WORST-CASE DETERMINISTIC IDENTIFICATION IN H-INFINITY - THE CONTINUOUS-TIME CASE

In this note, recent results obtained by the authors for worst-case/deterministic H infinity identification of discrete-time plants are extended to continuous-time plants. The problem considered involves identification of the transfer function of a stable strictly proper continuous-time plant from a finite number of noisy point samples of the plant frequency response. The assumed a priori information consists of a lower bound on the relative stability of the plant, an upper bound on a certain gain associated with the plant, an upper bound on the "roll-off rate" of the plant, and an upper bound on the noise level. Concrete plans of identification algorithms are provided for this problem. Explicit worst-case/deterministic error bounds are provided for each algorithm in these plans. These bounds establish that the given plans of algorithms are robustly convergent and (essentially) asymptotically optimal. Additionally, these bounds provide an a priori computable H infinity uncertainty specification, corresponding to the resulting identified plant transfer function, as an explicit function of the plant and noise a priori information and the data cardinality.