NDLI: Robust controllability of interval fractional order linear time invariant stochastic systems

Content Provider	IEEE Xplore Digital Library
Author	Caibin Zeng YangQuan Chen Qigui Yang
Copyright Year	2012
Description	Author affiliation: Mechatronics, Embedded Systems and Automation (MESA) Lab, School of Engineering, University of California, Merced 5200 North Lake Road, 95343, USA (YangQuan Chen) \|\| School of Sciences, South China University of Technology, Guangzhou 510640, China (Caibin Zeng; Qigui Yang)
Abstract	We deal with the robust controllability problem of the fractional order linear time invariant (FO-LTI) stochastic systems with interval coefficients. We present a necessary and sufficient condition for the controllability problem for the case when there is no interval uncertainty. Based on the concept of linear independency of interval vectors, we formulate the approach to check the robust controllability of interval FO-LTI stochastic systems by employing some simple but very effective sufficient condition for checking the linear independency of interval vectors. Finally, an illustrative example is presented. We show that this interval FO-LTI stochastic system is weakly controllable, while the corresponding deterministic system without noise perturbation is uncontrollable.
Starting Page	4047
Ending Page	4050
File Size	125913
Page Count	4
File Format	PDF
ISBN	9781467320658
ISSN	07431546
e-ISBN	9781467320665
e-ISBN	9781467320641
DOI	10.1109/CDC.2012.6425949
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-10
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stochastic systems Controllability Vectors Robustness Stability analysis Silicon Asymptotic stability Uncertain systems Fractional order stochastic systems Linear independency Interval linear time invariant systems Robust controllability
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Modeling and Simulation

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