A Comparative Study of Extended Kalman Filtering and Unscented Kalman Filtering on Lie Group for Stewart Platform State Estimation

2021 ◽  
Author(s):  
Binhai Xie ◽  
Shuling Dai
Keyword(s):  
Comparative Study ◽  
State Estimation ◽  
Kalman Filtering ◽  
Lie Group ◽  
Stewart Platform ◽  
Extended Kalman Filtering

Configuration Estimation of Gough-Stewart Platforms Using Extended Kalman Filtering

2000 ◽  
Author(s):  
Ernest D. Fasse ◽  
Albert J. Wavering
Keyword(s):  
Real Time ◽  
Kalman Filtering ◽  
Time Estimation ◽  
Stewart Platform ◽  
Attitude Estimation ◽  
Extended Kalman Filtering ◽  
Filtering Algorithms ◽  
Satellite Attitude ◽  
Deterministic Methods ◽  
Real Time Estimation

Abstract This paper develops extended Kalman filtering algorithms for a generic Gough-Stewart platform assuming realistically available sensors such as length sensors, rate gyroscopes, and accelerometers. The basic idea is to extend existing methods for satellite attitude estimation. The nondeterministic methods are meant to be a practical alternative to existing iterative, deterministic methods for real-time estimation of platform configuration.

scholarly journals Kalman Filtering Applied to Induction Motor State Estimation

2020 ◽  
Author(s):  
Yassine Zahraoui ◽  
Mohamed Akherraz
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Induction Motor ◽  
Kalman Filtering ◽  
Speed Estimation ◽  
Stochastic Algorithm ◽  
State Variables ◽  
Extended Kalman Filtering ◽  
Filtering Theory ◽  
State Observation

This chapter presents a full definition and explanation of Kalman filtering theory, precisely the filter stochastic algorithm. After the definition, a concrete example of application is explained. The simulated example concerns an extended Kalman filter applied to machine state and speed estimation. A full observation of an induction motor state variables and mechanical speed will be presented and discussed in details. A comparison between extended Kalman filtering and adaptive Luenberger state observation will be highlighted and discussed in detail with many figures. In conclusion, the chapter is ended by listing the Kalman filtering main advantages and recent advances in the scientific literature.

scholarly journals Extended Kalman filtering for state estimation of a Hill muscle model

2018 ◽  
Vol 12 (3) ◽  
pp. 384-394 ◽  
Author(s):  
Hanieh Mohammadi ◽  
Hong Yao ◽  
Gholamreza Khademi ◽  
Thang T. Nguyen ◽  
Dan Simon ◽  
...  
Keyword(s):  
State Estimation ◽  
Kalman Filtering ◽  
Muscle Model ◽  
Extended Kalman Filtering ◽  
Hill Muscle Model

scholarly journals Hybrid extended Kalman filtering and noise statistics optimization for produce wash state estimation

2017 ◽  
Vol 212 ◽  
pp. 136-145 ◽  
Author(s):  
Vahid Azimi ◽  
Daniel Munther ◽  
Seyed Abolfazl Fakoorian ◽  
Thang Tien Nguyen ◽  
Dan Simon
Keyword(s):  
State Estimation ◽  
Kalman Filtering ◽  
Extended Kalman Filtering ◽  
Noise Statistics
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