Xiong, Jun. Setmembership state estimation and application on fault detection. PhD, Institut National Polytechnique de Toulouse, 2013

(Document in English)
PDF  Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader 2MB 
Official URL: http://ethesis.inptoulouse.fr/archive/00002394/
Abstract
In this thesis, a new approach to estimation problems under the presence of bounded uncertain parameters and statistical noise has been presented. The objective is to use the uncertainty model which appears as the most appropriate for every kind of uncertainty. This leads to the need to consider uncertain stochastic systems and to study how the two types of uncertainty combine : statistical noise is modeled as the centered gaussian variable and the unknown but bounded parameters are approximated by intervals. This results in an estimation problem that demands the development of mixed filters and a settheoretic strategy. The attention is drawn on set inversion problems and constraint satisfaction problems. The former is the foundation of a method for solving interval equations, and the latter can significantly improve the speed of interval based arithmetic and algorithms. An important contribution of this work consists in proposing an interval matrix inversion method which couples the algorithm SIVIA with the construction of a list of constraint propagation problems. The system model is formalized as an uncertain stochastic system. Starting with the interval Kalman filtering algorithm proposed in [Chen 1997] and that we name the IKF, an improved interval Kalman filtering algorithm (iIKF) is proposed. This algorithm is based on interval conditional expectation for interval linear systems. The iIKF has the same structure as the conventional Kalman filter while achieving guaranteed statistical optimality. The recursive computational scheme is developed in the setmembership context. Our improvements achieve guaranteed interval inversion whereas the original version IKF [Chen 1997] uses an instance (the upper bound) of the interval matrix to avoid the possible singularity problems. This point of view leads to a suboptimal solution that does not preserve guaranteed results, some solutions being lost. On the contrary, in the presence of unknownbutbounded parameters and measurement statistical errors, our estimation approach in the form of the iIKF provides guaranteed estimates, while maintaining a computational burden comparable to that of classic statistical approaches. Several constraint based techniques have also been implemented to limit the overestimation effect due to interval propagation within the interval Kalman filter recursive structure. The results have shown that the iIKF out puts bounded estimates that enclose all the solutions consistent with bounded errors and achieves good overestimation control. iIKF is used to propose a fault detection algorithm which makes use of a SemiClosed Loop strategy which does not correct the state estimate with the measure as soon as a fault is detected. Two methods for generating fault indicators are proposed : they use the a priori state estimate and a threshold based on the a posteriori and a priori covariance matrix, respectively, and check the consistency against the measured output. Through different examples, the advantages of the iIKF with respect to previous versions are exhibited and the efficiency of the iIKF based SemiClosed Loop fault detection algorithm is clearly demonstrated.
Item Type:  PhD Thesis 

Uncontrolled Keywords:  
Institution:  Université de Toulouse > Institut National Polytechnique de Toulouse  Toulouse INP (FRANCE) 
Laboratory name:  
Research Director:  Jauberthie, Carine and TravéMassuyès, Louise 
Statistics:  download 
Deposited On:  10 Dec 2013 22:58 
Repository Staff Only: item control page