A Nonlinear Adaptive Resilient Observer Design for a Class of Lipschitz Systems Using LMI

This paper addresses the parameter and state estimation problem in the presence of the observer gain perturbations for Lipschitz systems that are linear in the unknown parameters and nonlinear in the states. A nonlinear adaptive resilient observer is designed, and its stability conditions based on the Lyapunov technique are derived. The gain for this observer is derived systematically using the linear matrix inequality approach. A numerical example and a physical setup are provided to show the effectiveness of the proposed method.     

An Iterative LMI-Based Reduced-Order Observer Design for Fractional-Order Chaos Synchronization

In this paper, attempts are made to design a reduced-order observer for a nonlinear Lipschitz class of fractional-order systems. It is assumed that nonlinear terms not only depend on measurable states but depend on unknown states and inputs as well. The sufficient conditions for stability of the observer based on the Lyapunov technique are derived and converted into linear matrix inequalities (LMIs). To overcome the main drawback of previous research studies which assumed that the sum of terms in infinite series coming from fractional derivative of a Lyapunov function is bounded and its upper bound is predefined, we used an iterative LMI-based algorithm to find out this bound. A four-wing chaotic system is implemented in both PSpice and MATLAB software as a case study. Simulation results are reported to show the effectiveness of the proposed iterative LMI-based reduced-order observer in tracking the unmeasurable state variables of the chaotic fractional system in different initial conditions.     

Nonfragile Observer for Discrete-Time Switched Nonlinear Systems with Time Delay

This paper investigates the problem of the nonfragile observer design for discrete-time switched nonlinear systems with time delay. Based on the average dwell-time approach and linear matrix inequality (LMI) techniques, an exponential stability criterion for the discrete-time switched delay system with Lipschitz nonlinearity is derived. Based on several technical lemmas, the discrete-time observer design can be transferred to the problem of solving a set of LMIs. Furthermore, in cases when the gain of the state observer varies, a kind of nonfragile observer is proposed, and the solution to the observer gain is also obtained by solving a set of LMIs. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.     

Observer-Based H ∞ Synchronization and Unknown Input Recovery for a Class of Digital Nonlinear Systems

This paper considers the synchronization and unknown input recovery problem for a class of digital nonlinear systems based on a nonlinear observer approach. A generalized Luenberger-like observer is introduced for a class of discrete-time Lipschitz nonlinear systems. Stability conditions for the existence of asymptotic observers are established in terms of some linear matrix inequalities. It is shown that the proposed conditions are less conservative than some existing ones in the recent literature. Moreover, an observer design method is used to address the problem of H _∞ synchronization and unknown input recovery for a class of Lipschitz nonlinear systems in the presence of disturbances in both the state and output equations. Finally, a numerical example is provided to illustrate the effectiveness of the proposed design.     

Statistical analysis of neural network modeling and identificationof nonlinear systems with memory

The paper presents a statistical analysis of neural network modeling and identification of nonlinear systems with memory. The nonlinear system model is comprised of a discrete-time linear filter H followed by a zero-memory nonlinear function g(.). The system is corrupted by input and output independent Gaussian noise. The neural network is used to identify and model the unknown linear filter H and the unknown nonlinearity g(.). The network architecture is composed of a linear adaptive filter and a two-layer nonlinear neural network (with an arbitrary number of neurons). The network is trained using the backpropagation algorithm. The paper studies the MSE surface and the stationary points of the adaptive system. Recursions are derived for the mean transient behavior of the adaptive filter coefficients and neural network weights for slow learning. It is shown that the Wiener solution for the adaptive filter is a scaled version of the unknown filter H. Computer simulations show good agreement between theory and Monte Carlo estimations     

Identification of nonlinear systems using the Wiener model

An algorithm for the identification of nonlinear systems which can be described by a Wiener model consisting of a linear system followed by a single-valued nonlinearity is presented. Crossconolation techniques are employed to decouple the identification of the linear dynamics from the characterisation of the nonlinear element.     

On Nonparametric Identification of Wiener Systems

In this paper, a new method for the identification of the Wiener nonlinear system is proposed. The system, being a cascade connection of a linear dynamic subsystem and a nonlinear memoryless element, is identified by a two-step semiparametric approach. The impulse response function of the linear part is identified via the nonlinear least-squares approach with the system nonlinearity estimated by a pilot nonparametric kernel regression estimate. The obtained estimate of the linear part is then used to form a nonparametric kernel estimate of the nonlinear element of the Wiener system. The proposed method permits recovery of a wide class of nonlinearities which need not be invertible. As a result, the proposed algorithm is computationally very efficient since it does not require a numerical procedure to calculate the inverse of the estimate. Furthermore, our approach allows non-Gaussian input signals and the presence of additive measurement noise. However, only linear systems with a finite memory are admissible. The conditions for the convergence of the proposed estimates are given. Computer simulations are included to verify the basic theory     

Stochastic analysis of gradient adaptive identification ofnonlinear systems with memory for Gaussian data and noisy input andoutput measurements

This paper investigates the statistical behavior of two gradient search adaptive algorithms for identifying an unknown nonlinear system comprised of a discrete-time linear system H followed by a zero-memory nonlinearity g(·). The input and output of the unknown system are corrupted by additive independent noises. Gaussian models are used for all inputs. Two competing adaptation schemes are analyzed. The first is a sequential adaptation scheme where the LMS algorithm is first used to estimate the linear portion of the unknown system. The LMS algorithm is able to identify the linear portion of the unknown system to within a scale factor. The weights are then frozen at the end of the first adaptation phase. Recursions are derived for the mean and fluctuation behavior of the LMS algorithm, which are in excellent agreement with Monte Carlo simulations. When the nonlinearity is modeled by a scaled error function, the second part of the sequential gradient identification scheme is shown to correctly learn the scale factor and the error function scale factor. Mean recursions for the scale factors show good agreement with Monte Carlo simulations. For slow learning, the stationary points of the gradient algorithm closely agree with the stationary points of the theoretical recursions. The second adaptive scheme simultaneously learns both the linear and nonlinear portions of the unknown channel. The mean recursions for the linear and nonlinear portions show good agreement with Monte Carlo simulations for slow learning. The stationary points of the gradient algorithm also agree with the stationary points of the theoretical recursions     

Adaptive Fuzzy Output-Feedback Control for Switched Nonlinear Systems with Arbitrary Switchings

This paper investigates the adaptive fuzzy output-feedback control problem for single-input and single-output switched uncertain nonlinear systems. The addressed systems in this paper have the characteristics of arbitrary switchings, unknown nonlinear dynamics and immeasurable states. A common state observer is designed independent of switching signals. Fuzzy logic systems are utilized to approximate unknown lumped nonlinear dynamics. Based on the framework of backstepping design technique, an adaptive fuzzy output-feedback control scheme is developed. By using the common Lyapunov function theory, the stability of the closed-loop system is proved. The proposed control scheme does not need the assumptions that the states of the controlled system are available for measurement and that the switching signals satisfy the average dwell time. Moreover, it can guarantee that all the closed-loop signals are bounded, and the system output eventually converges to a small neighborhood of the origin. Finally, simulation studies are provided to further check the effectiveness of the proposed control scheme.     

State and Input Simultaneous Estimation for a Class of Time-Delay Systems With Uncertainties

This brief addresses the problem of estimation of both the states and the unknown inputs of a class of systems that are subject to a time-varying delay in their state variables, to an unknown input, and also to an additive uncertain, nonlinear disturbance. Conditions are derived for the solvability of the design matrices of a reduced-order observer for state and input estimation, and for the stability of its dynamics. To improve computational efficiency, a delay-dependent asymptotic stability condition is then developed using the linear matrix inequality formulation. A design procedure is proposed and illustrated by a numerical example.     

Adaptive Fuzzy Output-Feedback Tracking Control for a Class of Switched Stochastic Nonlinear Time-Delay Systems

In this paper, a design scheme for an adaptive fuzzy tracking controller is proposed for a class of switched stochastic nonlinear time-delay systems via dynamic output-feedback. First, a reduced-order observer is introduced to estimate the unmeasurable states of the switched system. During the adaptive controller design procedure, an appropriate stochastic Lyapunov–Krasovskii functional deals with the time-delay terms, and fuzzy logic systems are employed to approximate the unknown nonlinearities. Based on the designed controller, the semi-globally uniform ultimate boundedness of all the closed-loop signals is guaranteed and the tracking error converges to a small neighborhood of the origin. Finally, a simulation example is given to illustrate the validity of the proposed approach.     

Robust Kalman Filter Design for Markovian Jump Linear Systems with Norm-Bounded Unknown Nonlinearities

This paper considers the problems of stability and filtering for a class of linear hybrid systems with nonlinear uncertainties and Markovian jump parameters. The hybrid system under study involves a continuous-valued system state vector and a discretevalued system mode. The unknown nonlinearities in the system are time varying and norm bounded. The Markovian jump parameters are modeled by a Markov process with a finite number of states. First, we show the equivalence of the sets of norm-bounded linear and nonlinear uncertainties. Then, instead of the original hybrid linear system with nonlinear uncertainties, we consider the same system with linear uncertainties. By using a Riccati equation approach for this new system, a robust filter is designed using two sets of coupled Riccati-like equations such that the estimation error is guaranteed to have an upper bound.     

Linear estimator for fractional systems

We address the issue of state estimation of nonlinear incommensurate fractional-order systems via linear observer in this paper. The basic idea is proposed under a synchronization framework which makes the response system a linear observer for the state of the drive system. By developing this approach, a linear time-invariant synchronization error system is obtained, and stability analysis is relied on the theory of linear incommensurate fractional-order systems. The suggested tool proves to be effective and systematic in achieving global synchronization. Simulation results verify and illustrate the effectiveness of the proposed method on some new fractional-order hyperchaotic systems.     

Dynamic system identification with order statistics

Systems consisting of linear dynamic and memory-less nonlinear subsystems are identified. The paper deals with systems in which the nonlinear element is followed by a linear element, as well as systems in which the subsystems are connected in parallel. The goal of the identification is to recover the nonlinearity from noisy input-output observations of the whole system; signals interconnecting the elements are not measured. Observed values of the input signal are rearranged in increasing order, and coefficients for the expansion of the nonlinearity in trigonometric series are estimated from the new sequence of observations obtained in this way. Two algorithms are presented, and their mean integrated square error is examined. Conditions for pointwise convergence are also established. For the nonlinearity satisfying the Lipschitz condition, the error converges to zero. The rate of convergence derived for differentiable nonlinear characteristics is insensitive to the roughness of the probability density of the input signal. Results of numerical simulation are also presented     

Identification and control for heart rate regulation during treadmill exercise

This paper proposes a novel integrated approach for the identification and control of Hammerstein systems to achieve desired heart rate profile tracking performance for an automated treadmill system. For the identification of Hammerstein systems, the pseudorandom binary sequence input is employed to decouple the identification of dynamic linear part from input nonlinearity. The powerful epsilon-insensitivity support vector regression method is adopted to obtain sparse representations of the inverse of static nonlinearity in order to obtain an approximate linear model of the Hammerstein system. An Hinfinity controller is designed for the approximated linear model to achieve robust tracking performance. This new approach is successfully applied to the design of a computer-controlled treadmill system for the regulation of heart rate during treadmill exercise. Minimizing deviations of heart rate from a preset profile is achieved by controlling the speed of the treadmill. Both conventional proportional-integral-derivative (PID) control and the proposed approaches have been employed for the controller design. The proposed algorithm achieves much better heart rate tracking performance.     

Three-level m sequences

Those properties of 3-level m sequences which are likely to prove useful in the crosscorrelation method of system dynamic analysis are listed. The use of 3-level m sequence signals in the identification of nonlinear systems is discussed, and it is shown that the number of crosscorrelation experiments required to determine the impulse response of the linear channel of a system with an amplitude nonlinearity is reduced by the use of these signals.     

Nonlinear macromodeling with AWE

Asymptotic Waveform Evaluation (AWE) [1, 2] is an efficient and general technique for simulating linear(ized) circuits. This paper discusses strategies for macromodeling nonlinear circuits with AWE. One approach, multi-region AWE macromodels, represents an extension of piecewise linear models, with the addition of internal states. Each region represents an AWE approximation to a linearization (at some bias point) of the nonlinear circuit of interest. In addition a technique is presented for initializing the internal states when the model transitions from one linearization to another during a transient simulation. The second approach is to treat nonlinearity as a second order effect that is superimposed on a linear solution as a post-processing step. A relaxation algorithm that exploits the reuseable AWE solution is employed to modify the linear solution so that it accounts for the macromodel nonlinearity.This work was supported by the Semiconductor Research Corporation.     

A stable adaptive Hammerstein filter employing partial orthogonalization of the input signals

This paper presents an algorithm that adapts the parameters of a Hammerstein system model. Hammerstein systems are nonlinear systems that contain a static nonlinearity cascaded with a linear system. In this paper, the static nonlinearity is modeled using a polynomial system, and the linear filter that follows the nonlinearity is an infinite-impulse response (IIR) system. The adaptation of the nonlinear components is improved by orthogonalizing the inputs to the coefficients of the polynomial system. The step sizes associated with the recursive components are constrained in such a way as to guarantee bounded-input bounded-output (BIBO) stability of the overall system. This paper also presents experimental results that show that the algorithm performs well in a variety of operating environments, exhibiting stability and global convergence of the algorithm.     

Identification of certain time-varying nonlinear Wiener andHammerstein systems

The problem of identification and tracking of time-varying nonlinear systems is addressed. In particular, the Wiener system that consists of a dynamic time-varying linear part followed by a fixed nonlinearity and the Hammerstein system in which the order of these two blocks is reversed are studied. The extended Kalman filter (EKF) algorithm is applied. It is also shown that this algorithm can be reformulated in terms of a nonlinear minimization problem with a quadratic inequality constraint in order to ensure exponential stability, resulting in the algorithm CEKF. As indicated by means of numerical examples, this latter algorithm is less sensitive to the chosen initialization than the EKF. The proposed algorithms depend on certain second-order statistics that may be unknown in a typical scenario. A method for estimation of these quantities is proposed. It is demonstrated that the suggested algorithms can be successfully applied to the problem of acoustic echo cancelation