Nonlinear observer design by observer error linearization
SIAM Journal on Control and Optimization
Physica D
Input observability and input reconstruction
Automatica (Journal of IFAC)
Robust model-based fault diagnosis for dynamic systems
Robust model-based fault diagnosis for dynamic systems
Extracting Driving Signals from Non-Stationary Time Series
SBRN '02 Proceedings of the VII Brazilian Symposium on Neural Networks (SBRN'02)
Brief paper: Unbiased minimum-variance input and state estimation for linear discrete-time systems
Automatica (Journal of IFAC)
A New Formulation of the Rao-Blackwellized Particle Filter
SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
Input estimation in nonlinear dynamical systems using differential algebra techniques
Automatica (Journal of IFAC)
Posterior Cramer-Rao bounds for discrete-time nonlinear filtering
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
An EM Algorithm for Nonlinear State Estimation With Model Uncertainties
IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Particle filters for state-space models with the presence ofunknown static parameters
IEEE Transactions on Signal Processing
Complexity analysis of the marginalized particle filter
IEEE Transactions on Signal Processing
Marginalized particle filters for mixed linear/nonlinear state-space models
IEEE Transactions on Signal Processing
Nonlinear observers: a circle criterion design and robustness analysis
Automatica (Journal of IFAC)
Correspondence item: An innovations approach to fault detection and diagnosis in dynamic systems
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
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Estimation of additive driving-forces (e.g., hidden inputs) in nonlinear dynamic systems is addressed with varying amounts of a priori knowledge on system models exemplified by three typical scenarios: (1) there is no sufficient prior knowledge to build a mathematical model of the underlying system; (2) the system is partially described by an analytic model; (3) a complete and accurate model of the underlying system is available. Three algorithms are proposed for each scenario and analyzed comprehensively. The adaptive driving-force estimator (ADFE) [1,2] is used for the retrieval of driving-forces using only the system outputs for the first scenario. A variational Bayesian and a Bayesian algorithm are established for the second and the third scenarios, respectively. All three algorithms are studied in depth on a nonlinear dynamic system with equivalent computational resources, and the Posterior Cramer-Rao Lower Bounds (PCRLB) are specified as performance metrics for each case. The results lead to a thorough understanding of the capabilities and limitations of the ADFE, which manifests itself as an effective technique for the estimation of rapidly varying hidden inputs unless a complete and accurate model is available. Moreover, the methods developed in this paper facilitate a suitable framework for the construction of new and efficient tools for various input estimation problems. In particular, the proposed algorithms constitute a readily available basis for the design of novel input residual estimators to approach the Fault Diagnosis and Isolation (FDI) problem from a new and different perspective.