Open Access
Identification of Linear Systems with Nonlinear Distortions
Reads0
Chats0
TLDR
A theoretical framework is proposed that extends the linear system description to include the impact of nonlinear distortions: the nonlinear system is replaced by a linear model plus a 'nonlinear noise source'.Abstract:
This paper studies the impact of nonlinear distortions on linear system identification. It collects a number of previously published methods in a fully integrated approach to measure and model these systems from experimental data. First a theoretical framework is proposed that extends the linear system description to include the impact of nonlinear distortions: the nonlinear system is replaced by a linear model plus a 'nonlinear noise source'. The class of nonlinear systems covered by this approach is described and the properties of the extended linear representation are studied. These results are used to design the experiments; to detect the level of the nonlinear distortions; to measure efficiently the 'best' linear approximation; to reveal the even or odd nature of the nonlinearity; to identify a parametric linear model; and to improve the model selection procedures in the presence of nonlinear distortions.read more
Citations
More filters
Journal ArticleDOI
From experiment design to closed-loop control
TL;DR: It is argued that a guiding principle should be to model as well as possible before any model or controller simplifications are made as this ensures the best statistical accuracy.
Journal ArticleDOI
System Identification Methods for (Operational) Modal Analysis: Review and Comparison
TL;DR: In this article, the authors extensively review operational modal analysis approaches and related system identification methods and compare them in an extensive Monte Carlo simulation study, and then compare the results with the results obtained in an experimental setting.
Book ChapterDOI
Nonlinear System Identification
TL;DR: This chapter contains sections titled: Historical Review Supervised Multilayer Networks unsupervised Neural Networks: Kohonen Network Unsupervised Networks: Adaptive Resonance Theory Network Model Validation and Recommended Exercises.
Journal ArticleDOI
Identification of nonlinear systems using Polynomial Nonlinear State Space models
TL;DR: A method to model nonlinear systems using polynomial nonlinear state space equations by identifying first the best linear approximation of the system under test is proposed.
Journal ArticleDOI
Nonlinear System Analysis and Identification from Random Data
A. V. Metcalfe,Julius S. Bendat +1 more
TL;DR: This paper presents a meta-analysis of statistical errors in Nonlinear Estimates of Linear and Nonlinear Systems and their applications in Input/Output Relationships and Bilinear and Trilinear Systems.
References
More filters
Journal ArticleDOI
Fusion of hypothesis testing for nonlinearity detection in small time series
TL;DR: Numerical results show that the method of fusion changes when the time series length increases, and the robustness of hypothesis testing for small time series is discussed.
Journal ArticleDOI
The use of binary sequences in determining the best linear approximation of nonlinear systems
TL;DR: It is found that MLBS's offer advantages when experiment time limitation prohibits a large amount of averaging, but IRBS's have the advantage that even order nonlinear contributions do not affect the quality of the estimate, but the disadvantage of either a longer experiment time or a lower frequency resolution.
Proceedings ArticleDOI
Feedback for nonlinear system identification
TL;DR: It is shown that output feedback is sufficient to solve the system identification problem in a two-step procedure, and numerical simulations of the identification of two classical systems that fit the assumed model structure are presented.
Higher order sinusoidal input describing functions : extending linear techniques towards non-linear systems analysis
TL;DR: In this thesis an alternative concept for frequency based nonlinear system analysis is presented, the required measurement techniques are described and some application examples are shown.
Batch-to-batch learning for model-based control of process systems with application to cooling crystallization
TL;DR: In this article, the authors used the measured data in order to refine from batch to batch the model that is used to design the controller, and the performance delivered by the model-based controller is expected to improve.