This paper investigates the problem of interval estimation for discrete-time linear descriptor systems subject to unknown-but-bounded uncertainties. Based on prediction-correction mechanism, we proposed a twostep interval estimator to over-estimate the bounds of the system states. To reduce the conservatism, a novel parameterization-based approach is presented. Besides, by using a Frobenius norm-based minimization approach, optimal correction is calculated. Compared to the volume criterion, the computational efficiency is greatly enhanced. Finally, two numerical examples are presented to illustrate the efficiency and potential application of the proposed approach.
This paper investigates the problem of interval estimation for discrete-time linear descriptor systems subject to unknown-but-bounded uncertainties. Based on prediction-correction mechanism, we proposed a twostep interval estimator to over-estimate the bounds of the system states. To reduce the conservatism, a novel parameterization-based approach is presented. Besides, by using a Frobenius norm-based minimization approach, optimal correction is calculated. Compared to the volume criterion, the computational efficiency is greatly enhanced. Finally, two numerical examples are presented to illustrate the efficiency and potential application of the proposed approach.