The paper gives a survey of errors-in-variables methods in system identification. Background and motivation are given, and examples illustrate why the identification problem can be difficult. Under general weak assumptions, the systems are not identifiable, but can be parameterized using one degree-of-freedom. Examples where identifiability is achieved under additional assumptions are also provided. A number of approaches for parameter estimation of errors-in-variables models are presented. The underlying assumptions and principles for each approach are highlighted.

Errors-in-Variables Methods in System Identification

Nonlinear system identification is an extremely broad topic, since every system that is not linear is nonlinear. That makes it impossible to give a full overview of all aspects of the fi eld. For this reason, the selection of topics and the organization of the discussion are strongly colored by the personal journey of the authors in this nonlinear universe.

Nonlinear System Identification: A User-Oriented Road Map

Recent development in wireless technology enables communication between vehicles. The concept of cooperative adaptive cruise control (CACC)–which uses wireless communication between vehicles–aims at string stable behavior in a platoon of vehicles. “String stability” means any non-zero position, speed, and acceleration errors of an individual vehicle in a string do not amplify when they propagate upstream. In this article, we will discuss the string stability of CACC and evaluate its performance under varying packet loss ratios, beacon sending frequencies, and time headway settings in simulation experiments. The simulation framework is built up with a controller prototype, a traffic simulator, and a network simulator.

/pdf/evaluation-of-cacc-string-stability-using-sumo-simulink-and-6ewhpn8d29.pdf

Evaluation of CACC string stability using SUMO, Simulink and OMNeT++

Estimation of nonparametric noise and FRF models for multivariable systems—Part I: Theory

Identification of block-oriented nonlinear systems starting from linear approximations: A survey

The objective of this paper is twofold. The first part provides further insight in the statistical properties of the Welch power spectrum estimator. A major drawback of the Welch method reported in the literature is that the variance is not a monotonic decreasing function of the fraction of overlap. Selecting the optimal fraction of overlap, which minimizes the variance, is in general difficult since it depends on the window used. We show that the explanation for the nonmonotonic behavior of the variance, as reported in the literature, does not hold. In the second part, this extra insight allows one to eliminate the nonmonotonic behavior of the variance for the Welch power spectrum estimator (PSE) by introducing a small modification to the Welch method. The main contributions of this paper are providing extra insight in the statistical properties of the Welch PSE; modifying the Welch PSE to circular overlap-the variance is a monotonically decreasing function of the fraction of overlap, making the method more user friendly; and an extra reduction of variance with respect to the Welch PSE without introducing systematic errors-this reduction in variance is significant for a small number of data records only.

Welch Method Revisited: Nonparametric Power Spectrum Estimation Via Circular Overlap

In many engineering applications, linear models are preferred, even if it is known that the system is disturbed by nonlinear distortions. A large class of nonlinear systems, which are excited with a ldquoGaussianrdquo random excitation, can be represented as a linear system G BLA plus a nonlinear noise source Y S . The nonlinear noise source represents that part of the output that is not captured by the linear approximation. In this paper, it is shown that the best linear approximation G BLA and the power spectrum S Y S of the nonlinear noise source Y S are invariants for a wide class of excitations with a user-specified power spectrum. This shows that the alternative ldquolinear representationrdquo of a nonlinear system is robust, making its use in the daily engineering practice very attractive. This result also opens perspectives to a new generation of dynamic system analyzers that also provide information on the nonlinear behavior of the tested system without increasing the measurement time.

Robustness Issues of the Best Linear Approximation of a Nonlinear System

This book enables readers to understand system identification and linear system modeling through 100 practical exercises without requiring complex theoretical knowledge. The contents encompass state-of-the-art system identification methods, with both time and frequency domain system identification methods covered, including the pros and cons of each. Each chapter features MATLAB exercises, discussions of the exercises, accompanying MATLAB downloads, and larger projects that serve as potential assignments in this learn-by-doing resource.

R. Pintelon

Papers

Welch Method Revisited: Nonparametric Power Spectrum Estimation Via Circular Overlap

Robustness Issues of the Best Linear Approximation of a Nonlinear System

Mastering System Identification in 100 Exercises

Mastering System Identification in 100 Exercises: Schoukens/Mastering

A Frequency Domain Approach