An identification scheme for control and fault diagnosis

A robust scheme to estimate a set of models for a linear time-invariant system, subject to perturbations in the physical parameters, from a short-time data record is proposed. The true model as well as the disturbances affecting the system are assumed unknown. However, the physical parameters are assumed to enter the coefficients of the system transfer function multilinearly. A set of models is identified by perturbing the physical parameters one-at-time and using a two-stage identification scheme. Exploiting the assumed multilinearity, the estimated set of models is validated. The proposed scheme is evaluated on a number of simulated systems and on a physical robot manipulator arm.