http://www.ifac-papersonline.net/ en Copyright 08:48 AM Sunday 19, 2013 IFAC-PapersOnline http://www.ifacpapersonline.com 08:48 AM Sunday 19, 2013 08:48 AM Sunday 19, 2013 ET webmaster@ifac-papersonline.net webmaster@ifac-papersonline.net http://www.ifac-papersonline.net/Detailed/27286.html 04:00 PM Wednesday 31, 1969 http://www.ifac-papersonline.net/static/luna/images/ifac/icon-download.gif http://www.ifac-papersonline.net/Detailed/27287.html 04:00 PM Wednesday 31, 1969 This report discusses problems and methodologies that lie in the broad scope of systems and signals, with special focus on modeling, identification and signal processing; adaptation and learning; discrete event and hybrid systems; and stochastic systems. A common theme underlying all these areas is that problems in control systems and signals are usually defined and best studied in the framework of stochastic approaches. Although there are common precepts among all these technologies, there are also many unique topics within each area. Therefore, the current key problems in each technology are explained, followed by a discussion of recent major accomplishments with trends, and finally some forecasts of likely developments are provided. The conclusion summarizes some general forecasts for the overall field of systems and signals. http://www.ifac-papersonline.net/static/luna/images/ifac/icon-download.gif http://www.ifac-papersonline.net/Detailed/27288.html 04:00 PM Wednesday 31, 1969 Linear Parameter Varying (LPV) systems appear in a form of LTI state space representations where the elements of the A(ρ), B(ρ), C(ρ) matrices depend on an unknown but at any time instant measurable vector parameter ρ ∈ P. This paper describes a geometric view of LPV systems. Geometric concepts and tools of invariant subspaces and algorithms for LPV systems affine in the parameters will be presented and proposed. Application of these results will be shown and referenced in solving various analysis (controllabilty/observability) problems, controller design and fault detection problems associated to LPV systems. http://www.ifac-papersonline.net/static/luna/images/ifac/icon-download.gif http://www.ifac-papersonline.net/Detailed/27289.html 04:00 PM Wednesday 31, 1969 This paper addresses the identification of non-linear dynamic systems. A wide class of these systems can be described using non-linear time-invariant regression models, that can be approximated by means of piecewise affine prototypes with an arbitrary degree of accuracy. This work concerns the identification of piecewise affine model structure through input-output data acquired from a dynamic process. In order to show the effectiveness of the developed technique, the results obtained in the identification of both a simple simulated system and a real dynamic process are reported. http://www.ifac-papersonline.net/static/luna/images/ifac/icon-download.gif http://www.ifac-papersonline.net/Detailed/27290.html 04:00 PM Wednesday 31, 1969 In this paper, the estimation of prediction intervals for multi-step-ahead predictions from dynamic neural network models is described. Usually, asymptotic methods based on linearizations are applied with the potential problem of large coverage errors and too optimistic prediction intervals. The potential sources of these problems are the negligence of the network parameter uncertainties and the non-normality of the error distribution. To overcome these restrictions, bootstrap methods are used here. New formulations are introduced to apply the bootstrap to nonlinear time series models with exogenous input. An explicit model of the error process considers the influence of different training data densities on the empirical error distribution. A Monte Carlo study illustrates the proposed methods. http://www.ifac-papersonline.net/static/luna/images/ifac/icon-download.gif http://www.ifac-papersonline.net/Detailed/27291.html 04:00 PM Wednesday 31, 1969 Second-order nonlinear ordinary differential equations (ODE's) can be used for modeling periodic signals. The right hand side function of the ODE model is parameterized in terms of polynomial basis functions. The least squares (LS) algorithm for estimating the coefficients of the polynomial basis gives biased estimates at low signal to noise ratios (SNRs). This is due to approximating the states of the ODE model using finite difference approximations from the noisy measurements. An analysis for this bias is given in this paper. http://www.ifac-papersonline.net/static/luna/images/ifac/icon-download.gif http://www.ifac-papersonline.net/Detailed/27292.html 04:00 PM Wednesday 31, 1969 State-dependent parameter representations of nonlinear stochastic sampled-data systems are studied. Velocity-based linearization is used characterize sampled-data systems using nominally linear models whose parameters can be represented as functions of past outputs and inputs. For stochastic systems the approach leads to state-dependent ARMAX (quasi-ARMAX) representations. The models and their parameters are identified from input-output data using feedforward neural networks to represent the model parameters as functions of past inputs and outputs. http://www.ifac-papersonline.net/static/luna/images/ifac/icon-download.gif http://www.ifac-papersonline.net/Detailed/27293.html 04:00 PM Wednesday 31, 1969 Identification of nonlinear dynamic systems from experimental data can be difficult when, as often happens, more data are available around equilibrium points and only sparse data are available far from those points. The probabilistic Gaussian Process model has already proved to model such systems efficiently. The purpose of this paper is to show how one can relatively easily combine measured data and linear local models in this model. Also, using previous results, we show how uncertainty can be propagated through such models when predicting ahead in time in an iterative manner. The approach is illustrated with a simple numerical example. http://www.ifac-papersonline.net/static/luna/images/ifac/icon-download.gif http://www.ifac-papersonline.net/Detailed/27294.html 04:00 PM Wednesday 31, 1969 The objective of this paper is to find the structure of a nonlinear system from measurement data, as a prior step to model estimation. Applying ANOVA directly on a dataset is compared to applying ANOVA on residuals from a linear model. The distributions of the involved test variables are computed and used to show that ANOVA is effective in finding which regressors give linear effects and what regressors produce nonlinear effects. The ability to find nonlinear substructures depending on only subsets of regressors is an ANOVA feature which is shown not to be affected by subtracting a linear model. http://www.ifac-papersonline.net/static/luna/images/ifac/icon-download.gif http://www.ifac-papersonline.net/Detailed/27295.html 04:00 PM Wednesday 31, 1969 Nonlinear dynamic systems of Hammerstein type are identified from input and output measurements. Identification algorithms for a memoryless nonlinear part and for a linear dynamic part are proposed. Convergence and rates of convergence of the algorithms are investigated. The class of nonlinearities considered in the paper is very large and cannot be parameterized therefore nonparametric approach is used. The performance of identification algorithms is studied in simulation experiments. http://www.ifac-papersonline.net/static/luna/images/ifac/icon-download.gif http://www.ifac-papersonline.net/Detailed/27296.html 04:00 PM Wednesday 31, 1969 The technique of dynamic data reconciliation has been previously studied in the literature and shown to be an effective tool to better estimate the true values of process variables by using information from both measured values and process models. Real-time implementation of dynamic data reconciliation involves solving complex optimization problem, leading to large computation time. This paper presents a study on the use of a dynamic Autoassociative Neural Network (AANN) for dynamic data reconciliation. Once trained, the AANN can be directly used for online signal validation. Closed-loop performance of the AANN for both linear and nonlinear processes was evaluated using simulations of two storage tank processes. The AANN provided accurate estimates of measured values for the two processes studied in this investigation. http://www.ifac-papersonline.net/static/luna/images/ifac/icon-download.gif http://www.ifac-papersonline.net/Detailed/27297.html 04:00 PM Wednesday 31, 1969 This paper considers the identification problem of continuous-time systems with unknown time delays from sampled input-output data. An iterative global separable nonlinear least-squares (GSEPNLS) method which estimates the time delays and transfer function parameters separably is derived, by using stochastic global-optimization technique to avoid convergence to a local minimum. Futhermore, the GSEPNLS method is modified to a novel global separable nonlinear instrumental variable (GSEPNIV) method to yield consistent estimates if the algorithm converges to the global minimum. Simulation results show that the proposed method works quite well. http://www.ifac-papersonline.net/static/luna/images/ifac/icon-download.gif http://www.ifac-papersonline.net/Detailed/27298.html 04:00 PM Wednesday 31, 1969 System identification of a flexible mechanical system using decimated data is studied. It is illustrated how the use of decimated data can give erroneous results due to the inter-sample behavior of the signals, and an intuitive explanation to this phenomenon is proposed. The possible improvement by using alternative assumptions for the intersample behavior is investigated. http://www.ifac-papersonline.net/static/luna/images/ifac/icon-download.gif http://www.ifac-papersonline.net/Detailed/27299.html 04:00 PM Wednesday 31, 1969 The paper proposes a novel approach to identification of continuous-time systems from sampled I/O data. The coefficients of plant transfer functions are directly identied by applying an iterative learning control which enables us to achieve perfect tracking for uncertain plants by iteration of trials. Furthermore, one way to make the method robust against the measurement noises is shown. One of the merits of the proposed method is that it does not require time-derivative of I/O signals. In addition, it indicates us the estimation accuracy explicitly through tracking control experiments. Numerical examples are given to illustrate the effectiveness of the proposed method. http://www.ifac-papersonline.net/static/luna/images/ifac/icon-download.gif http://www.ifac-papersonline.net/Detailed/27300.html 04:00 PM Wednesday 31, 1969 General closed-form expressions of linear continuous-time system responses, are derived. The system eigenvalues can be real and/or complex, and may be repeated. A recursive computationally attractive method is formulated by which the partial fraction expansion coefficients can be computed fast and accurately. The closed-form expressions include the numerator coefficients of the transfer function, a matrix containing the partial fraction expansion coefficients and the system's eigenvalues, and a vector containing the independent time-basis functions. Higher-order responses can easily be computed in closed form from the impulse response. http://www.ifac-papersonline.net/static/luna/images/ifac/icon-download.gif http://www.ifac-papersonline.net/Detailed/27301.html 04:00 PM Wednesday 31, 1969 This paper treats identification of continuous-time output error (OE) models based on sampled data. The exact method for doing this is well known both for data given in the time and frequency domains. This approach becomes somewhat complex, especially for non-uniformly sampled data. We study various ways to approximate the exact method for reasonably fast sampling. While an objective is to gain insights into the non-uniform sampling case, this paper only gives explicit results for uniform sampling. http://www.ifac-papersonline.net/static/luna/images/ifac/icon-download.gif http://www.ifac-papersonline.net/Detailed/27302.html 04:00 PM Wednesday 31, 1969 A novel direct approach for modeling continuous-time stochastic processes is proposed in this paper. First the observed data is passed through an input-to-state filter and the covariance of the output state is computed. The properties of the state covariance matrix is then exploited to estimate the positive real spectrum of the observed data at a set of prescribed points on the right half plane. Finally, the continuous-time parameters are obtained from the positive real spectrum estimates by solving a Nevanlinna-Pick interpolation problem. The estimated model is stable. The analytical results are illustrated using numerical simulations. http://www.ifac-papersonline.net/static/luna/images/ifac/icon-download.gif http://www.ifac-papersonline.net/Detailed/27303.html 04:00 PM Wednesday 31, 1969 Stationary properties of a recently suggested windup prevention scheme for recursive parameter estimation are investigated in the case of insufficient excitation. When the regressor vector contains data covering the whole parameter space, the algorithm has only one stationary point, the one defined by a weighting matrix. If the excitation is insufficient, the algorithm is shown to possess a manifold of stationary points and a parametrization of this manifold is given. However, if the past excitation conditions already caused the algorithm to converge to a certain point, the stationary solution would not be affected by current lack of excitation. This property guarantees good antiwindup properties of the studied parameter estimation algorithm. http://www.ifac-papersonline.net/static/luna/images/ifac/icon-download.gif http://www.ifac-papersonline.net/Detailed/27304.html 04:00 PM Wednesday 31, 1969 The windup properties of a recently suggested recursive parameter estimation algorithm are investigated in comparison with a number of well-known techniques such as the Normalized Least Squares Algorithm (NLMS) and the Kalman filter (KF). An acoustic echo cancellation application is used as a benchmark for comparing the properties of different approaches. The basic performance of the method, both for white and colored input signal, appears to be similar to that of the KF and superior to the NLMS. When the energy in the input signal decreases, the algorithm performs best of all compared estimation schemes. Once the solution of the Riccati equation of the algorithm converged to a user defined point, it will stay there even if the input excitation is reduced. This explains the good anti-windup properties of the method. http://www.ifac-papersonline.net/static/luna/images/ifac/icon-download.gif http://www.ifac-papersonline.net/Detailed/27305.html 04:00 PM Wednesday 31, 1969 Although partial response (PR) equalization employing the linearly constrained least-mean-square (LCLMS) algorithm is widely used in recording channels, there is no literature on its convergence analysis. Existing analyses of the LMS algorithm assume that the input signals are jointly Gaussian, which is an invalid assumption for PR equalization with binary input. In this paper, we present a convergence analysis of the LCLMS algorithm, without the Gaussian assumption. An approximate expression is derived for the misadjustment. It is shown that the step-size range required to guarantee stability is larger for binary data compared to Gaussian data. http://www.ifac-papersonline.net/static/luna/images/ifac/icon-download.gif http://www.ifac-papersonline.net/Detailed/27306.html 04:00 PM Wednesday 31, 1969 This contribution proposes a robust recursive algorithm for state estimation of linear multi-output systems with unknown but bounded disturbances corrupting both the state and measurement vectors. A novel approach based on state bounding techniques is presented. The proposed algorithm can be decomposed into two steps: time updating and observation updating that uses a switching estimation Kalman-like gain matrix. Particular emphasis will be given to the design of a weighting factor that ensures consistency of the estimated state vectors with the input-output data and the noise constraints and that enforces convergence properties. http://www.ifac-papersonline.net/static/luna/images/ifac/icon-download.gif http://www.ifac-papersonline.net/Detailed/27307.html 04:00 PM Wednesday 31, 1969 The problem of designing an output feedback compensator for any biased sinusoidal disturbance is considered. In this paper, we will develop the approach presented in (Marino et al., 2003). In (Marino et al., 2003) a compensator of order (2n+6) is proposed, which solves the posed problem by using the adaptive observers developed in (Marino and Tomei, 1995; Marino et al., 2001). This problem is solved by a (n+4)-order compensator. http://www.ifac-papersonline.net/static/luna/images/ifac/icon-download.gif http://www.ifac-papersonline.net/Detailed/27308.html 04:00 PM Wednesday 31, 1969 When data contains components with different characteristics and it is required to identify both, standard Gaussian regression, based on a model with a single stochastic process, is inadequate. In this paper, a novel adaptation of Gaussian regression, based on models with two stochastic processes, is presented. In both the prior and posterior joint probability distributions, the Gaussian processes for the two components are independent. The effectiveness of the revised Gaussian regression method is demonstrated by application to wind turbine time series data. http://www.ifac-papersonline.net/static/luna/images/ifac/icon-download.gif http://www.ifac-papersonline.net/Detailed/27309.html 04:00 PM Wednesday 31, 1969 This paper proposes a new method for prediction of chaotic time series based on Parallel Multi-Layer Perceptron (PMLP) net and dynamics reconstruction technique. The PMLP contains a number of multi-layer perceptron (MLP) subnets connected in parallel. Each MLP subnet predicts the future data independently with a different embedding dimension. The PMLP determines the final predicted result according to the weighted average of all sub-outputs. Simulation results show the effectiveness of the method. http://www.ifac-papersonline.net/static/luna/images/ifac/icon-download.gif http://www.ifac-papersonline.net/Detailed/27310.html 04:00 PM Wednesday 31, 1969 The theory of cyclostationarity has emerged as a new approach to characterizing a certain type of nonstationary signals. Many aspects of the spectral analysis of cyclostationary signals have been investigated but were essentially based on the use of the smoothed cyclic periodogram. This paper proposes a cyclic spectral estimator based on the averaged cyclic periodogram which benefits from better implementation properties. It shows that an unexpected but important condition for this estimator to be valid is to set enough overlap between adjacent segments in order to prevent cyclic leakage. It proves that setting the percentage of overlap to 75% with a hanning window, or 50% with a half-sine window fixes the problem. It also shows that in certain situations the cyclic leakage associated with the averaged cyclic periodogram can be made exactly zero, in contrast with the smoothed cyclic periodogram. Illustrative examples finally confirm the obtained results, where it is also demonstrated how to use them for efficiently estimating the Wigner-Ville spectrum. http://www.ifac-papersonline.net/static/luna/images/ifac/icon-download.gif