Frequency-domain identification of linear time-invariant systems under nonstandard conditions

1997 ◽  
Vol 46 (1) ◽  
pp. 65-71 ◽  
Author(s):  
R. Pintelon ◽  
J. Schoukens
Keyword(s):  
Frequency Domain ◽  
Linear Time ◽  
Linear Time Invariant ◽  
Domain Identification ◽  
Frequency Domain Identification ◽  
Nonstandard Conditions ◽  
Linear Time Invariant Systems ◽  
Time Invariant

A Frequency Domain Identification Scheme for Control and Fault Diagnosis

1997 ◽  
Vol 119 (1) ◽  
pp. 48-56 ◽  
Author(s):  
G. Mallory ◽  
R. Doraiswami
Keyword(s):  
Frequency Domain ◽  
Linear Time ◽  
Robot Manipulator ◽  
Physical Parameters ◽  
Linear Time Invariant ◽  
Domain Identification ◽  
Frequency Domain Identification ◽  
Identification Technique ◽  
Manipulator Arm ◽  
Linear Time Invariant System

A robust scheme to estimate a set of models for a linear time-invariant system, subject to perturbations in the physical parameters, from a frequency response data record is proposed. The true model as well as the disturbances affecting the system are assumed unknown. However, the physical parameters are assumed to enter the coefficients of the system transfer function multilinearly. A set of models is identified by perturbing the physical parameters one-at-time and using a frequency domain identification technique. Exploiting the assumed multilinearity, the estimated set of models is validated. The proposed scheme is evaluated on a number of simulated systems, and on a physical robot manipulator arm.

scholarly journals Frequency-Domain Identification of Linear Time-Periodic Systems Using LTI Techniques

2009 ◽  
Vol 4 (4) ◽  
Author(s):  
Matthew S. Allen
Keyword(s):  
Frequency Domain ◽  
Linear Time ◽  
Periodic Trajectory ◽  
Fourier Series Expansion ◽  
Linear Time Invariant ◽  
Domain Identification ◽  
Jeffcott Rotor ◽  
Time Invariant ◽  
Frequency Domain Approach ◽  
Time Periodic

A variety of systems can be faithfully modeled as linear with coefficients that vary periodically with time or linear time-periodic (LTP). Examples include anisotropic rotor-bearing systems, wind turbines, and nonlinear systems linearized about a periodic trajectory. Many of these have been treated analytically in the literature, yet few methods exist for experimentally characterizing LTP systems. This paper presents a set of tools that can be used to identify a parametric model of a LTP system, using a frequency-domain approach and employing existing algorithms to perform parameter identification. One of the approaches is based on lifting the response to obtain an equivalent linear time-invariant (LTI) form and the other based is on Fourier series expansion. The development focuses on the preprocessing steps needed to apply LTI identification to the measurements, the postprocessing needed to reconstruct the LTP model from the identification results, and the interpretation of the measurements. This elucidates the similarities between LTP and LTI identification, allowing the experimentalist to transfer insight between the two. The approach determines the model order of the system and the postprocessing reveals the shapes of the time-periodic functions comprising the LTP model. Further postprocessing is also presented, which allows one to generate the state transition and time-varying state matrices of the system from the output of the LTI identification routine, so long as the measurement set is adequate. The experimental techniques are demonstrated on simulated measurements from a Jeffcott rotor mounted on an anisotropic flexible shaft supported by anisotropic bearings.

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