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An algorithm for the robust exponential stabilization of a class of switched systems
Author(s) -
MancillaAguilar J. L.,
García R. A.
Publication year - 2014
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.3190
Subject(s) - bounded function , observability , control theory (sociology) , exponential growth , observer (physics) , norm (philosophy) , mathematics , observable , exponential stability , exponential function , state (computer science) , linear system , computer science , control (management) , algorithm , nonlinear system , mathematical analysis , quantum mechanics , artificial intelligence , political science , law , physics
Summary In this paper, we consider continuous‐time switched systems whose subsystems are linear, or, more generally, homogeneous of degree one. For that class of systems, we present a control algorithm that under certain conditions generates switching signals that globally exponentially stabilizes the switched system, even in the case in which there are model uncertainties and/or measurement errors, provided that the bounds of that uncertainties and errors depend linearly on the norm of the state of the system and are small enough in a suitable sense. We also show that in the case in which the measurement errors and the model uncertainties are bounded, the algorithm globally exponentially stabilizes the system in a practical sense, with a final error which depends linearly on the bounds of both the model uncertainties and the measurement errors. In other words, the closed‐loop system is exponentially input‐to‐state‐stable if one considers the perturbations and output measurements bounds as inputs. For switched linear systems, under mild observability conditions, we design an observer whose state‐estimation drives the control algorithm to exponentially stabilize the system in absence of perturbations and to stabilize it in an ultimately bounded way when the perturbations and the output measurement errors are bounded. Finally, we illustrate the behavior of the algorithm by means of simulations. Copyright © 2014 John Wiley & Sons, Ltd.