scholarly journals Online failure probability estimation under state estimation error and its application to angle of attack control of a reentry vehicle

2019 ◽  
Author(s):  
Nicolas Merlinge ◽  
Thibault Cantou ◽  
Karim Dahia
Keyword(s):  
State Estimation ◽  
Failure Probability ◽  
Estimation Error ◽  
Angle Of Attack ◽  
Probability Estimation ◽  
Reentry Vehicle

scholarly journals Real-Time Hydraulic Interval State Estimation for Water Transport Networks: a Case Study

2017 ◽  
Author(s):  
Stelios G. Vrachimis ◽  
Demetrios G. Eliades ◽  
Marios M. Polycarpou
Keyword(s):  
State Estimation ◽  
Real Time ◽  
Water Distribution ◽  
Estimation Error ◽  
Distribution Networks ◽  
Water Levels ◽  
Water Distribution Networks ◽  
Unaccounted For Water ◽  
Tank Water

Abstract. Hydraulic state estimation in water distribution networks is the task of estimating water flows and pressures in the pipes and nodes of the network based on some sensor measurements. This requires a model of the network, as well as knowledge of demand outflow and tank water levels. Due to modeling and measurement uncertainty, standard state-estimation may result in inaccurate hydraulic estimates without any measure of the estimation error. This paper describes a methodology for generating hydraulic state bounding estimates based on interval bounds on the parametric and measurement uncertainties. The estimation error bounds provided by this method can be applied to estimate the unaccounted-for water in water distribution networks. As a case study, the method is applied to a transport network in Cyprus, using actual data in real-time.

Maximum Landing Footprint Calculation Method with Angle of Attack Optimization for Reentry Vehicle

2021 ◽  
pp. 3321-3333
Author(s):  
Chi Fenghua ◽  
Wu Meng ◽  
Li Fei ◽  
Hui Xiangyang ◽  
Teng Rui
Keyword(s):  
Calculation Method ◽  
Angle Of Attack ◽  
Reentry Vehicle

scholarly journals State Uncertainty Normality Detection

2019 ◽  
Vol 67 (3) ◽  
pp. 1044-1062
Author(s):  
Sven K. Flegel ◽  
James C. Bennett
Keyword(s):  
State Estimation ◽  
Orbit Determination ◽  
Estimation Error ◽  
The State ◽  
Body Motion ◽  
Optical Observations ◽  
Test Cases ◽  
Unscented Transform ◽  
State Uncertainty ◽  
Random Particle

AbstractTwo fundamentally different approaches of determining normality of the probability density function of the state estimation error are compared by application to a range of test cases. The first method is the Henze-Zirkler test, which operates on a random particle sample. The variability of its result is quantified. Using this method, departure from normality has been found to occur in three stages which are detailed. The second test compares the offset in whitened space of the predicted state to the predicted covariance mean obtained from the unscented transform. This test is much more efficient than the random particle based approach and can be applied using any perturbations model. The comparison is performed on the state estimation error in Cartesian space and using two-body motion without process noise. The more efficient, unscented transform based approach shows excellent agreement with the Henze-Zirkler test for constructed test cases. Application to orbit determination results from passive optical observations assessed with a Batch-Least-Squares orbit determination however reveals some discrepancies which have yet to be understood and underline the importance of rigorous testing.

Robust State Estimation for Linear Systems

1993 ◽  
Vol 115 (1) ◽  
pp. 193-196
Author(s):  
S. S. Garimella ◽  
K. Srinivasan
Keyword(s):  
Dynamic System ◽  
State Estimation ◽  
Real Time ◽  
Linear Systems ◽  
Upper Bound ◽  
Estimation Error ◽  
System Model ◽  
Linear Dynamic ◽  
Modeling Errors ◽  
System States

Real-time state estimation of a linear dynamic system using an observer, in the presence of modeling errors in the system model used by the observer and uncertainty in the initial system states, is considered here. A guideline for designing observers for multioutput systems is established, based on an expression for an upper bound on the norm of the state estimation error derived in this paper. An example is presented to illustrate the usefulness of this guideline.

Sign in / Sign up

Export Citation Format

Share Document