adaptive tracking control for switched nonlinear systems with fuzzy actuator backlash abstract this paper concentrates on the adaptive tracking control problem for a class of switched nonlinear systems in nonstrict feedback form with uncertain actuator backlash the switching signal considered in our model is arbitrary the adaptive tracking controller design procedure is divided into three parts firstly a novel fuzzy backlash model is presented to describe the uncertain backlash then the fuzzy backlash is defuzzified and approximated by the linear combination of a linear term and a disturbance like term without constructing a backlash inverse finally an adaptive backstepping based tracking controller via common lyapunov function clf method is presented to guarantee the tracking error asymptotically converges to an adjustable region of zero the stability analysis and simulation examples are presented at the end of this paper to further verify effectiveness of our control scheme

practical adaptive fuzzy tracking control for a class of perturbed nonlinear systems with backlash nonlinearity abstract this paper addresses the adaptive fuzzy tracking control problem for a class of perturbed nonlinear systems with backlash nonlinearity in the proposed control scheme fuzzy logic systems are used to approximate the unknown nonlinearity in each backstepping design and the classic adaptive technique is employed to deal with some unknown parameters the proposed fuzzy controller is developed by introducing some non negative switching functions and novel parameter adaptation learning laws are designed using the modification method it is emphasized that with the help of novel parameter adaptation laws the high gain phenomenon in the previous work can be avoided in addition by borrowing the property of the introduced non negative functions and the inequality technique an adaptive technique is employed to estimate the norm of the ideal weight vector such that the number of adaptation learning parameters online is reduced by employing barbalat s lemma it can be proved that all the signals in the closed loop system remain semi globally uniformly ultimately bounded and especially that the tracking error converges to an accuracy predefined a priori two simulation examples are provided to verify the effectiveness of the proposed control scheme

on the universality of almost every quantum logic gate lloyd phys rev lett 75 346 1995 showed that almost every quantum logic gate is universal in the sense that it can be used to approximate any unitary transformation the argument relied on a more general fact whose proof was not given in detail we give a complete proof of this more general fact

stabilisation of a thermo elastic system under nonuniformly bounded disturbances in the boundary input abstractwe consider the exponential stabilisation for a thermo elastic system of type ii with nonuniformly bounded external disturbances in the boundary control we begin with finding feedback controls for the system without disturbances by lyapunov function approach then based on the resulting feedback control we give new dynamic feedback control laws that make the system exponentially stable by tracking disturbances exponentially through a time varying high gain observer we apply the linear operator semi group theory to prove the well posedness of the resulting closed system finally we prove that the solution of the closed loop system can approach zero polynomially or exponentially by the lyapunov function approach

adaptive neural tracking control for a class of nonstrict feedback stochastic nonlinear systems with unknown backlash like hysteresis this paper considers the problem of adaptive neural control of stochastic nonlinear systems in nonstrict feedback form with unknown backlash like hysteresis nonlinearities to overcome the design difficulty of nonstrict feedback structure variable separation technique is used to decompose the unknown functions of all state variables into a sum of smooth functions of each error dynamic by combining radial basis function neural networks universal approximation capability with an adaptive backstepping technique an adaptive neural control algorithm is proposed it is shown that the proposed controller guarantees that all the signals in the closed loop system are four moment semiglobally uniformly ultimately bounded and the tracking error eventually converges to a small neighborhood of the origin in the sense of mean quartic value simulation results further show the effectiveness of the presented control scheme

improvements in shape from focus for holographic reconstructions with regard to focus operators neighborhood size and height value interpolation this paper presents a shape from focus method which is improved with regard to the mathematical operator used for contrast measurement the selection of the neighborhood size surface refinement through interpolation and surface postprocessing three dimensional models of living human faces are presented with such a high resolution that single hairs are visible

robust adaptive control of uncertain nonlinear systems in the presence of input saturation and external disturbance in this technical note we consider adaptive control of single input uncertain nonlinear systems in the presence of input saturation and unknown external disturbance by using backstepping approaches two new robust adaptive control algorithms are developed by introducing a well defined smooth function and using a nussbaum function the nussbaum function is introduced to compensate for the nonlinear term arising from the input saturation unlike some existing control schemes for systems with input saturation the developed controllers do not require assumptions on the uncertain parameters within a known compact set and a priori knowledge on the bound of the external disturbance besides showing global stability transient performance is also established and can be adjusted by tuning certain design parameters

rigid structure from motion from a blind source separation perspective we present an information theoretic approach to define the problem of structure from motion sfm as a blind source separation one given that for almost all practical joint densities of shape points the marginal densities are non gaussian we show how higher order statistics can be used to provide improvements in shape estimates over the methods of factorization via singular value decomposition svd bundle adjustment and bayesian approaches previous techniques have either explicitly or implicitly used only second order statistics in models of shape or noise a further advantage of viewing sfm as a blind source problem is that it easily allows for the inclusion of noise and shape models resulting in maximum likelihood ml or maximum a posteriori map shape and motion estimates a key result is that the blind source separation approach has the ability to recover the motion and shape matrices without the need to explicitly know the motion or shape pdf we demonstrate that it suffices to know whether the pdf is sub or super gaussian i e semi parametric estimation and derive a simple formulation to determine this from the data we provide extensive experimental results on synthetic and real tracked points in order to quantify the improvement obtained from this technique

depth estimation of face images using the nonlinear least squares model in this paper we propose an efficient algorithm to reconstruct the 3d structure of a human face from one or more of its 2d images with different poses in our algorithm the nonlinear least squares model is first employed to estimate the depth values of facial feature points and the pose of the 2d face image concerned by means of the similarity transform furthermore different optimization schemes are presented with regard to the accuracy levels and the training time required our algorithm also embeds the symmetrical property of the human face into the optimization procedure in order to alleviate the sensitivities arising from changes in pose in addition the regularization term based on linear correlation is added in the objective function to improve the estimation accuracy of the 3d structure further a model integration method is proposed to improve the depth estimation accuracy when multiple nonfrontal view face images are available experimental results on the 2d and 3d databases demonstrate the feasibility and efficiency of the proposed methods

adaptive variable structure control of a class of nonlinear systems with unknown prandtl ishlinskii hysteresis control of nonlinear systems preceded by unknown hysteresis nonlinearities is a challenging task and has received increasing attention in recent years due to growing industrial demands involving varied applications in the literature many mathematical models have been proposed to describe the hysteresis nonlinearities the challenge addressed here is how to fuse those hysteresis models with available robust control techniques to have the basic requirement of stability of the system the purpose of the note is to show such a possibility by using the prandtl ishlinskii pi hysteresis model an adaptive variable structure control approach serving as an illustration is fused with the pi model without necessarily constructing a hysteresis inverse the global stability of the system and tracking a desired trajectory to a certain precision are achieved simulation results attained for a nonlinear system are presented to illustrate and further validate the effectiveness of the proposed approach

adaptive neural network dynamic surface control for a class of time delay nonlinear systems with hysteresis inputs and dynamic uncertainties in this paper an adaptive neural network nn dynamic surface control is proposed for a class of time delay nonlinear systems with dynamic uncertainties and unknown hysteresis the main advantages of the developed scheme are 1 nns are utilized to approximately describe nonlinearities and unknown dynamics of the nonlinear time delay systems making it possible to deal with unknown nonlinear uncertain systems and pursue the mathcal l infty performance of the tracking error 2 using the finite covering lemma together with the nns approximators the krasovskii function is abandoned which paves the way for obtaining the mathcal l infty performance of the tracking error 3 by introducing an initializing technique the mathcal l infty performance of the tracking error can be achieved 4 using a generalized prandtl ishlinskii pi model the limitation of the traditional pi hysteresis model is overcome and 5 by applying the young s inequalities to deal with the weight vector of the nns the updated laws are needed only at the last controller design step with only two parameters being estimated which reduces the computational burden it is proved that the proposed scheme can guarantee semiglobal stability of the closed loop system and achieves the mathcal l infty performance of the tracking error simulation results for general second order time delay nonlinear systems and the tuning metal cutting system are presented to demonstrate the efficiency of the proposed method

elastic shape texture matching for human face recognition in this paper a novel elastic shape texture matching method namely estm for human face recognition is proposed in our approach both the shape and the texture information are used to compare two faces without establishing any precise pixel wise correspondence the edge map is used to represent the shape of an image while the texture information is characterized by both the gabor representations and the gradient direction of each pixel combining these features a shape texture hausdorff distance is devised to compute the similarity of two face images the elastic matching is robust to small local distortions of the feature points such as those caused by facial expression variations in addition the use of the edge map gabor representations and the direction of the image gradient can all alleviate the effect of illumination to a certain extent with different databases experimental results show that our algorithm can always achieve a better performance than other face recognition algorithms under different conditions except when an image is under poor and uneven illumination experiments based on the yale database ar database orl database and yaleb database show that our proposed method can achieve recognition rates of 88 7 97 7 78 3 and 89 5 respectively

asymptotic tracking control of uncertain nonlinear systems with unknown actuator nonlinearity and unknown gain signs two adaptive control schemes for a class of uncertain nonlinear systems preceded by actuator nonlinearity are presented we consider two possible input nonlinearities that is backlash like hysteresis and symmetric dead zone which can be simultaneously dealt with by using the proposed compensation controllers the knowledge on the signs of the unknown plant parameters and the parameters in the input nonlinearity models is not required only the boundedness of the reference signal is assumed the nussbaum gain approach is employed to remove the assumption on the control gain sign the first scheme is based on the backstepping technique the final step of the recursive design is crucial for the closed loop stability in the second scheme a direct adaptive control law is designed the controller has a simple structure and only one adaptation parameter is updated in both controllers it is shown that asymptotic tracking is ensured two numerical examples are provided to show the effectiveness of the theor

adaptive tracking of switched nonlinear systems with prescribed performance using a reference dependent reparametrisation approach in this paper adaptive tracking control of switched nonlinear systems in the parametric strict feedback form is investigated after defining a reparametrisation lemma in the presence of a non zero reference signal we propose a new adaptive backstepping design of the virtual controllers that can handle the extra terms arising from the reparametrisation and that the state of the art backstepping designs cannot dominate the proposed adaptive design guarantees under arbitrarily fast switching an a priori bound for the steady state performance of the tracking error and a tunable bound for the transient error finally the proposed method by overcoming the need for subsystems with common sign of the input vector field enlarges the class of uncertain switched nonlinear systems for which the adaptive tracking problem can be solved a numerical example is provided to illustrate the proposed control scheme

a coupled statistical model for face shape recovery from brightness images we focus on the problem of developing a coupled statistical model that can be used to recover facial shape from brightness images of faces we study three alternative representations for facial shape these are the surface height function the surface gradient and a fourier basis representation we jointly capture variations in intensity and the surface shape representations using a coupled statistical model the model is constructed by performing principal components analysis on sets of parameters describing the contents of the intensity images and the facial shape representations by fitting the coupled model to intensity data facial shape is implicitly recovered from the shape parameters experiments show that the coupled model is able to generate accurate shape from out of training sample intensity images

fuzzy approximation based global adaptive control for uncertain strict feedback systems with a priori known tracking accuracy abstract in this study we propose a novel adaptive backstepping fuzzy control scheme for a class of uncertain strict feedback systems where the tracking accuracy is known a priori and we also introduce a multiswitching based adaptive fuzzy controller compared with the existing method for adaptive fuzzy control the advantages of the proposed scheme are as follows first the controller guarantees that all the closed loop signals are globally uniformly ultimately bounded which differs from most existing adaptive fuzzy control approaches where the semi global boundedness of the closed loop signals is ensured under a harsh assumption on the approximation domain of the fuzzy logic system second our controller ensures that the tracking error converges to an accuracy that is given a priori for the uncertain strict feedback system which cannot be achieved using existing adaptive fuzzy control methods third based on some nonnegative functions we analyze the convergence of the tracking error using barbalat s lemma fourth the main technical novelty is the construction of three new n th order continuously differentiable switching functions which are used to design the desired controller finally three simulation examples are provided that illustrate the effectiveness of the proposed control strategy

exponential stabilization of conservation systems with interior disturbance abstract in this paper we consider the stabilization problem of the conservation systems with interior disturbance employing the idea of sliding mode control we design a nonlinear distributed feedback controller we prove the solvability of the resulted closed loop system by the maximal monotone operator theory further we prove the exponential stability of the closed loop system in particular we prove that the requirement of a classical solution in the lyapunov approach is unnecessary

robust processing of optical flow of fluids this paper proposes a new approach coupling physical models and image estimation techniques for modelling the movement of fluids the fluid flow is characterized by turbulent movement and dynamically changing patterns which poses challenges to existing optical flow estimation methods the proposed methodology which relies on navier stokes equations is used for processing fluid optical flow by using a succession of stages such as advection diffusion and mass conservation a robust diffusion step jointly considering the local data geometry and its statistics is embedded in the proposed framework the diffusion kernel is gaussian with the covariance matrix defined by the local second derivatives such an anisotropic kernel is able to implicitly detect changes in the vector field orientation and to diffuse accordingly a new approach is developed for detecting fluid flow structures such as vortices the proposed methodology is applied on artificially generated vector fields as well as on various image sequences

decentralized adaptive stabilization in the presence of unknown backlash like hysteresis due to the difficulty of handling both hysteresis and interactions between subsystems there is still no result available on decentralized stabilization of unknown interconnected systems with hysteresis even though the problem is practical and important in this paper we provide solutions to this challenging problem by proposing two new schemes to design decentralized output feedback adaptive controllers using backstepping approach for each subsystem a general transfer function with arbitrary relative degree is considered the interactions between subsystems are allowed to satisfy a nonlinear bound with certain structural conditions in the first scheme no knowledge is assumed on the bounds of unknown system parameters in case that the uncertain parameters are inside known compact sets we propose an alternative scheme where a projection operation is employed in the adaptive laws in both schemes the effects of the hysteresis and the effects due to interactions are taken into consideration in devising local control laws it is shown that the designed local adaptive controllers can ensure all the signals in the closed loop system bounded a root mean square type of bound is obtained for the system states as a function of design parameters this implies that the transient system performance can be adjusted by choosing suitable design parameters with scheme ii the proposed control laws allow arbitrarily strong interactions provided their upper bounds are available in the absence of hysteresis perfect stabilization is ensured and the l 2 norm of the system states is also shown to be bounded by a function of design parameters when the second scheme is applied

adaptive fuzzy output feedback control of uncertain nonlinear systems with unknown backlash like hysteresis in this paper the problem of adaptive fuzzy output feedback control is investigated for a class of uncertain nonlinear systems with unknown backlash like hysteresis and unmeasured states the fuzzy logic systems are used to approximate the nonlinear system functions and a fuzzy state observer is designed to estimate the unmeasured states by utilizing the fuzzy state observer and combining the adaptive backstepping technique with adaptive fuzzy control design an observer based adaptive fuzzy output feedback control approach is developed it is proved that the proposed control approach can guarantee that all the signals in the closed loop system are semi globally uniformly ultimately bounded suub and both observer error and tracking error can converge to a small neighborhood of the origin two simulations are included to illustrate the effectiveness of the proposed approach

robust adaptive control of a class of nonlinear systems with unknown backlash like hysteresis deals with adaptive control of a class of nonlinear dynamic systems preceded by unknown backlash like hysteresis nonlinearities where the hysteresis is modeled by a differential equation by exploiting solution properties of the differential equation and combining those properties with adaptive control techniques a robust adaptive control algorithm is developed without constructing a hysteresis inverse the new control law ensures global stability of the adaptive system and achieves both stabilization and tracking to within a desired precision simulations performed on a nonlinear system illustrate and clarify the approach

block backstepping control of mimo systems based on the lyapunov stability theorem a methodology of designing the block backstepping controller for a class of multi input multi output mimo systems is proposed to solve the tracking problem some adaptive mechanisms are embedded both in the virtual input controller and in the backstepping controllers so that not only are the perturbations suppressed but also some knowledge of the upper bound of perturbations is not required finally an example of controlling a two axis piezoelectric microposition is used to demonstrate the feasibility of the proposed methodology

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

trajectory space a dual representation for nonrigid structure from motion existing approaches to nonrigid structure from motion assume that the instantaneous 3d shape of a deforming object is a linear combination of basis shapes these bases are object dependent and therefore have to be estimated anew for each video sequence in contrast we propose a dual approach to describe the evolving 3d structure in trajectory space by a linear combination of basis trajectories we describe the dual relationship between the two approaches showing that they both have equal power for representing 3d structure we further show that the temporal smoothness in 3d trajectories alone can be used for recovering nonrigid structure from a moving camera the principal advantage of expressing deforming 3d structure in trajectory space is that we can define an object independent basis this results in a significant reduction in unknowns and corresponding stability in estimation we propose the use of the discrete cosine transform dct as the object independent basis and empirically demonstrate that it approaches principal component analysis pca for natural motions we report the performance of the proposed method quantitatively using motion capture data and qualitatively on several video sequences exhibiting nonrigid motions including piecewise rigid motion partially nonrigid motion such as a facial expressions and highly nonrigid motion such as a person walking or dancing

adaptive backstepping control of a class of uncertain nonlinear systems with unknown backlash like hysteresis in this note we consider the same class of systems as in a previous paper i e a class of uncertain dynamic nonlinear systems preceded by unknown backlash like hysteresis nonlinearities where the hysteresis is modeled by a differential equation in the presence of bounded external disturbances by using backstepping technique robust adaptive backstepping control algorithms are developed unlike some existing control schemes for systems with hysteresis the developed backstepping controllers do not require the uncertain parameters within known intervals also no knowledge is assumed on the bound of the disturbance like term a combination of the external disturbances and a term separated from the hysteresis model it is shown that the proposed controllers not only can guarantee global stability but also transient performance

adaptive neural control for a class of nonlinear systems with uncertain hysteresis inputs and time varying state delays in this paper adaptive variable structure neural control is investigated for a class of nonlinear systems under the effects of time varying state delays and uncertain hysteresis inputs the unknown time varying delay uncertainties are compensated for using appropriate lyapunov krasovskii functionals in the design and the effect of the uncertain hysteresis with the prandtl ishlinskii pi model representation is also mitigated using the proposed control by utilizing the integral type lyapunov function the closed loop control system is proved to be semi globally uniformly ultimately bounded sguub extensive simulation results demonstrate the effectiveness of the proposed approach