems with uncertainty the takagi sugeno t s fuzzy model is adopted for representing a chaotic system possessing a time delay a unified approach for designing a fuzzy observer which integrates parallel distributed compensation with an adaptive updating method is first discussed an observer based fuzzy control scheme is then developed to deal with stabilization and tracking problems based on lyapunov stability analysis sufficient conditions that ensure robust control performance of time delay chaotic systems are derived these conditions are represented in terms of linear matrix inequalities and adaptive updating laws finally the proposed approach is validated through numerical synchronization stabilization and model tracking control simulations

neuro fuzzy adaptive control based on dynamic inversion for robotic manipulators this paper presents a stable neuro fuzzy nf adaptive control approach for the trajectory tracking of the robotic manipulator with poorly known dynamics firstly the fuzzy dynamic model of the manipulator is established using the takagi sugeno t s fuzzy framework with both structure and parameters identified through input output data from the robot control process secondly based on the derived fuzzy dynamics of the robotic manipulator the dynamic nf adaptive controller is developed to improve the system performance by adaptively modifying the fuzzy model parameters on line the dynamic nf system aims to approximate the whole robot dynamics rather than its nonlinear components as is done by static neural networks the dynamic inversion introduced for the controller design is constructed by the dynamic nf system and will help the nf controller design because it does not require the assumption that the robot states should be within a compact set it is generally known that the compact set cannot be specified before the control loop is closed thirdly the system stability and the convergence of tracking errors are guaranteed by lyapunov stability theory and the learning algorithm for the dynamic nf system is obtained thereby finally simulation studies are carried out to show the viability and effectiveness of the proposed control approach

nested analysis of variance with autocorrelated errors summary in this paper we consider the problem where there is a randomized experimental design with several successive time measurements on each experimental unit one approach to the analysis of such data is to treat time as the subplot treatment and to use a split plot analysis of variance alternatively the problem may be considered in a more general multivariate framework here we recognize the timeinduced correlations and apply an autoregressive time series modelling approach estimation and testing are addressed two examples are presented to illustrate the practicality of our procedure some extensions are also considered briefly frequently data arise from a randomized experimental design where several successive measurements over time are made on each experimental unit there are many examples such as the following i in medical trials observations may be taken on each subject at regular time intervals ii in wildlife radio telemetry studies the distance moved by each animal may be tracked during successive periods of a day iii in plant animal experiments observations may be taken on the same plant animal as it grows and changes over time in all cases there will be a correlation between observations taken on the same experimental unit at different time points it is also likely that there is a decay in the correlation with increasing time lags in other words observations on the same experimental unit which are close together should be more highly correlated than those that are far apart in time this is contrary to the assumption of constant correlation between all time points subplots within a unit whole plot made by the traditional split plot analysis an alternative approach is to consider a general multivariate analysis of variance manova model which assumes an arbitrary covariance matrix for the observations see timm 1980 however we believe that the multivariate model is overparametrized and also it cannot easily incorporate missing values therefore we consider a time series modelling approach to the problem which serves as a compromise we now present the time series model that we consider for analyzing such data we denote the variable under study by the letter y with two subscripts where the first subscript refers to the individual experimental units and the second subscript refers to the successive measurements for the particular individual we assume that n individuals are selected at random and that measurements are taken at ti consecutive time points on the ith individual we consider a linear model r