A gradient algorithm for the identification of interconnected discrete time varying systems

2019 ◽  
Vol 37 (3) ◽  
pp. 909-928
Author(s):  
Yamna Ghoul
Keyword(s):  
Discrete Time ◽  
Parametric Identification ◽  
Identification Problem ◽  
Gradient Algorithm ◽  
Interconnected Systems ◽  
Time Varying ◽  
Content Type ◽  
Time Varying Parameters ◽  
Time Varying Systems ◽  
Vector Observation

Purpose An identification scheme to identify interconnected discrete-time (DT) varying systems. Design/methodology/approach The purpose of this paper is the identification of interconnected discrete time varying systems. The proposed technique permits the division of global system to many subsystems by building a vector observation of each subsystem and then using the gradient method to identify the time-varying parameters of each subsystem. The convergence of the presented algorithm is proven under a given condition. Findings The effectiveness of the proposed technique is then shown with application to a simulation example. Originality/value In the past decade, there has been a renewed interest in interconnected systems that are multidimensional and composed of similar subsystems, which interact with their closest neighbors. In this context, the concept of parametric identification of interconnected systems becomes relevant, as it considers the estimation problem of such systems. Therefore, the identification of interconnected systems is a challenging problem in which it is crucial to exploit the available knowledge about the interconnection structure. For time-varying systems, the identification problem is much more difficult. To cope with this issue, this paper addresses the identification of DT dynamical models, composed by the interconnection of time-varying systems.

Krein Space-based Hθ Fault Estimation for Linear Discrete Time-varying Systems

2009 ◽  
Vol 34 (12) ◽  
pp. 1529-1533 ◽  
Author(s):  
Mai-Ying ZHONG ◽  
Shuai LIU ◽  
Hui-Hong ZHAO
Keyword(s):  
Discrete Time ◽  
Krein Space ◽  
Fault Estimation ◽  
Time Varying ◽  
Time Varying Systems ◽  
Kreĭn Space

scholarly journals The VIT Transform Approach to Discrete-Time Signals and Linear Time-Varying Systems

Eng ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 99-125
Author(s):  
Edward W. Kamen
Keyword(s):  
Discrete Time ◽  
Initial Conditions ◽  
Linear Time ◽  
Order Term ◽  
Initial Time ◽  
Time Varying ◽  
Varying Coefficients ◽  
Time Signals ◽  
Time Varying Systems ◽  
Time Varying Coefficients

A transform approach based on a variable initial time (VIT) formulation is developed for discrete-time signals and linear time-varying discrete-time systems or digital filters. The VIT transform is a formal power series in z−1, which converts functions given by linear time-varying difference equations into left polynomial fractions with variable coefficients, and with initial conditions incorporated into the framework. It is shown that the transform satisfies a number of properties that are analogous to those of the ordinary z-transform, and that it is possible to do scaling of z−i by time functions, which results in left-fraction forms for the transform of a large class of functions including sinusoids with general time-varying amplitudes and frequencies. Using the extended right Euclidean algorithm in a skew polynomial ring with time-varying coefficients, it is shown that a sum of left polynomial fractions can be written as a single fraction, which results in linear time-varying recursions for the inverse transform of the combined fraction. The extraction of a first-order term from a given polynomial fraction is carried out in terms of the evaluation of zi at time functions. In the application to linear time-varying systems, it is proved that the VIT transform of the system output is equal to the product of the VIT transform of the input and the VIT transform of the unit-pulse response function. For systems given by a time-varying moving average or an autoregressive model, the transform framework is used to determine the steady-state output response resulting from various signal inputs such as the step and cosine functions.

scholarly journals An Extended Time Series Algorithm for Modal Identification from Nonstationary Ambient Response Data Only

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Chang-Sheng Lin ◽  
Dar-Yun Chiang ◽  
Tse-Chuan Tseng
Keyword(s):  
Time Series ◽  
Arma Model ◽  
Identification Problem ◽  
Nonstationary Time Series ◽  
Modal Identification ◽  
Ambient Vibration ◽  
Time Varying ◽  
Varying Parameters ◽  
Response Data ◽  
Time Varying Parameters

Modal Identification is considered from response data of structural systems under nonstationary ambient vibration. The conventional autoregressive moving average (ARMA) algorithm is applicable to perform modal identification, however, only for stationary-process vibration. The ergodicity postulate which has been conventionally employed for stationary processes is no longer valid in the case of nonstationary analysis. The objective of this paper is therefore to develop modal-identification techniques based on the nonstationary time series for linear systems subjected to nonstationary ambient excitation. Nonstationary ARMA model with time-varying parameters is considered because of its capability of resolving general nonstationary problems. The parameters of moving averaging (MA) model in the nonstationary time-series algorithm are treated as functions of time and may be represented by a linear combination of base functions and therefore can be used to solve the identification problem of time-varying parameters. Numerical simulations confirm the validity of the proposed modal-identification method from nonstationary ambient response data.

Discrete-Time Robust Control of Linear Periodically Time-Varying Systems

2007 ◽  
Author(s):  
S. Kalender ◽  
H. Flashner
Keyword(s):  
Robust Control ◽  
Discrete Time ◽  
Robustness Analysis ◽  
Value Theory ◽  
Time Varying ◽  
Sampled Data ◽  
Point Mapping ◽  
Time Dynamic ◽  
Time Representation ◽  
Time Varying Systems

An approach for robust control of periodically time-varying systems is proposed. The approach combines the point-mapping formulation and a parameterization of the control vector to formulate an equivalent time-invariant discrete-time representation of the system. The discrete-time representation of the dynamic system allows for the application of known sampled-data control design methodologies. A perturbed, discrete-time dynamic model is formulated and plant parametric uncertainty are obtained using a truncated point-mapping algorithm. The error bounds due to point-mapping approximation are computed and a robustness analysis problem of the system due to parametric uncertainties is formulated using structured singular value theory. The proposed approach is illustrated by two design examples. Simulation studies show good performance robustness of the control system to parameter perturbations and system nonlinearities.

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