z-logo
Premium
Worst‐case stability and performance with mixed parametric and dynamic uncertainties
Author(s) -
Apkarian Pierre,
Noll Dominikus
Publication year - 2016
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.3628
Subject(s) - parametric statistics , mathematical optimization , upper and lower bounds , stability (learning theory) , convergence (economics) , computer science , set (abstract data type) , certificate , extension (predicate logic) , mathematics , algorithm , mathematical analysis , economics , programming language , economic growth , statistics , machine learning
Summary This work deals with computing the worst‐case stability and the worst‐case H ∞ performance of linear time‐invariant systems subject to mixed real‐parametric and complex‐dynamic uncertainties in a compact parameter set. Our novel algorithmic approach is tailored to the properties of the nonsmooth worst‐case functions associated with stability and performance, and this leads to a fast and reliable optimization method, which finds good lower bounds of μ . We justify our approach theoretically by proving a local convergence certificate. Because computing μ is known to be NP‐hard, our technique should be used in tandem with a classical μ upper bound to assess global optimality. Extensive testing indicates that the technique is practically attractive. Copyright © 2016 John Wiley & Sons, Ltd.

This content is not available in your region!

Continue researching here.

Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom