Classical system identification in a deterministic setting

To reconcile identification with robust control, researchers have focused their attention on worst case identification. However, with assumptions such as unknown but bounded noise, worst case identification, leads to conservative bounds. Furthermore, these bounds are obtained at exponential cost. On the other hand in traditional identification, one obtains confidence bounds at polynomial cost. To overcome these shortcomings we explore several relaxations and restrictions on the worst case identification problem. In particular, we develop deterministic equivalent of stochastic identification