Algorithms for identification in H∞with unequally spaced function measurements

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Abstract
Worst-case identification in H∞is considered in the situation in which corrupted frequency response measurements are available at an arbitrary set of frequencies. Two new classes of algorithms are presented: one yields polynomial models directly, the other is a two-stage algorithm producing rational models. Each has improved convergence rates for the class of exponentially stable discrete-time systems (with errors typically O(ε)+ O(Δr) for arbitrarily large r, where ε is the noise level and Δ is the maximum spacing between identification points). A numerical example is given.

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