Kalman filtering: theory and practice
Kalman filtering: theory and practice
Robust model-based fault diagnosis for dynamic systems
Robust model-based fault diagnosis for dynamic systems
Eigenstructure Assignment for Control System Design
Eigenstructure Assignment for Control System Design
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Brief paper: LMI-based sensor fault diagnosis for nonlinear Lipschitz systems
Automatica (Journal of IFAC)
Observer-Based Optimal Fault Detection and Diagnosis Using Conditional Probability Distributions
IEEE Transactions on Signal Processing
New approach to mixed H2/H∞ filtering for polytopic discrete-time systems
IEEE Transactions on Signal Processing - Part II
Robust ℋ∞ filtering for continuous timevarying uncertain systems with deterministic input signals
IEEE Transactions on Signal Processing
Robust H/sub /spl infin// filtering for stochastic time-delay systems with missing measurements
IEEE Transactions on Signal Processing
Robust Filtering for Linear Time-Invariant Continuous Systems
IEEE Transactions on Signal Processing
Robust H2/H∞ filtering for linearsystems with error variance constraints
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
State/noise estimator for descriptor systems with application to sensor fault diagnosis
IEEE Transactions on Signal Processing
Sampled-data filtering with error covariance assignment
IEEE Transactions on Signal Processing
Technical Communique: Dynamic observers for linear time-invariant systems
Automatica (Journal of IFAC)
Design and analysis of robust residual generators for systems under feedback control
Automatica (Journal of IFAC)
| Hi-index | 35.68 |
In practical engineering, it is inevitable that a system is perturbed by noise signals. Unfortunately, H∞/H∞ filtering may fail to detect some faults when the noise distribution matrix are the same as the fault distribution matrix. In this paper, it is shown that the dynamic feedback gain of a dynamic filter introduces additional zeros to the filter, and both the filter poles and the additional zeros can be assigned arbitrarily. In order to attenuate band-limited noises, the zero assignment technique is used, and an optimal dynamic fault detection filtering approach is proposed by locating the zeros to the noise frequencies and optimizing the poles. Compared to other dynamic filter design approaches, the zero assignment technique gives a better tradeoff between more design freedom and computation costs. As shown in the simulation, a better noise attenuation and fault detection performance have been obtained. The zero assignment in multivariable fault detection filter design would be the main contribution of this paper.