Using the Best Linear Approximation With Varying Excitation Signals for Nonlinear System Characterization

TL;DR

This paper investigates experimental techniques using varying excitation signals to accurately identify the internal structure of nonlinear systems through block-oriented models, enhancing measurement precision and reliability.

Contribution

It introduces a novel combination of strategies for system excitation that reduces distortion in frequency response measurements for nonlinear system characterization.

Findings

Improved accuracy in identifying system structure

Reduced measurement distortion with proposed excitation methods

Validated on real systems with feedback nonlinear blocks

Abstract

Block oriented model structure detection is quite desirable since it helps to imagine the system with real physical elements. In this work we explore experimental methods to detect the internal structure of the system, using a black box approach. Two different strategies are compared and the best combination of these is introduced. The methods are applied on two real systems with a static nonlinear block in the feedback path. The main goal is to excite the system in a way that reduces the total distortion in the measured frequency response functions to have more precise measurements and more reliable decision about the structure of the system.