Robust l1 estimation using the Popov-Tsypkin multiplier with application to robust fault detection

Considers the design of robust l₁ estimators based on multiplier theory (which is intimately related to mixed structured singular value theory) and the application of robust l₁ estimators to robust fault detection. 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. Specifically, the Popov-Tsypkin multiplier is used to develop an upper bound on an l₁ cost function over an uncertainty set. The robust l₁ estimation problem is formulated as a parameter optimization problem in which the upper bound is minimized subject to a Riccati equation constraint. The estimation algorithm has two stages. The first stage solves a mixed-norm H₂/l₁ estimation problem. In the second stage the l₁ estimator is made robust. The robust l₁ estimation framework is then applied to robust fault detection of dynamic systems