Robust H Estimation with Application to Robust Fault Detection

Abstract

The key to estimator-based, robust fault detection is to generate residuals which are robust against plant uncertainties and external disturbance inputs, which in turn requires the design of robust estimators. Hence, this paper considers the design of robust H 2 estimators using a parameter-dependent bounding function approach in conjunction with multiplier theory (which is intimately related to mixed-structured singular value theory). Speci cally, the Popov-Tsypkin multiplier is used to develop an upper bound on an H 2 cost function over an uncertainty set. The robust H 2 estimation problem is formulated as a parameter optimization problem in which the upper bound is minimized subject to a Riccati equation constraint. A continuation algorithm that uses quasi-Newton (BFGS) corrections is developed to solve the minimization problem. The robust H 2 estimation framework is then applied to the robust fault detection of dynamic systems. The results are applied to a simpli ed longitudinal ight control system. It is shown that the robust fault detection procedure based on the robust H 2 estimation methodology proposed in this paper can reduce false alarm rates.

Sensor Faults Isolation in Networked Control Systems: Application to Mobile Robot Platoons

“…Gertler et al proposed the design of dynamic parity relations for fault detection and isolation [ 8 ]. The optimization-based approach usually using either or has also been done by Song et al and Niemann et al [ 9 , 10 ]. Menke et al and White et al used the Kalman filter for fault estimation in aerospace systems [ 11 , 12 ].…”