%AMiller, Jared%ADai, Tianyu%ASznaier, Mario%D2022%I
%K
%MOSTI ID: 10447843
%PMedium: X
%TData-Driven Superstabilizing Control of Error-in-Variables Discrete-Time Linear Systems
%XThis 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.
Country unknown/Code not availablehttps://doi.org/10.1109/CDC51059.2022.9992363OSTI-MSA