Determination of state-space model uncertainty using bootstrap techniques

Robust control theory is widely used as the theoretical basis for the design of controllers with reduced sensibility to model errors. The model parameters variance–covariance (VC) matrix allows to design controllers with a consistent control action, even in the presence of moderate model mismatch. This paper presents a technique to extract the state-space model variance–covariance matrix using bootstrap techniques. The VC matrix is estimated from bootstrapped models using a first-order approximation of the model parameters space. The technique is applied by estimating the nominal model uncertainty of a deisopentanizer petrochemical unit. The model uncertainty is determined more accurately by the proposed method, when compared to the use of minimal canonical parameterization, providing better first-order approximation confidence intervals.