Parameter identification and state estimation of a microalgae dynamical model in sulphur deprived conditions: Global sensitivity analysis, optimization criterion, extended Kalman filter

2014 ◽  
Vol 92 (8) ◽  
pp. 1378-1395
Author(s):  
Moemen Daboussy ◽  
Mariana Titica ◽  
Lionel Boillereaux
Keyword(s):  
Sensitivity Analysis ◽  
Kalman Filter ◽  
Parameter Identification ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
Global Sensitivity Analysis ◽  
Optimization Criterion ◽  
Dynamical Model ◽  
Global Sensitivity

Dimensionality Reduction of High-Fidelity Machine Tool Models by Using Global Sensitivity Analysis

2021 ◽  
pp. 1-13
Author(s):  
Johannes Ellinger ◽  
Thomas Semm ◽  
Michael F. Zäh
Keyword(s):  
Sensitivity Analysis ◽  
Machine Tool ◽  
Parameter Identification ◽  
Machine Tools ◽  
Global Sensitivity Analysis ◽  
Reference Model ◽  
Evaluation Criteria ◽  
Unknown Parameters ◽  
Global Sensitivity ◽  
Process Simulations

Abstract Models that are able to accurately predict the dynamic behavior of machine tools are crucial for a variety of applications ranging from machine tool design to process simulations. However, with increasing accuracy, the models tend to become increasingly complex, which can cause problems identifying the unknown parameters which the models are based on. In this paper, a method is presented that shows how parameter identification can be eased by systematically reducing the dimensionality of a given dynamic machine tool model. The approach presented is based on ranking the model's input parameters by means of a global sensitivity analysis. It is shown that the number of parameters, which need to be identified, can be drastically reduced with only limited impact on the model's fidelity. This is validated by means of model evaluation criteria and frequency response functions which show a mean conformity of 98.9 % with the full-scale reference model. The paper is concluded by a short demonstration on how to use the results from the global sensitivity analysis for parameter identification.

Cascaded Dual Extended Kalman Filter for Combined Vehicle State Estimation and Parameter Identification

2013 ◽  
Author(s):  
Ayyoub Rezaeian ◽  
Reza Zarringhalam ◽  
Saber Fallah ◽  
William Melek ◽  
Amir Khajepour ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
Vehicle State Estimation

Relative State Estimation for Spacecrafts in Proximity in the Presence of Sensor Noise

2013 ◽  
Vol 765-767 ◽  
pp. 2299-2304
Author(s):  
Xu Huang ◽  
Ye Yan ◽  
Yang Zhou
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
Relative Motion ◽  
Velocity Estimation ◽  
Light Detection And Ranging ◽  
Dynamical Model ◽  
Lidar System ◽  
Relative State ◽  
Angle Measurements

This paper develops a relative position and velocity estimation approach for spacecrafts in proximity. A dynamical model is built at first to describe the relative motion between the chaser and target. In this approach a light detection and ranging (LIDAR) system is used to perform the range and angle measurements of the target relative to the chaser. The three-axis magnetometer (TAM) and gyro are installed on the chaser to measure the chasers attitude. An extended Kalman filter (EKF) is designed to estimate the relative state by combination of the measurements and dynamical model. Numerical simulations prove the validity of proposed filter.

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