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

Handling peak amplitude constraints, or equivalently <inline-formula> <tex-math notation="LaTeX">l_{\infty } </tex-math></inline-formula>-norm constraints, is an important application demand in experiment design for system identification. The aim of this letter is t...

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Bibliographic Details
Published inIEEE control systems letters Vol. 6; pp. 1784 - 1789
Main Authors Dirkx, Nic, Bosselaar, Marcel, Oomen, Tom
Format Journal Article
LanguageEnglish
Published IEEE 2022
Subjects
Approximation algorithms
Complexity theory
Convergence
Crest-factor optimization
experiment design
MIMO communication
Minimization
Optimization
Smoothing methods
system identification
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ISSN2475-1456
2475-1456
DOI10.1109/LCSYS.2021.3133655

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Summary:Handling peak amplitude constraints, or equivalently <inline-formula> <tex-math notation="LaTeX">l_{\infty } </tex-math></inline-formula>-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 <inline-formula> <tex-math notation="LaTeX">l_{\infty } </tex-math></inline-formula>-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.
ISSN:2475-1456
2475-1456
DOI:10.1109/LCSYS.2021.3133655