Identification algorithms based on H/sub infinity / state-space filtering techinques
Michael John Grimble, Rizwan Hashim, U. Shaked · 2005
An identification algorithm is proposed based on an extension of the results of H/sub infinity / filtering and Kalman filtering theory. The objective is to minimize the H/sub infinity / norm of the map from exogenous inputs (noise) to the estimation error of the parameters of an autoregressive moving average with external variable (ARMAX) model. The technique can provide an improved fit of a low-order estimated model to be obtained, relative to the usual least squares based algorithms. The function gamma which arises in H/sub infinity / filtering problems can be found by iteration, starting with a high initial value and then computing gamma online until it converges to the optimal value. An online check on the a posteriori covariance matrix is necessary to make sure the solution remains valid. The proposed algorithm is straightforward to implement and has the potential to improve the robustness of self-tuning filtering and control algorithms.>