Abstract
This paper investigates theH∞fixed-lag fault estimator design for linear discrete time-varying (LDTV) systems with intermittent measurements, which is described by a Bernoulli distributed random variable. Through constructing a novel partially equivalent dynamic system, the fault estimator design is converted into a deterministic quadratic minimization problem. By applying the innovation reorganization technique and the projection formula in Krein space, a necessary and sufficient condition is obtained for the existence of the estimator. The parameter matrices of the estimator are derived by recursively solving two standard Riccati equations. An illustrative example is provided to show the effectiveness and applicability of the proposed algorithm.
Highlights
To satisfy the growing demands for reliability and safety in control systems, more and more research efforts are made for model-based fault detection (FD) during the past decades; see [1,2,3,4,5,6] and references therein
This paper investigates the H∞ fixed-lag fault estimator design for linear discrete time-varying (LDTV) systems with intermittent measurements, which is described by a Bernoulli distributed random variable
The FD issue concerns designing a fault detection filter (FDF) for generating a residual signal such that the sensitivity of residual to fault is intensified by enhancing the robustness to the disturbance
Summary
To satisfy the growing demands for reliability and safety in control systems, more and more research efforts are made for model-based fault detection (FD) during the past decades; see [1,2,3,4,5,6] and references therein. In [11, 12], unified optimal solutions are derived in the framework of maximizing H−/H∞ and H∞/H∞ FD performance indices for linear continuous time-varying (LCTV) and linear discrete time-varying (LDTV) systems, respectively. In [13,14,15], the H∞ filtering based fault estimation methods are proposed for LDTV systems in virtue of the Krein space based reorganized innovation analysis and projection theory in the background of [16,17,18,19,20,21]
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