technical communique adaptive ilc for a class of discrete time systems with iteration varying trajectory and random initial condition in this work we present a discrete time adaptive iterative learning control ailc scheme to deal with systems with time varying parametric uncertainties using the analogy between the discrete time axis and the iterative learning axis the new adaptive ilc can incorporate a recursive least squares rls algorithm hence the learning gain can be tuned iteratively along the learning axis and pointwisely along the time axis when the initial states are random and the reference trajectory is iteration varying the new ailc can achieve the pointwise convergence over a finite time interval asymptotically along the iterative learning axis

hybridization and regularization schemes applied to identify rough acoustic impedance profiles in slender structures with noisy data the modified sequential algebraic algorithm msaa is a routine developed to solve the longitudinal acoustic wave propagation problem in inhomogeneous media this method accurately predicts the echo generated by the inhomogeneities particularly when the acoustic impedance variation profile in the damaged part of the structure is non smooth used for solving the inverse problem by a stochastic optimization approach msaa efficiently identifies rough profiles of impedance with low noise level in the input data as noise level increases the inverse problem turns out to be ill posed and often yields inappropriate solutions the purpose of this paper is to apply a regularization scheme and to use a hybrid optimization strategy to reduce the error in the damage estimation in the numerical examples 40 parameters are successfully identified for a signal to noise ratio of 20 db for all tested damage profiles the regularization and hybridization procedures decreased the maximum absolute error to levels lowe

robustifying nonlinear systems using high order neural network controllers a robust control methodology for affine control of nonlinear dynamical systems is developed in this paper a correction control signal is added to a nominal controller designed to guarantee a desired performance for the corresponding nominal system to render the actual system uniformly and ultimately bounded the control signal is smooth and does not require a priori knowledge of an upper bound on the modeling error and or optimal weight values simulations performed on a simple nonlinear system illustrate the approach

generation of anisotropic smoothness regularization filters for eit in the inverse conductivity problem as in any ill posed inverse problem regularization techniques are necessary in order to stabilize inversion a common way to implement regularization in electrical impedance tomography is to use tikhonov regularization the inverse problem is formulated as a minimization of two terms the mismatch of the measurements against the model and the regularization functional most commonly differential operators are used as regularization functionals leading to smooth solutions whenever the imaged region presents discontinuities in the conductivity distribution such as interorgan boundaries the smoothness prior is not consistent with the actual situation in these cases the reconstruction is enhanced by relaxing the smoothness constraints in the direction normal to the discontinuity in this paper we derive a method for generating gaussian anisotropic regularization filters the filters are generated on the basis of the prior structural information allowing a better reconstruction of conductivity profiles matching these priors when incorporating prior information into a reconstruction algorithm the risk is of biasing the inverse solutions toward the assumed distributions simulations show that with a careful selection of the regularization parameters the reconstruction algorithm is still able to detect conductivities patterns that violate the prior information a generalized singular value decomposition analysis of the effects of the anisotropic filters on regularization is presented in the last sections of the paper

direct feedback control design for nonlinear systems we propose an approach for the direct design from data of controllers finalized at solving tracking problems for nonlinear systems this approach called direct feedback dfk design overcomes relevant problems typical of the standard design methods such as modeling errors non trivial parameter identification non convex optimization and difficulty in nonlinear control design considering a set membership sm approach we provide three main contributions the first one is a theoretical framework for the stability analysis of nonlinear feedback control systems in which the controller email protected is an approximation identified from data of an ideal inverse model f o in this framework we derive sufficient conditions under which email protected stabilizes the closed loop system the second contribution is a technique for the direct design of an approximate controller f from data having suitable optimality stability and sparsity properties in particular we show that f is an almost optimal controller in a worst case sense and we derive a guaranteed accuracy bound which can be used to quantify the performance level of the dfk control system we also show that when the number of data used for control design tends to infinity and these data are dense in the controller domain the closed loop stability is guaranteed for a set of trajectories of interest the technique is based on convex optimization and sparse identification methods and thus avoids the problem of local minima and allows an efficient online controller implementation in real world applications the third contribution is a simulation study regarding the application of dfk to the challenging problem of control design for a class of airborne wind energy generators

discrete time model reference adaptive control of nonlinear dynamical systems using neural networks a multilayer discrete time neural net nn controller is presented for the direct model reference adaptive control of a class of multi input multi output mim0 nonlinear dynamical systems the nonlinear dynamical system is assumed to be controllable and its state vector is available for measurement the nn controller exhibits learning while functioning features instead of learning then control so that control is immediate with no explicit learning phase needed the tracking error between the output of a nonlinear plant and an ideal linear model converges within a very short time persistence of excitation pe is not needed linearity in the parameters is not required and certainty equivalence is not used this overcomes several limitations of standard adaptive control the novel weight tuning paradigm for the nn controller is based on the well known delta rule but includes a modification to the learning rate parameter plus a correction term it guarantees tracking as well as bounded nn weights in non i

adaptive learning control for finite interval tracking based on constructive function approximation and wavelet using a constructive function approximation network an adaptive learning control alc approach is proposed for finite interval tracking problems the constructive function approximation network consists of a set of bases and the number of bases can evolve when learning repeats the nature of the basis allows the continuous adaptive learning of parameters when the network undergoes any structural changes and consequently offers the flexibility in tuning the network structure the expandability of the bases guarantees precision of the function approximation and avoids the trial and error procedure in structure selection for any fixed structure network two classes of unknown nonlinear functions namely either global l2 or local l2 with a known bounding function are taken into consideration using the lyapunov method the existence of solution and the convergence property of the proposed alc system are discussed in a rigorous manner by virtue of the celebrated orthonormal and multiresolution properties wavelet network is used as the universal function approximator with the weights tuned by the proposed adaptive learning mechanism

adaptive control of nonlinear systems using neural networks layered networks are used in a nonlinear adaptive control problem the plant is an unknown feedback linearizable discrete time system represented by an input output model a state space model of the plant is obtained to define the zero dynamics which are assumed to be stable a linearizing feedback control is derived in terms of some unknown nonlinear functions to identify these functions it is assumed that they can be modelled by layered neural networks the weights of the networks are updated and used to generate the control a local convergence result is given computer simulations verify the theoretical result

adaptive nn control for a class of strict feedback discrete time nonlinear systems in this paper both full state and output feedback adaptive neural network nn controllers are presented for a class of strict feedback discrete time nonlinear systems firstly lyapunov based full state adaptive nn control is presented via backstepping which avoids the possible controller singularity problem in adaptive nonlinear control and solves the noncausal problem in the discrete time backstepping design procedure after the strict feedback form is transformed into a cascade form another relatively simple lyapunov based direct output feedback control is developed the closed loop systems for both control schemes are proven to be semi globally uniformly ultimately bounded

neural network adaptive control for nonlinear nonnegative dynamical systems nonnegative and compartmental dynamical system models are derived from mass and energy balance considerations that involve dynamic states whose values are nonnegative these models are widespread in engineering and life sciences and typically involve the exchange of nonnegative quantities between subsystems or compartments wherein each compartment is assumed to be kinetically homogeneous in this paper we develop a full state feedback neural adaptive control framework for adaptive set point regulation of nonlinear uncertain nonnegative and compartmental systems the proposed framework is lyapunov based and guarantees ultimate boundedness of the error signals corresponding to the physical system states and the neural network weighting gains in addition the neural adaptive controller guarantees that the physical system states remain in the nonnegative orthant of the state space for nonnegative initial conditions

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

a fast algorithm to solve the inverse scattering problem in layered media with arbitrary input a new sequential algorithm to solve the one dimensional inverse scattering problem in layered media with arbitrary inlet pulse is presented the procedure consists of the mathematical inversion of the exact algebraic solution for the direct problem derived in tenenbaum and zindeluk j acoust soc am xx xxxx xxxx 1992 and features computational efficiency together with improved numerical stability simulated examples of successful impedance profile identification with noisy data are reported and a detailed analysis of the stability improvement is provided impact tests on variable cross section long bars monitored with strain gauges are also reported and the method is applied to actual parameter profile identification with success

integrated design of a linear neuro adaptive controller in the presence of norm bounded uncertainties adaptive neural controllers are often criticised for the lack of clear and easy design methodologies that relate adaptive neural network nn design parameters to performance requirements this study proposes a methodology for the design of an integrated linear adaptive model reference controller that guarantees component wise boundedness of the tracking error within an a priori specified compact domain the approach is based on the design of a robust invariant ellipsoidal set where both the nn reconstruction error and the neuro adaptive control are considered as bounded persistent uncertainties we show that all the performance and control requirements for the closed loop system can be expressed as linear matrix inequality constraints this brings the advantage that feasibility and optimal design parameters can be effectively computed while solving a linear optimisation problem an advantage of the method is that it allows a systematic and quantitative evaluation of the interplay between the design param

in vivo impedance imaging with total variation regularization we show that electrical impedance tomography eit image reconstruction algorithms with regularization based on the total variation tv functional are suitable for in vivo imaging of physiological data this reconstruction approach helps to preserve discontinuities in reconstructed profiles such as step changes in electrical properties at interorgan boundaries which are typically smoothed by traditional reconstruction algorithms the use of the tv functional for regularization leads to the minimization of a nondifferentiable objective function in the inverse formulation this cannot be efficiently solved with traditional optimization techniques such as the newton method we explore two implementations methods for regularization with the tv functional the lagged diffusivity method and the primal dual interior point method pd ipm first we clarify the implementation details of these algorithms for eit reconstruction next we analyze the performance of these algorithms on noisy simulated data finally we show reconstructed eit images of in vivo data for ventilation and gastric emptying studies in comparison to traditional quadratic regularization tv regulariza tion shows improved ability to reconstruct sharp contrasts

experimental studies in nonlinear discrete time adaptive prediction and control this paper presents implementation results using recently introduced discrete time adaptive prediction and control techniques using online function approximators we consider a process control experiment as our test bed and develop a discrete time adaptive predictor for liquid volume and a discrete time adaptive controller for reference volume tracking we use takagi sugeno ts fuzzy systems as our function approximators and for both prediction and control we investigate the use of a least squares update of the fuzzy system s parameters

stable auto tuning of adaptive fuzzy neural controllers for nonlinear discrete time systems in direct adaptive control the adaptation mechanism attempts to adjust a parameterized nonlinear controller to approximate an ideal controller in the indirect case however we approximate parts of the plant dynamics that are used by a feedback controller to cancel the system nonlinearities in both cases approximators such as linear mappings polynomials fuzzy systems or neural networks can be used as either the parameterized nonlinear controller or identifier model in this paper we present algorithms to tune some of the parameters e g the adaptation gain and the direction of descent for a gradient based approximator parameter update law used for a class of nonlinear discrete time systems in both direct and indirect cases in our proposed algorithms the adaptation gain and the direction of descent are obtained by minimizing the instantaneous control energy we will show that updating the adaptation gain can be viewed as a special case of updating the direction of descent we will also compare the direct and indirect adaptive control schemes and illustrate their performance via a simple surge tank example

an inverse problems approach to mr ept image reconstruction magnetic resonance electrical properties tomography mr ept is an imaging modality that maps the spatial distribution of the electrical conductivity and permittivity using standard mri systems the presence of a body within the scanner alters the rf field and by mapping these alterations it is possible to recover the electrical properties the field is time harmonic and can be described by the helmholtz equation approximations to this equation have been previously used to estimate conductivity and permittivity in terms of first or second derivatives of rf field data using these same approximations an inverse approach to solving the mr ept problem is presented here that leverages a forward model for describing the magnitude and phase of the field within the imaging domain and a fitting approach for estimating the electrical properties distribution the advantages of this approach are that 1 differentiation of the measured data is not required thus reducing noise sensitivity and 2 different regularization schemes can be adopted depending on prior knowledge of the distribution of conductivity or permittivity leading to improved image quality to demonstrate the developed approach both quadratic qr and total variation tv regularization methods were implemented and evaluated through numerical simulation and experimentally acquired data the proposed inverse approach to mr ept reconstruction correctly identifies contrasts and accurately reconstructs the geometry in both simulations and experiments the tv regularized scheme reconstructs sharp spatial transitions which are difficult to reconstruct with other more traditional approaches

asymptotic tracking of uncertain systems with continuous control using adaptive bounding this paper presents a robust adaptive control design method for a class of multiple input multiple output uncertain nonlinear systems in the presence of parametric and nonparametric uncertainties and bounded disturbances using the approximation properties of the unknown continuous nonlinearities and the adaptive bounding technique the developed controller achieves asymptotic convergence of the tracking error to zero while ensuring boundedness of parameter estimation errors the algorithm does not assume the knowledge of any bound on the unknown quantities in designing the controller it is based on an integral technique involving the filtered tracking error and produces a continuous control theoretical developments are illustrated via simulation results

stable adaptive control using fuzzy systems and neural networks stable direct and indirect adaptive controllers are presented which use takagi sugeno fuzzy systems conventional fuzzy systems or a class of neural networks to provide asymptotic tracking of a reference signal for a class of continuous time nonlinear plants with poorly understood dynamics the indirect adaptive scheme allows for the inclusion of a priori knowledge about the plant dynamics in terms of exact mathematical equations or linguistics while the direct adaptive scheme allows for the incorporation of such a priori knowledge in specifying the controller we prove that with or without such knowledge both adaptive schemes can learn how to control the plant provide for bounded internal signals and achieve asymptotically stable tracking of a reference input in addition for the direct adaptive scheme a technique is presented in which linguistic knowledge of the inverse dynamics of the plant may be used to accelerate adaptation the performance of the indirect and direct adaptive schemes is demonstrated through the longitudinal control of an automobile within an automated lane

neural network enhanced output regulation in nonlinear systems a neural network based approach is developed to approximate the feedforward function needed for solving the nonlinear output regulation problem the approach eliminates the need for solving the regulator equations thus applying to complex uncertain nonlinear systems

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

a modified sequential algebraic algorithm applied to identify rough acoustic impedance profiles in slender structures this paper develops and examines a modified technique for identifying damage in slender structures an algorithm called modified sequential algebraic algorithm is presented to solve the longitudinal acoustic wave propagation problem in inhomogeneous media this method accurately predicts the echo generated by the inhomogeneities when the acoustic impedance variation profile in the damaged part of the structure is non smooth for these cases the available algorithms fail to provide the actual regressive wave component in the following a stochastic optimization scheme the differential evolution method is applied to solve the inverse problem numerical examples with smooth and non smooth impedance profiles are considered it is shown that for smooth profiles both the available techniques and the modified algorithm succeed in identifying the actual damage nevertheless in situations where a rough profile is to be reconstructed only the modified algorithm succeeds the technique is found to be robust with respect to additive noise in the signals

on the sensitivity of elastic waves due to structural damages time frequency based indexing method time frequency analysis of various simulated and experimental signals due to elastic wave scattering from damage are performed using wavelet transform wt and hilbert huang transform hht and their performances are compared in context of quantifying the damages spectral finite element method is employed for numerical simulation of wave scattering an analytical study is carried out to study the effects of higher order damage parameters on the reflected wave from a damage based on this study error bounds are computed for the signals in the spectral and also on the time frequency domains it is shown how such an error bound can provide an estimate of error in the modelling of wave propagation in structure with damage measures of damage based on wt and hht is derived to quantify the damage information hidden in the signal the aim of this study is to obtain detailed insights into the problem of 1 identifying localised damages 2 dispersion of multifrequency non stationary signals after they interact with various types of damage and 3 quantifying the damages sensitivity analysis of the signal due to scattered wave based on time frequency representation helps to correlate the variation of damage index measures with respect to the damage parameters like damage size and material degradation factors

feedback linearization control design for systems with fuzzy uncertainty we derive a state feedback control design for nonlinear systems with fuzzy uncertainty we use a fuzzy version of the kolmogorov forward equation and an equivalent deterministic system to represent the effects of the fuzzy uncertainty feedback linearization of the equivalent deterministic system cancels both the system nonlinearities and the effects of the fuzzy uncertainty on the state membership function our controller obtains asymptotic stability and an approximation to our controller drives the system state to a small bounded set

input output stability theory for direct neuro fuzzy controllers in an input output i o setting we undertake a detailed theoretical investigation of the stability of a given direct static multiple input single output neuro fuzzy controller operating under feedback control dependent only on the functional gain of the plant to be controlled it is shown that various stability regions in weight space are convex and necessary and sufficient conditions are given for these stability regions to be open and bounded the convexity results coupled with the stability test give a practical method for constructing the stability regions we show that an adaptive neuro fuzzy controller is stable under feedback if we constrain the weights of the controller to lie within any compact set within the stability region combining a projection operator with any standard training law can thus give a stable adaptive controller

an exact solution for the one dimensional elastic wave equation in layered media a new approach to the direct scattering problem in one dimensional inhomogeneous bodies is presented a general formulation considering density wave speed and cross section variation is introduced and a compact form for the wave equation in the characteristic plane with a generalized acoustical impedance profile as a parameter is established with the aid of a nonsingular variable transformation the wave propagation pattern in layered or discretized continuous media is then examined and an exact algebraic solution for the reflected wave is obtained the resulting formula is adequate to an arbitrary input pulse furnishing a quick computation of all multiple reflection contributions to the final echo

deadzone compensation in motion control systems using neural networks a compensation scheme is presented for general nonlinear actuator deadzones of unknown width the compensator uses two neural networks nns one to estimate the unknown deadzone and another to provide adaptive compensation in the feedforward path the compensator nn has a special augmented form containing extra neurons whose activation functions provide a jump function basis set for approximating piecewise continuous functions rigorous proofs of closed loop stability for the deadzone compensator are provided and yield tuning algorithms for the weights of the two nns the technique provides a general procedure for using nns to determine the preinverse of an unknown right invertible function

technical communique robust adaptive regulation of discrete time nonlinear systems with arbitrary nonlinearities this note develops a new recursive estimator with a dead zone by using the estimator a robust adaptive regulator for a class of simple discrete time nonlinear systems with bounded disturbance is presented a global stability result of the closed loop system is obtained through lyapunov analysis without assuming any growth conditions on the nonlinearities the regulation performance is also presented through a simulation example

multiple model self tuning control for a class of nonlinear systems this study develops a novel nonlinear multiple model self tuning control method for a class of nonlinear discrete time systems an increment system model and a modified robust adaptive law are proposed to expand the application range thus eliminating the assumption that either the nonlinear term of the nonlinear system or its differential term is global bounded the nonlinear self tuning control method can address the situation wherein the nonlinear system is not subject to a globally uniformly asymptotically stable zero dynamics by incorporating the pole placement scheme a novel nonlinear control structure based on this scheme is presented to improve control precision stability and convergence can be confirmed when the proposed multiple model self tuning control method is applied furthermore simulation results demonstrate the effectiveness of the proposed method

stochastic algorithms for robustness of control performances in recent years there has been a growing interest in developing statistical learning methods to provide approximate solutions to difficult control problems in particular randomized algorithms have become a very popular tool used for stability and performance analysis as well as for design of control systems however as randomized algorithms provide an efficient solution procedure to the intractable problems stochastic methods bring closer to understanding the properties of the real systems the topic of this paper is the use of stochastic methods in order to solve the problem of control robustness the case of parametric stochastic uncertainty is considered necessary concepts regarding stochastic control theory and stochastic differential equations are introduced then a convergence analysis is provided by means of the chernoff bounds which guarantees robustness in mean and in probability as an illustration the robustness of control performances of example control systems is computed

neural network based control design an lmi approach we address a neural network based control design for a discrete time nonlinear system our design approach is to approximate the nonlinear system with a multilayer perceptron of which the activation functions are of the sigmoid type symmetric to the origin a linear difference inclusion representation is then established for this class of approximating neural networks and is used to design a state feedback control law for the nonlinear system based on the certainty equivalence principle the control design equations are shown to be a set of linear matrix inequalities where a convex optimization algorithm can be applied to determine the control signal further the stability of the closed loop is guaranteed in the sense that there exists a unique global attraction region in the neighborhood of the origin to which every trajectory of the closed loop system converges finally a simple example is presented so as to illustrate our control design procedure

adaptive mnn control for a class of non affine narmax systems with disturbances in this paper adaptive multi layer neural network mnn control is developed for a class of discrete time non affine nonlinear systems in nonlinear auto regressive moving average with exogenous inputs narmax model by using implicit function theorem the existence of the implicit desired feedback control idfc is proved mnns are used as the emulator of the desired feedback control projection algorithms are used to guarantee the boundedness of the neural network nn weights which removes the need of persistent exciting pe condition for parameter convergence simulation results show the effectiveness of the proposed control scheme