Estimation of the Non-Measurable State Variables of a Transcutaneous Energy Transmission System for Artificial Human Implants Using Extended Kalman Filters

被引：3
|
作者：
Liu, Wei [1 ]
Tang, Houjun [1 ]
Fang, Wan [1 ]
Ye, Pengsheng [1 ]
机构：
[1] Shanghai Jiao Tong Univ, Dept Elect Engn, Shanghai, Peoples R China
来源：
CIRCUITS SYSTEMS AND SIGNAL PROCESSING | 2009年 / 28卷 / 04期
关键词：
Non-measurable state variables; Transcutaneous energy transmission system; Artificial human implants; Extended Kalman filter; Estimator;
D O I：
10.1007/s00034-009-9099-2
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Due to the separation of the two sides of the coupling network, the acquisition of data on the operating state variables of a transcutaneous energy transmission system (TETS) inside the human body is difficult. A non-measurable state estimation approach is used in this work to facilitate the estimation of non-measurable variables on the secondary side of the TETS, including the current of the secondary coil and the output voltage of the secondary side. The estimation algorithm is based on a discrete dynamic mathematical model of the TETS. Following this model, using the extended Kalman filter (EKF) algorithm, the complexity of the TETS for artificial human implants can be reduced, while the reliability is simultaneously enhanced. Additionally, as an adaptive filter, the EKF can also successfully filter out processing noise during energy transmission. All of the results are verified by simulation using MATLAB.
引用
收藏
页码：581 / 593
页数：13
相关论文
共 50 条
  • [1] Estimation of the Non-Measurable State Variables of a Transcutaneous Energy Transmission System for Artificial Human Implants Using Extended Kalman Filters
    Wei Liu
    Houjun Tang
    Wan Fang
    Pengsheng Ye
    Circuits, Systems & Signal Processing, 2009, 28 : 581 - 593
  • [2] STATE ESTIMATORS (OBSERVERS) FOR THE ONLINE ESTIMATION OF NON-MEASURABLE PROCESS VARIABLES
    VANDERHEIJDEN, RTJM
    HELLINGA, C
    LUYBEN, KCAM
    HONDERD, G
    TRENDS IN BIOTECHNOLOGY, 1989, 7 (08) : 205 - 209
  • [3] State of Charge Estimation Using Extended Kalman Filters for Battery Management System
    Taborelli, Carlo
    Onori, Simona
    2014 IEEE INTERNATIONAL ELECTRIC VEHICLE CONFERENCE (IEVC), 2014,
  • [4] Estimation of hidden state variables of the intracranial system using constrained nonlinear Kalman filters
    Hu, Xiao
    Nenov, Valeriy
    Vespa, Paul
    Bergsneider, Marvin
    2005 27TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-7, 2005, : 5631 - 5634
  • [5] Estimation of hidden state variables of the intracranial system using constrained nonlinear Kalman filters
    Hu, Xiao
    Nenov, Valeriy
    Bergsneider, Marvin
    Glenn, Thomas C.
    Vespa, Paul
    Martin, Neil
    IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2007, 54 (04) : 597 - 610
  • [6] Modeling and Simulation of a Transcutaneous Energy Transmission System Used in Artificial Organ Implants
    Fang, Wan
    Liu, Wei
    Qian, Jie
    Tang, Houjun
    Ye, Pengsheng
    ARTIFICIAL ORGANS, 2009, 33 (12) : 1069 - 1074
  • [7] State Estimation of Wastewater Treatment Processes Using Distributed Extended Kalman Filters
    Zeng, Jing
    Liu, Jinfeng
    Zou, Tao
    Yuan, Decheng
    2016 IEEE 55TH CONFERENCE ON DECISION AND CONTROL (CDC), 2016, : 6721 - 6726
  • [8] State estimation for heavy oil hydroprocessing reactors using extended Kalman filters
    Calderon, Cristian J.
    Ricardez-Sandoval, Luis
    Ancheyta, Jorge
    FUEL, 2020, 262 (262)
  • [9] State Estimation in TPN and PPN Guidance Laws by Using Unscented and Extended Kalman Filters
    Moosapour, S. H.
    Moosapour, S.
    Asadollahi, Mostafa
    2013 21ST IRANIAN CONFERENCE ON ELECTRICAL ENGINEERING (ICEE), 2013,
  • [10] Wave excitation force estimation of wave energy floats using extended Kalman filters
    Davis, Andrew F.
    Fabien, Brian C.
    OCEAN ENGINEERING, 2020, 198 (198)
12345