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- W4387245217 abstract "We use polynomial approximation theory to perform data-driven analysis and control of linear, continuous-time invariant systems. We transform the continuous-time input- and state trajectories into discrete sequences consisting of the coefficients of their orthogonal polynomial bases representations. We show that the dynamics of the transformed input- and state signals and those of the original continuous-time trajectories are described by the same system matrices. We investigate informativity, quadratic stabilization, and <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$mathcal {H}_{2}$</tex-math></inline-formula> -performance problems for continuous-time systems. We deal with the case in which machine-precision accuracy in the representation of continuous-time signals can be achieved from the data using a finite number of basis elements, and the case in which the approximation error is non-negligible." @default.
- W4387245217 created "2023-10-03" @default.
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- W4387245217 date "2023-01-01" @default.
- W4387245217 modified "2023-10-03" @default.
- W4387245217 title "Orthogonal polynomial bases for data-driven analysis and control of continuous-time systems" @default.
- W4387245217 doi "https://doi.org/10.1109/tac.2023.3321214" @default.
- W4387245217 hasPublicationYear "2023" @default.
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