Hosam E. Emara-Shabaik

Electromechanical modelling of overhead cranes

A non-linear electromechanical model describing the dynamical behaviour of overhead cranes is presented. The model takes into consideration the non-linear dependence of the load sway on transients of driving motors during voltage disturbances and start-ups. This is extremely useful in practice since load sway control schemes normally utilize input voltages as control variables. The mechanical subsystem within the model is that of the load sway and has the swing angles and their derivatives as state variables. Each of the two driving motors, namely, the girder motor and the trolley motor, is represented by the classical fifth-order model of induction machines where electrical transients as well as mechanical transients are considered. The model is linearized using Taylor series expansion around a normal operating point. A numerical example is presented to illustrate the usefulness of the results.

New ℋ 2 Filter for Uncertain Singular Systems Using Strict LMIs

In this paper, the robust ℋ2 filtering problem for a class of linear singular systems with norm-bounded uncertainties is investigated. A class of linear regular filters, which can be realized in practice, is fully analyzed, and then necessary and sufficient conditions for ℋ2 performance of the filtered system are provided. It is established that the solution to the filtering design problem can be cast in the format of strict linear matrix inequalities (LMIs). A numerical example is worked out to illustrate the theoretical developments.

On identification of parallel block-cascade nonlinear models

The class of nonlinear systems studied in this paper is assumed to be modelled by parallel block-cascades. Such models are composed of parallel branches where each branch has a linear block in cascade with a zero-memory nonlinear block followed by another linear block. These types of models are extensively used to represent nonlinear dynamic systems and are known in the literature as Wiener-Hammerstein models. Using a zero-mean stationary white gaussian sequence as an input to such models, a structure identification criterion is developed, utilizing the bispectrum estimate of the output sequence only. The application of this criterion is shown by several simulation examples. Also, impulse response estimation of an example of such a model is considered to show the effectiveness of the proposed identification technique.

Recursive Parameter Identification of a Class of Nonlinear Systems From Noisy Measurements

A model is proposed to identify the parameters of a class of stochastic nonlinear systems. The model structure is made up of two linear dynamic elements separated by a nonlinear static one. The nonlinear element is assumed to be of the polynomial type with known order. The identification is based on input/output data where the output is contaminated with measurement noise. The convergence analysis of the proposed recursive identification algorithm utilizes stochastic Lyapunov functions. Sufficient conditions for the almost sure convergence of the estimated parameters to the true ones are obtained.

Non-linearity detection of weakly non-linear dynamic systems using cumulants

Non-linearity detection in dynamic systems is a fundamental issue in non-linear system identification. This problem is treated with the aid of the perturbation technique. A criterion for the detection of even non-linearities is developed in terms of the third-order cumulants of vector stochastic processes.

A robust H? filtering approach for singular systems

For a class of linear uncertain singular systems, the problem of designing robust H ? filter, which can be readily implemented in practice by conventional hardware, is investigated. It is established that the solution to the filtering problem can be cast in the format of linear matrix inequalities (LMIs). An important special cases is provided. A numerical example is worked out to illustrate the theoretical developments.

Nonlinear Systems Modeling & Identification Using Higher Order Statistics/Polyspectra

ABSTRACT Some important questions in modeling and identification of nonlinear dynamic systems are addressed. These questions deal with detecting nonlinear behavior in the system dynamics and the classification of the system model structures. Answers to these questions are provided in terms of third and fourth order cumulants, bispectrum and bicoherence of the system output only.

Non-parametric identification of generalized Hammerstein models

The Hammerstein model is considered in a generalized form, where its nonlinear element can have multi-inputs and a finite memory. The identification of the multi-input finite memory nonimearity and the impulse response sequence of the model is treated using a non-parametric approach. A numerical example is given. The identification results of the example illustrate the effectiveness of the developed technique.