scholarly journals Application of the Unscented Kalman Filter (UKF) Estimation Techniques for Fault Detection Diagnosis and Isolation (FDDI) in Attitude Control (AC) and Heating Ventilation Air Conditioning (HVAC) Systems

10.5772/6818 ◽  
2009 ◽  
Author(s):  
Nicolae Tudoroiu ◽  
Kash Khorasani ◽  
Mohammed Zaheeruddin ◽  
Eshan ◽  
Sobhani Tehrani ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Attitude Control ◽  
Air Conditioning ◽  
Unscented Kalman Filter ◽  
Hvac Systems ◽  
Estimation Techniques

scholarly journals Particle Swarm Optimization Based Adaptive Kalman Filters for Fault Diagnosis of Reaction Wheels

2021 ◽  
Author(s):  
Afshin Rahimi
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Unscented Kalman Filter ◽  
Particle Swarm ◽  
Filter Parameter ◽  
Filter Performance ◽  
Filter Tuning ◽  
Selection Of

There has been an increasing interest in fault diagnosis in recent years, as a result of the growing demand for higher performance, efficiency, reliability and safety in control systems. A faulty sensor or actuator may cause process performance degradation, process shut down, or a fatal accident. Quick fault detection and isolation can help avoid abnormal event progression and minimize the quality and productivity offsets. In space systems specifically, space and power are limited in the satellites, which means that hardware redundancy is not very practical. If actuator faults occur, analytical redundancy techniques should be employed to determine if, where, and how the fault(s) occurred. To do so, different approaches have been developed and studied and one of the wellknown approaches in the literature is using the Kalman Filter as an observer for the purpose of parameter estimation and fault detection. The gains for the filter should be selected and the selection of the process and measurement noise statistics, commonly referred to as “filter tuning,” is a major implementation issue for the Kalman filter. This process can have a significant impact on the filter performance. In practice, Kalman filter tuning is often an ad-hoc process involving a considerable amount of time for trial and error to obtain a filter with desirable –qualitative or quantitative- performance characteristics. This thesis focuses on presenting an algorithm for automation of the selection of the gains using an evolutionary swarm intelligence based optimization algorithm (Particle Swarm) to minimize the residuals of the estimated parameters. The methodology can be applied to any filter or controller but in this thesis, an Adaptive Unscented Kalman Filter parameter estimation applied to a reaction wheel unit is used for the purpose of performance evaluation of the proposed methodology.

UNSCENTED KALMAN FILTER FOR FAULT DETECTION

2005 ◽  
Vol 38 (1) ◽  
pp. 113-118 ◽  
Author(s):  
K. Xiong ◽  
C.W. Chan ◽  
H.Y. Zhang
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Unscented Kalman Filter

A Comparative Study of Friction Estimation and Compensation Using Extended, Iterated, Hybrid, and Unscented Kalman Filters

2013 ◽  
Author(s):  
Aftab Ahmad ◽  
Kjell Andersson ◽  
Ulf Sellgren
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Unscented Kalman Filter ◽  
Experimental Simulation ◽  
Estimation Technique ◽  
Friction Forces ◽  
Haptic Devices ◽  
Estimation Techniques ◽  
Iterated Extended Kalman Filter ◽  
Friction Estimation

Transparency is a key performance evaluation criterion for haptic devices, which describes how realistically the haptic force/torque feedback is mimicked from a virtual environment or in case of master-slave haptic device. Transparency in haptic devices is affected by disturbance forces like friction between moving parts. An accurate estimate of friction forces for observer based compensation requires estimation techniques, which are computationally efficient and gives reduced error between measured and estimated friction. In this work different estimation techniques based on Kalman filter, such as Extended Kalman filter (EKF), Iterated Extended Kalman filter (IEKF), Hybrid extended Kalman filter (HEKF) and Unscented Kalman filter (UKF) are investigated with the purpose to find which estimation technique that gives the most efficient and realistic compensation using online estimation. The friction observer is based on a newly developed friction smooth generalized Maxwell slip model (S-GMS). Each studied estimation technique is demonstrated by numerical and experimental simulation of sinusoidal position tracking experiments. The performances of the system are quantified with the normalized root mean-square error (NRMSE) and the computation time. The results from comparative analyses suggest that friction estimation and compensation based on Iterated Extended Kalman filter both gives a reduced tracking error and computational advantages compared to EKF, HEKF, UKF, as well as with no friction compensation.

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