Three-stage unscented Kalman filter for state and fault estimation of nonlinear system with unknown input

2017 ◽  
Vol 354 (18) ◽  
pp. 8421-8443 ◽  
Author(s):  
Mengli Xiao ◽  
Yongbo Zhang ◽  
Huimin Fu
Keyword(s):  
Kalman Filter ◽  
Nonlinear System ◽  
Unscented Kalman Filter ◽  
Fault Estimation ◽  
Unknown Input

scholarly journals Aircraft Inertial Measurement Unit Fault Diagnosis Based on Optimal Two-Stage UKF

2018 ◽  
Vol 36 (5) ◽  
pp. 933-941
Author(s):  
Qizhi He ◽  
Weiguo Zhang ◽  
Degang Huang ◽  
Huakun Chen ◽  
Jinglong Liu
Keyword(s):  
Kalman Filter ◽  
Fault Diagnosis ◽  
Nonlinear System ◽  
Inertial Measurement Unit ◽  
Unscented Kalman Filter ◽  
Optimal Estimation ◽  
Measurement Unit ◽  
Two Stage ◽  
Inertial Measurement ◽  
System States

Optimal two stage Kalman filter (OTSKF) is able to obtain optimal estimation of system states and bias for linear system which contains random bias. Unscented Kalman filter (UKF) is a conventional nonlinear filtering method which utilizes Sigmas point sampling and unscented transformation technology realizes propagation of state means and covariances through nonlinear system. Aircraft is a typical complicate nonlinear system, this paper treats the faults of Inertial Measurement Unit (IMU) as random bias, established a filtering model which contains faults of IMU. Hybird the two stage filtering technique and UKF, this paper proposed an optimal two stage unscented Kalman filter (OTSUKF) algorithm which is suitable for fault diagnosis of IMU, realized optimal estimation of system states and faults identification of IMU via proposed innovative designing method of filtering model and the algorithm was validated that it is robust to wind disterbance via real flight data and it is also validated that proposed OTSUKF is optimal in the existance of wind disturbance via comparing with the existance iterated optimal two stage extended kalman filter (IOTSEKF) method.

Study on SRUKF Applied in Initial Alignment with Large Misalignment Angle on Stationary Base of SINS

2011 ◽  
Vol 383-390 ◽  
pp. 5088-5093 ◽  
Author(s):  
Kai Cheng ◽  
Chun Mei Huang ◽  
Yue Yuan Zhao
Keyword(s):  
Kalman Filter ◽  
Nonlinear System ◽  
Inertial Navigation System ◽  
Unscented Kalman Filter ◽  
Estimation Accuracy ◽  
Alignment Error ◽  
Square Root ◽  
Misalignment Angle ◽  
Initial Alignment ◽  
Better Than

The initial alignment error model of SINS (Strap-down Inertial Navigation System) with large misalignment angle is nonlinear. The traditional EKF (Extended Kalman Filter) was used to linearization a nonlinear system, but its performance is limited. In this paper we use the SRUKF (Square Root Unscented Kalman Filter) to process this nonlinear system and the results indicate that SRUKF is better than EKF in convergence speed and estimation accuracy.

Adaptive Kalman filtering for sensor fault estimation and isolation of satellite attitude control based on descriptor systems

2019 ◽  
Vol 41 (6) ◽  
pp. 1686-1698 ◽  
Author(s):  
Mao Wang ◽  
Tiantian Liang
Keyword(s):  
Kalman Filter ◽  
Attitude Control ◽  
Unscented Kalman Filter ◽  
Fault Isolation ◽  
Descriptor Systems ◽  
Fault Estimation ◽  
Multiple Model ◽  
Sensor Fault ◽  
Systems Model ◽  
Sensor Fault Estimation

Sensor fault estimation and isolation is significant for an attitude control systems model of a satellite, as it works in a complex environment. The standard unscented Kalman filter algorithm may lose its accuracy when the noise is considerable. Therefore, an adaptive filtering algorithm is proposed based on the sampled-data descriptor model. The performance of the unscented Kalman filter in sensor fault estimation is improved by the adaptive algorithm depending on innovation and the measurement residual, and its convergence is guaranteed. Combining the adaptive unscented Kalman filter with the multiple-model adaptive estimation, a sensor fault isolation method is proposed. Finally, simulation examples show that this algorithm has better estimating accuracy and isolation results.

scholarly journals Fault detection in a three-tank hydraulic system using unknown input observer and extended Kalman filter

2021 ◽  
Vol 9 (4A) ◽  
Author(s):  
Ayman E. O. HASSAN ◽  
◽  
Tasnim A. A. MOHAMMED ◽  
Aşkın DEMİRKOL ◽  
◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Hydraulic System ◽  
Fault Estimation ◽  
Linear Matrix ◽  
Unknown Input ◽  
Matrix Inequality ◽  
Sensor Faults ◽  
Unknown Input Observer ◽  
System A

This paper presents the problem of fault diagnosis in a three-tank hydraulic system. A mathematical model of the system is developed in order to apply two different observing algorithms. Unknown Input Observer (UIO) and Extended Kalman Filter (EKF) have been used to detect and isolate actuator and sensor faults. For Unknown Input Observer (UIO), residuals are calculated from the measured and estimated output according to the eigenvalues of the system after processed by Linear Matrix Inequality (LMI). Extended Kalman filter uses process and measurement noise variances for state estimation. Unknown Input Observer and Extended Kalman Filter's performance in fault estimation and isolation is evaluated under different scenarios. Using Extended Kalman Filter (EKF), faults can be diagnosed effectively in the presence of noise, while Unknown Input Observer (UIO) is working better in the absence of noise, and simulation results illustrate that clearly.

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