Nonlinear identification of skeletal muscle dynamics with sigma-point kalman filter for model-based FES

2008 ◽  
Author(s):  
Mitsuhiro Hayashibe ◽  
Philippe Poignet ◽  
David Guiraud ◽  
Hassan El Makssoud
Keyword(s):  
Skeletal Muscle ◽  
Kalman Filter ◽  
Nonlinear Identification ◽  
Sigma Point ◽  
Model Based ◽  
Muscle Dynamics

scholarly journals In Vivo Identification of Skeletal Muscle Dynamics with Nonlinear Kalman Filter: Comparison between EKF and SPKF

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Mitsuhiro Hayashibe ◽  
David Guiraud ◽  
Philippe Poignet
Keyword(s):  
Skeletal Muscle ◽  
Kalman Filter ◽  
Initial Conditions ◽  
Medial Gastrocnemius ◽  
Muscle Model ◽  
Unknown Parameters ◽  
Muscle System ◽  
Muscle Dynamics ◽  
Subject Specific

Skeletal muscle system has nonlinear dynamics and subject-specific characteristics. Thus, it is essential to identify the unknown parameters from noisy biomedical signals to improve the modeling accuracy in neuroprosthetic control. The objective of this work is to develop an experimental identification method for subject-specific biomechanical parameters of a physiological muscle model which can be employed to predict the nonlinear force properties of stimulated muscle. Our previously proposed muscle model, which can describe multiscale physiological system based on the Hill and Huxley models, was used for the identification. The identification protocols were performed on two rabbit experiments, where the medial gastrocnemius was attached to a motorized lever system to record the force by the nerve stimulation. The muscle model was identified using nonlinear Kalman filters: sigma-point and extended Kalman filter. The identified model was evaluated by comparison with experimental measurements in the cross-validation manner. The feasibility could be demonstrated by comparison between the estimated parameter and the measured value. The estimates with SPKF showed 5.7% and 2.9% error in each experiment with 7 different initial conditions. It reveals that SPKF has great advantage especially for the identification of multiscale muscle model which accounts for the high nonlinearity and discontinuous states between muscle contraction and relaxation process.

scholarly journals The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2085
Author(s):  
Xue-Bo Jin ◽  
Ruben Jonhson Robert RobertJeremiah ◽  
Ting-Li Su ◽  
Yu-Ting Bai ◽  
Jian-Lei Kong
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Future Development ◽  
Data Driven ◽  
Estimation Methods ◽  
Multiple Model ◽  
Development Trends ◽  
Research Results ◽  
Model Driven ◽  
Model Based

State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems’ development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.

Adaptive exact linearization control of batch polymerization reactors using a Sigma-Point Kalman Filter

2008 ◽  
Vol 18 (7-8) ◽  
pp. 663-675 ◽  
Author(s):  
Marc-André Beyer ◽  
Wolfgang Grote ◽  
Gunter Reinig
Keyword(s):  
Kalman Filter ◽  
Exact Linearization ◽  
Sigma Point

Model-Based Pileup Events Correction via Kalman-Filter Tunnels

2019 ◽  
Vol 66 (1) ◽  
pp. 528-535
Author(s):  
Bingqi Liu ◽  
Mingzhe Liu ◽  
Mingfu He ◽  
Yingjie Ma ◽  
Xianguo Tuo
Keyword(s):  
Kalman Filter ◽  
Model Based
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