Frequency domain, parametric estimation of the evolution of the time-varying dynamics of periodically time-varying systems from noisy input-output observations
This paper presents a frequency domain, parametric identification method for continuous- and discrete-time, slow linear time-periodic (LTP) systems from input output measurements. In this framework, the output as well as the input is allowed to be corrupted by stationary noise (i. e. an errors-in-variables approach is adopted). It is assumed that the system under consideration can be excited by a broad-band periodic signal with a user-defined amplitude spectrum (i. e. a multisine), and that the periodicity of the excitation signal T-exc, can be synchronized with the periodicity of the time-variation T-sys (i.e. T-exc/T-sys is an element of Q), such that the system reaches a steady state (a periodic solution). T-sys, is also known as the pumping period. Once the parametric estimation of the time-evolution of the system parameters has been performed, the system model is evaluated at the level of the instantaneous transfer function (also known as system function, or parametric transfer function), which rigorously characterizes LW systems. If the dynamics of the LTP system are slowly varying or the system is linear parameter varying (LPV), a frozen transfer function approach is provided to easily visualize and assess the quality of the estimated model. To give the estimated quantities a quality label, uncertainty bounds on the model-related quantities (such as the time-periodic (TP) system parameters, the frozen transfer function, the frozen resonance frequency, etc) are derived in this paper as well. Besides, a clear distinction between the instantaneous and the frozen transfer function concept is made, and both can be estimated with the proposed identification scheme. The user decides which transfer function definition suits best its purpose in practice. Finally, the identification algorithm is applied to a simulation example and to real measurements on an extendible robot arm. (C) 2013 Elsevier Ltd. All rights reserved,
Source (journal)
Mechanical systems and signal processing. - London
London : 2014
47:1-2(2014), p. 151-174
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Web of Science
Creation 14.09.2015
Last edited 15.11.2017