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Title: Data-Driven Superstabilizing Control of Error-in-Variables Discrete-Time Linear Systems
This paper proposes a method to find superstabilizing controllers for discrete-time linear systems that are consistent with a set of corrupted observations. The L-infinity bounded measurement noise introduces a bilinearity between the unknown plant parameters and noise terms. A superstabilizing controller may be found by solving a feasibility problem involving a set of polynomial nonnegativity constraints in terms of the unknown plant parameters and noise terms. A sequence of sum-of-squares (SOS) programs in rising degree will yield a super-stabilizing controller if such a controller exists. Unfortunately, these SOS programs exhibit very poor scaling as the degree increases. A theorem of alternatives is employed to yield equivalent, convergent (under mild conditions), and more computationally tractable SOS programs.  more » « less
Award ID(s):
2208182
NSF-PAR ID:
10447843
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
60th IEEE Conf. Decision and Control
Page Range / eLocation ID:
4924 to 4929
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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