Experimental validation of an Extended Kalman Filter estimating acetate concentration in E. coli cultures

2013 ◽  
Vol 23 (2) ◽  
pp. 148-157 ◽  
Author(s):  
L. Dewasme ◽  
G. Goffaux ◽  
A.-L. Hantson ◽  
A. Vande Wouwer
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Experimental Validation ◽  
Acetate Concentration ◽  
E Coli

scholarly journals Experimental validation of the proposed extended Kalman filter with unknown inputs algorithm based on data fusion

2019 ◽  
Vol 39 (4) ◽  
pp. 835-849 ◽  
Author(s):  
Jinshan Huang ◽  
Xianzhi Li ◽  
Xiongjun Yang ◽  
Zhupeng Zheng ◽  
Ying Lei
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Extended Kalman Filter ◽  
Experimental Validation ◽  
Structural Parameters ◽  
Wide Band ◽  
Unknown Inputs ◽  
Filter Methods ◽  
Filter Approach ◽  
Shear Building

The extended Kalman filter is a useful tool in the research of structural health monitoring and vibration control. However, the traditional extended Kalman filter approach is only applicable when the information of external inputs to structures is available. In recent years, some improved extended Kalman filter methods applied with unknown inputs have been proposed. The authors have proposed an extended Kalman filter with unknown inputs based on data fusion of partially measured displacement and acceleration responses. Compared with previous approaches, the drifts in the estimated structural displacements and unknown external inputs can be avoided. The feasibility of proposed extended Kalman filter with unknown inputs has been demonstrated by some numerical simulation examples. However, experimental validation of the proposed extended Kalman filter with unknown inputs has not been conducted. In this paper, an experiment is conducted to validate the effectiveness of the proposed approach. A five-story shear building model subjected to an unknown external excitation of wide-band white noise is conducted. Moreover, the data fusion of partially measured strain and acceleration responses from the building is adopted as it is difficult to accurately measure structural displacement in practice. Identified results show that the recently proposed extended Kalman filter with unknown inputs can be applied to identify structural parameters, structural states, and the unknown inputs in real time.

Combining Genetic Algorithms and Extended Kalman Filter to Estimate Ankle’s Muscle-Tendon Parameters

2015 ◽  
Author(s):  
Luis Enrique Coronado ◽  
Raul Chavez-Romero ◽  
Mauro Maya ◽  
Antonio Cardenas ◽  
Davide Piovesan
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Mechanical Model ◽  
Postural Stability ◽  
Human Subject ◽  
Experimental Validation ◽  
Quiet Standing ◽  
Experimental Measurements ◽  
Third Order ◽  
Biomechanical Parameter

This work proposes a set of simulation and experimental measurements to estimate muscle biomechanical parameter during human quiet standing. Understanding the mechanisms involved in postural stability is indispensable to improve the knowledge of how humans can regain balance against possible disturbances. Postural stability requires the ability to compensate the movement of the body’s center of gravity caused by external or internal perturbations. This paper describes the implementation of a hybrid parameter-estimation approach to infer the features of the human neuro-mechanical system during quiet standing and the recovery from a fall. The estimation techniques combines a genetic algorithm with the State-Augmented Extended Kalman Filter. These two algorithms running sequentially are utilized to estimate the musculo-skeletal parameters. This paper shows results of the approach when representing human standing as either a second-order or third order mechanical model. Experimental validation on a human subject is also presented.

scholarly journals Extended Kalman Filter Design for Acetate Estimation in E. coli Cultures

2011 ◽  
Vol 44 (1) ◽  
pp. 6218-6223 ◽  
Author(s):  
L. Dewasme ◽  
G. Goffaux ◽  
A.-L. Hantson ◽  
A. Vande Wouwer
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Filter Design ◽  
E Coli
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