Model error bounds for identification of stochastic models for control design

Abstract

A method for computing model error bounds for system identification of stochastic systems is disclosed. The model error bounds take the form of additive frequency-weighted singular value bounds such that they are directly used in H,and &mgr;-synthesis robust control design methods. The largest singular value of the additive uncertainty bound is determined by performing a high number of simulations for the model uncertainty. Simulated values of the uncertainty are computed for a large data population, such that each candidate entry of simulated value lies on the 3-sigma ellipsoids defined by the covariance functions. For each simulated value of uncertainty, the maximum singular values are then determined. In order to determine the scalar uncertainty function needed for robust control design, the maximum over the population of the maximum singular values of uncertainty simulated values is then computed.