adaptive fuzzy tracking control of nonlinear time delay systems with dead zone output mechanism based on a novel smooth model this paper presents a novel fuzzy adaptive controller for controlling a class of dead zone output nonlinear systems with time delays a new approximate model is first designed to describe a special dead zone phenomenon encountered by the output mechanism of nonlinear systems and the proposed smooth model can be conveniently fused with available adaptive fuzzy control techniques in addition the coupling effect that the dead zone output and the time delayed states coexist in a common coupling function makes the tracking control design more complicated to further address this difficulty a compensation method using mean value theorem with lyapunov krasovskii function is presented in this paper by using the proposed output dead zone model and based on lyapunov synthesis a new optimized algorithm is developed to guarantee the prescribed convergence of tracking error and the boundedness of all the signals in the closed loop systems simulations have been implemented to verify the performance of the proposed fuzzy adaptive controller

discrete time adaptive control in the presence of input constraints the problem of ensuring that a control input remains within certain bounds is ubiquitous in all control applications this problem becomes even more difficult in the context of adaptive control since the plant to be controlled has unknown parameters in this paper we establish conditions under which minimum phase discrete time plants can be adaptively controlled in a stable manner in the presence of amplitude constraints on the input all open loop stable plants are shown to result in global stability with the proposed adaptive controller and open loop unstable plants are shown to result in local stability when the initial conditions of the closed loop system lie within a compact set it is also shown that when initial conditions exceed a certain value open loop unstable plants can become unstable it is furthermore shown that the adaptive algorithms can be appropriately modified in the presence of exogeneous disturbances and certain modeling errors to yield boundedness simulations are presented to support the theoretical derivations

globally stable adaptive system design for minimum phase siso plants with input saturation model reference adaptive control problem for single input single output time invariant continuous time plants with input saturation is considered with main attention focused on global properties a sufficient condition is presented and a new design method of adaptive control systems is proposed if a priori information about the plant is available to choose the reference model and the reference input so that the sufficient condition holds the closed loop adaptive control system designed by the proposed method can have global stability and globally output tracking property it is shown that the sufficient condition becomes necessary in some cases

on the dispersion measure of owa operators we describe some basic features of the owa operator we turn to the problem of determining the weights associated with this operator and particularly the maximal dispersion entropy approach we consider the possibility of using minimization of dispersion after discussing concerns with both maximization and minimization of dispersion we investigate the possibility of finding an optimal solution intermediate to these extremes we next consider alternative measures of dispersion we introduce a fundamental requirement for a measure of dispersion called the preference for equal division a number of general classes of dispersion measures are provided notable among these are those based on t norm and t conorm operators

a modified forward backward splitting method for maximal monotone mappings we consider the forward backward splitting method for finding a zero of the sum of two maximal monotone mappings this method is known to converge when the inverse of the forward mapping is strongly monotone we propose a modification to this method in the spirit of the extragradient method for monotone variational inequalities under which the method converges assuming only the forward mapping is lipschitz continuous on some closed convex subset of its domain the modification entails an additional forward step and a projection step at each iteration applications of the modified method to decomposition in convex programming and monotone variational inequalities are discussed

quantifiers induced by subjective expected value of sample information with bernstein polynomials abstract a kind of personalized quantifier the so called sevsi induced quantifier as an acronym for subjective expected value of sample information is developed in this paper by introducing bernstein polynomials of higher degree this allows us to provide a novel solution to improve the final representation of the quantifier that generally performed poorly in our previous work thus enhancing the quality of global approximation of functions and improving the operability of this kind of quantifier for practical use we show some properties of the developed quantifier we also prove the consistency of the owa aggregation under the guidance of this type of quantifier finally we experimentally show that the developed quantifier outperforms the one with the piecewise linear interpolation in many aspects of geometrical characteristics and operability thus it could be considered as an effective analytical tool to help handle the complex cases involving people s personalities or behavior intentions that have to be considered in decision making under uncertainty

rate constrained adaptive control the problem of direct adaptive control subject to a control rate constraint is considered theoretical results concerned with the system boundedness and control tracking properties are presented these results require a finite gain stability condition of the associated non adaptive control system the results are applied to a stable plant

adaptive tracking control of uncertain mimo nonlinear systems with input constraints in this paper adaptive tracking control is proposed for a class of uncertain multi input and multi output nonlinear systems with non symmetric input constraints the auxiliary design system is introduced to analyze the effect of input constraints and its states are used to adaptive tracking control design the spectral radius of the control coefficient matrix is used to relax the nonsingular assumption of the control coefficient matrix subsequently the constrained adaptive control is presented where command filters are adopted to implement the emulate of actuator physical constraints on the control law and virtual control laws and avoid the tedious analytic computations of time derivatives of virtual control laws in the backstepping procedure under the proposed control techniques the closed loop semi global uniformly ultimate bounded stability is achieved via lyapunov synthesis finally simulation studies are presented to illustrate the effectiveness of the proposed adaptive tracking control

adaptive neural tracking control for stochastic nonlinear strict feedback systems with unknown input saturation in this paper the problem of adaptive neural tracking control is considered for a class of single input single output siso strict feedback stochastic nonlinear systems with input saturation to deal with the non smooth input saturation nonlinearity a smooth nonaffine function of the control input signal is used to approximate the input saturation function classical adaptive technique and backstepping are used for control synthesis based on the mean value theorem a novel adaptive neural control scheme is systematically derived without requiring the prior knowledge of bound of input saturation it is shown that under the action of the proposed adaptive controller all the signals of the closed loop system remain bounded in probability and the tracking error converges to a small neighborhood around the origin in the sense of mean quartic value two simulation examples are provided to demonstrate the effectiveness of the presented results

compatible weighting method with rank order centroid maximum entropy ordered weighted averaging approach in a situation where imprecise attribute weights such as a rank order are captured various approximate weighting methods have been proposed to aid multiattribute decision analysis among others it is well known that the rank order centroid roc weights result in the highest performance in terms of the identification of the best alternative under the ranked attribute weights in this paper we aim to reinterpret the meaning of the roc weights and to develop a compatible weighting method that is based on other well established academic disciplines the ordered weighted averaging owa method is a nonlinear aggregation method in that the weights are associated with the objects reordered according to their magnitudes in the aggregation process some interesting semantics can be attached to the approximate weights in view of the measure developed in the owa method furthermore the weights generated by the maximum entropy method show equally compatible performance with the roc weights under some condition which is demonstrated by theoretical and simulation analysis

continuous direct adaptive control with saturation input constraint this note establishes global boundedness and control tracking properties of a continuous time direct adaptive controller subject to an input saturation constraint under a finite gain stability condition on a nonadaptive controller using the true plant parameters global stability of the adaptive version of the controller is established it is also shown that the adaptive controller can account for modeling uncertainties and limited disturbances hence providing a certain degree of robustness

adaptive one step ahead control subject to an input amplitude constraint this paper investigates the adaptive control of a single input single output process when there exists a constraint on the amplitude of the applied control signal in particular an adaptive control scheme is proposed and is shown to be globally convergent in the sense that the control asymptotically matches the amplitude constrained one step ahead optimal control some simulation results are presented to demonstrate the performance of the scheme

stability of the modified projected dynamical systems abstract in this paper we propose and analyse a modified projection type dynamical system associated with variational inequalities by using the technique of updating the solution we prove that the globally asymptotic stability of this dynamical system requires only the pseudomonotonicity of the underlying operator which is a weaker condition than monotonicity the results obtained in this paper represent a significant improvement of the previously known results

implicit dynamical systems and quasi variational inequalities in this paper we propose and analyze some implicit projected dynamical systems associated with quasi variational inequalities by using the techniques of the projection and the wiener hopf equations we prove that the globally asymptotic stability of these dynamical systems requires only the pseudomonotonicity of the operator which is a weaker condition than monotonicity we also discuss some special cases which can be obtained from our main results the results obtained in this paper represent a significant improvement of the previously known results

implicit resolvent dynamical systems for quasi variational inclusions in this paper we suggest and analyze a class of implicit resolvent dynamical systems for quasi variational inclusions by using the resolvent operator technique we show that the trajectory of the solution of the implicit dynamical system converges globally exponentially to the unique solution of the quasi variational inclusions our results can be considered as a significant extension of the previously known results

dynamic surface control using neural networks for a class of uncertain nonlinear systems with input saturation in this paper a dynamic surface control dsc scheme is proposed for a class of uncertain strict feedback nonlinear systems in the presence of input saturation and unknown external disturbance the radial basis function neural network rbfnn is employed to approximate the unknown system function to efficiently tackle the unknown external disturbance a nonlinear disturbance observer ndo is developed the developed ndo can relax the known boundary requirement of the unknown disturbance and can guarantee the disturbance estimation error converge to a bounded compact set using ndo and rbfnn the dsc scheme is developed for uncertain nonlinear systems based on a backstepping method using a dsc technique the problem of explosion of complexity inherent in the conventional backstepping method is avoided which is specially important for designs using neural network approximations under the proposed dsc scheme the ultimately bounded convergence of all closed loop signals is guaranteed via lyapunov analysis simulation results are given to show the effectiveness of the proposed dsc design using ndo and rbfnn

stability analysis of input constrained continuous time indirect adaptive control abstract stability analysis of continuous time indirect pole placement adaptive control with input constraint is presented in this paper it is shown that the closed loop system for a class of stable type 1 plants is globally stable in the sense that all the signals in the loop remain bounded and that certain degree of robustness with respect to modelling uncertainties and disturbances can be achieved by implementing a simple fixed dead zone in the parameter estimator it is also shown that if the plant is free from modelling uncertainties and disturbances and the control input is not saturated for a finite period of time then the closed loop system will asymptotically be characterized by the desired closed loop poles within this time period

adaptive control in the presence of input constraints this paper deals with the problem of adaptively controlling a linear time invariant plant in the presence of constraints on the input amplitude we introduce a new adaptive algorithm which ensures that the plant can be locally stabilized in addition to the standard assumptions which are required for adaptive control in the ideal case an upper bound on the plant control parameter is required to be known the results are evaluated by simulation studies

adaptive tracking with saturating input and controller integral action the authors consider the problem of controlling stable linear plants subject to input saturation constraints and parametric uncertainties a globally stable adaptive controller which performs well in the presence of such constraints is designed using a specific pole placement that accounts for the saturation constraint an integral action to deal with the tracking problem and an adaptive control law with an ad hoc excitation capability together with a parameter adaptation to cope with the parametric uncertainty the proposed controller allows one to track slowly varying reference sequences particularly in the case of a constant reference the output tracking error vanishes asymptotically and the control input does stop saturating after a finite time

brief adaptive control of input constrained type 1 plants stabilization and tracking we consider the problem of controlling non minimum phase type 1 plants in presence of model uncertainty and a saturation constraint imposed on the control input the control objectives are the following i robust global asymptotic stabilization of the controlled plant with respect to a multiplicative modeling error ii output regulation in presence of external perturbations and iii tracking of output reference trajectories the problem is dealt with using a saturated indirect adaptive regulator based on a specific pole placement of the closed loop poles the slower the reference trajectory and the perturbation sequence the better the tracking and regulation quality

robust adaptive neural network control for a class of uncertain mimo nonlinear systems with input nonlinearities in this paper robust adaptive neural network nn control is investigated for a general class of uncertain multiple input multiple output mimo nonlinear systems with unknown control coefficient matrices and input nonlinearities for nonsymmetric input nonlinearities of saturation and deadzone variable structure control vsc in combination with backstepping and lyapunov synthesis is proposed for adaptive nn control design with guaranteed stability in the proposed adaptive nn control the usual assumption on nonsingularity of nn approximation for unknown control coefficient matrices and boundary assumption between nn approximation error and control input have been eliminated command filters are presented to implement physical constraints on the virtual control laws then the tedious analytic computations of time derivatives of virtual control laws are canceled it is proved that the proposed robust backstepping control is able to guarantee semiglobal uniform ultimate boundedness of all signals in the closed loop system finally simulation results are presented to illustrate the effectiveness of the proposed adaptive nn control

siso linear system control via saturating actuator l2 tracking performance in the presence of arbitrary shaped inputs most studies on linear system control in the presence of actuator saturation have focused on closed loop asymptotic stabilisation available tracking results are generally limited to constraint compatible inputs references disturbances with known exogenous input models the problem of output reference tracking in the presence of arbitrary shaped not necessarily constraint compatible inputs has yet to be investigated in this study we show that an tracking performance is achievable for siso linear systems using a given class of saturated controllers accordingly the tracking error energy resp power is bounded by the energy resp power of a well defined mismatch input depending on the disturbance and reference compatibility in the disturbance free case this entails perfect asymptotic tracking of arbitrary shaped constraint compatible reference signals otherwise the average tracking quality is proportional to the mismatch input power in the case of periodic inputs constraint compatible or not

adaptive neural output feedback control of output constrained nonlinear systems with unknown output nonlinearity this paper addresses the problem of adaptive neural output feedback control for a class of special nonlinear systems with the hysteretic output mechanism and the unmeasured states a modified bouc wen model is first employed to capture the output hysteresis phenomenon in the design procedure for its fusion with the neural networks and the nussbaum type function two key lemmas are established using some extended properties of this model to avoid the bad system performance caused by the output nonlinearity a barrier lyapunov function technique is introduced to guarantee the prescribed constraint of the tracking error in addition a robust filtering method is designed to cancel the restriction that all the system states require to be measured based on the lyapunov synthesis a new neural adaptive controller is constructed to guarantee the prescribed convergence of the tracking error and the semiglobal uniform ultimate boundedness of all the signals in the closed loop system simulations are implemented to evaluate the performance of the proposed neural control algorithm in this paper

stability of rate constrained robust pole placement adaptive control systems abstract this paper presents stability analysis for discrete time pole placement adaptive control systems with input rate saturation constraints it is shown that with appropriate design the robust adaptive control system is stable for a class of stable minimum phase plants in the sense that all the signals in the loop remain bounded in the presence of modelling uncertainties and disturbances if the plant is free from modelling uncertainties and disturbances and the control input rate remains unsaturated for a period of time then the closed loop adaptive control system will asymptotically be characterized by the desired closed loop poles within this period

stable adaptive control for not necessarily stable discrete linear plants with bounded non linear inputs abstract this paper deals with the design of stable adaptive control schemes for discrete plants under non linear bounded inputs two algorithms are studied one of them being a generalization of the d step ahead standard algorithm while the second one involves the use of a time decreasing adaptation gain the class of inputs for which the algorithms perform in a stable fashion include saturations relays and saturated dead zones the plant stability assumption is not requested to prove standard non asymptotic stability results the mechanism to achieve scheme s stability is the use of an extra weighted additive term in the adaptation error which takes into account the output deviation with respect to that associated with linear inputs

observer based adaptive nn control for a class of uncertain nonlinear systems with nonsymmetric input saturation this paper is concerned with the problem of adaptive tracking control for a class of uncertain nonlinear systems with nonsymmetric input saturation and immeasurable states the radial basis function of neural network nn is employed to approximate unknown functions and an nn state observer is designed to estimate the immeasurable states to analyze the effect of input saturation an auxiliary system is employed by the aid of adaptive backstepping technique an adaptive tracking control approach is developed under the proposed adaptive tracking controller the boundedness of all the signals in the closed loop system is achieved moreover distinct from most of the existing references the tracking error can be bounded by an explicit function of design parameters and saturation input error finally an example is given to show the effectiveness of the proposed method

day to day dynamic network disequilibria and idealized traveler information systems in this paper we present tatonnement models for calculating static wardropian user equilibria on congested networks with fully general demand and cost structures we present both a qualitative analysis of stability and numerical studies which show that such an approach provides a reliable means for determining static user equilibria we also describe circumstances for which these models depict day to day adjustments from one realizable disequilibrium state to another and how these adjustment processes differ depending on the quality of the information being provided by abstract traveler information systems specifically we demonstrate that such dynamic adjustment processes settle down to equilibria both when information is complete and when it is incomplete