A Fast Smoothing-Based Algorithm to Generate l-Norm Constrained Signals for Multivariable Experiment Design

Nic Dirkx (Corresponding author), Marcel Bosselaar, Tom Oomen

Research output: Contribution to journalArticleAcademicpeer-review

7 Downloads (Pure)

Abstract

Handling peak amplitude constraints, or equivalently l∞-norm constraints, is an important application demand in experiment design for system identification. The aim of this letter is to present a method for the design of excitation signals with prescribed power spectrum under l∞-norm constraints for systems with many inputs and outputs. The method exploits an exponential smoothing function in an iterative algorithm. Fast convergence is achieved by a computationally efficient construction of the gradient and the Hessian matrix. Experimental results show excellent convergence behavior that overcomes local minima, while significantly reducing computation time compared to existing techniques.

Original languageEnglish
Pages (from-to)1784-1789
Number of pages6
JournalIEEE Control Systems Letters
Volume6
DOIs
Publication statusPublished - 2022

Keywords

  • Crest-factor optimization
  • experiment design
  • system identification

Fingerprint

Dive into the research topics of 'A Fast Smoothing-Based Algorithm to Generate l-Norm Constrained Signals for Multivariable Experiment Design'. Together they form a unique fingerprint.

Cite this