Application of CRONE Control to a Sampled Time Varying System With Periodic Coefficients

2008 ◽  
Vol 130 (6) ◽  
Author(s):  
Jocelyn Sabatier ◽  
Aitor Garcia Iturricha ◽  
Mathieu Moze ◽  
Alain Oustaloup
Keyword(s):  
Robust Control ◽  
Frequency Domain ◽  
Discrete Time ◽  
Time Varying ◽  
Periodic Coefficients ◽  
Crone Control ◽  
Time Varying Systems

An application of CRONE robust control extended to discrete-time-varying systems with periodic coefficients is presented. The application is carried out in the frequency domain through the representation of considered systems using time varying z-transforms and time varying pseudofrequency responses.

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.

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.

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