Fractional Order Systems Fractional Order PID Controller Chaotic Fractional Order Systems Field Programmable Gate Array, Microcontroller and Field Programmable Analog Array Implementation Switched Capacitor and Integrated Circuit Design Modeling of Ionic Polymeric Metal Composite

Fractional Order Systems: Modeling and Control Applications

In this paper, we present two novel algorithms to realize a finite dimensional, linear time-invariant state-space model from input-output data. The algorithms have a number of common features. They are classified as one of the subspace model identification schemes, in that a major part of the identification problem consists of calculating specially structured subspaces of spaces defined by the input-output data. This structure is then exploited in the calculation of a realization. Another common feature is their algorithmic organization: an RQ factorization followed by a singular value decomposition and the solution of an overdetermined set (or sets) of equations. The schemes assume that the underlying system has an output-error structure and that a measurable input sequence is available. The latter characteristic indicates that both schemes are versions of the MIMO Output-Error State Space model identification (MOESP) approach. The first algorithm is denoted in particular as the (elementary MOESP scheme)...

Subspace model identification-Part 1. The output-error state-space model identification class of algorithms

Subspace-based methods for the identification of linear time-invariant systems

Two noniterative subspace-based algorithms which identify linear, time-invariant MIMO (multi-input/multioutput) systems from frequency response data are presented. The algorithms are related to the recent time-domain subspace identification techniques. The first algorithm uses equidistantly, in frequency, spaced data and is strongly consistent under weak noise assumptions. The second algorithm uses arbitrary frequency spacing and is strongly consistent under more restrictive noise assumptions, promising results are obtained when the algorithms are applied to real frequency data originating from a large flexible structure.

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Subspace-based multivariable system identification from frequency response data

Nonlinear system identification in structural dynamics: 10 more years of progress

A Study of Random Vibration Characteristics of the Quarter-Car Model

https://sites.uclouvain.be/socn/archive/Documents/lec9a.pdf

Hüseyin Akçay

Papers

Subspace-based multivariable system identification from frequency response data

A Study of Random Vibration Characteristics of the Quarter-Car Model

Orthonormal basis functions for modelling continuous-time systems

Rational Basis Functions for Robust Identification from Frequency and Time-Domain Measurements

Short-term wind speed forecasting by spectral analysis from long-term observations with missing values