Wrist Model based on Musculoskeletal System and its Application to Wrist Angle Estimation

2011 ◽  
Author(s):  
Eita Sawaguchi ◽  
Teruyoshi Sadahiro ◽  
Masami Iwase
Keyword(s):  
Musculoskeletal System ◽  
Angle Estimation ◽  
Model Based

Compressed Sensing Trilinear Model-Based Angle Estimation for Bistatic MIMO Radar

2013 ◽  
Vol 347-350 ◽  
pp. 1033-1038 ◽  
Author(s):  
Xiao Fei Zhang ◽  
Jian Feng Li ◽  
Ming Zhou ◽  
De Ben
Keyword(s):  
Compressed Sensing ◽  
Multiple Input Multiple Output ◽  
Estimation Algorithm ◽  
Mimo Radar ◽  
Estimation Problem ◽  
Angle Estimation ◽  
Model Based ◽  
Trilinear Model ◽  
Input Multiple Output ◽  
Esprit Algorithm

In this paper, we address the transmit angle and receive angle estimation problem for a bistatic multiple-input multiple-output (MIMO) radar. This paper links MIMO radar angle estimation problem to the compressed sensing trilinear model. Exploiting this link, it derives a compressed sensing trilinear model-based angle estimation algorithm, which can obtain automatically paired two-dimensional angle estimation. The proposed algorithm requires no spectral peak searching or pair matching, and it has better angle estimation performance than conventional algorithms including estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. Simulation results illustrate performance of the algorithm.

scholarly journals Model–based development of neuroprostheses for paraplegic patients

1999 ◽  
Vol 354 (1385) ◽  
pp. 877-894 ◽  
Author(s):  
Riener Robert
Keyword(s):  
Electrical Stimulation ◽  
Mathematical Models ◽  
Motor Neurons ◽  
Musculoskeletal System ◽  
Functional Electrical Stimulation ◽  
Human Subjects ◽  
Experimental Tests ◽  
Muscular Force ◽  
Force Output ◽  
Model Based

In paraplegic patients with upper motor neuron lesions the signal path from the central nervous system to the muscles is interrupted. Functional electrical stimulation applied to the lower motor neurons can replace the lacking signals. A so–called neuroprosthesis may be used to restore motor function in paraplegic patients on the basis of functional electrical stimulation. However, the control of multiple joints is difficult due to the complexity, nonlinearity, and time–variance of the system involved. Furthermore, effects such as muscle fatigue, spasticity, and limited force in the stimulated muscle further complicate the control task. Mathematical models of the human musculoskeletal system can support the development of neuroprostheses. In this article a detailed overview of the existing work in the literature is given and two examples developed by the author are presented that give an insight into model–based development of neuroprostheses for paraplegic patients. It is shown that modelling the musculoskeletal system can provide better understanding of muscular force production and movement coordination principles. Models can also be used to design and test stimulation patterns and feedback control strategies. Additionally, model components can be implemented in a controller to improve control performance. Eventually, the use of musculoskeletal models for neuroprosthesis design may help to avoid internal disturbances such as fatigue and optimize muscular force output. Furthermore, better controller quality can be obtained than in previous empirical approaches. In addition, the number of experimental tests to be performed with human subjects can be reduced. It is concluded that mathematical models play an increasing role in the development of reliable closed–loop controlled, lower extremity neuroprostheses.

A fusion methodology for sideslip angle estimation on the basis of kinematics-based and model-based approaches

2019 ◽  
Vol 234 (7) ◽  
pp. 1930-1943
Author(s):  
Xiaoyu Li ◽  
Nan Xu ◽  
Qin Li ◽  
Konghui Guo ◽  
Jianfeng Zhou
Keyword(s):  
Magnitude Estimation ◽  
Front Wheel ◽  
Lateral Acceleration ◽  
Area Network ◽  
Sideslip Angle ◽  
Small Magnitude ◽  
Angle Estimation ◽  
Model Based ◽  
Road Friction ◽  
Basic Approaches

This article introduces a reliable fusion methodology for vehicle sideslip angle estimation, which only needs the Controller Area Network–Bus signals of production vehicles and has good robustness to vehicle parameters, tire information, and road friction coefficient. The fusion methodology consists of two basic approaches: the kinematic-based approach and the model-based approach. The former is constructed into the extended Kalman filter for transient stage and large magnitude estimation, while the latter is designed to be an adaptive scheme for steady-state and small magnitude estimation. On this basis, combining the advantages of the two methods, a weight allocation strategy is proposed based on the front wheel steering angle and transient characteristics of lateral acceleration and yaw rate. The validity of the method is verified by simulation and experiment, and it is proved that the method can be effectively used for the sideslip angle estimation.

scholarly journals An Improved Trilinear Model-Based Angle Estimation Method for Co-Prime Planar Arrays

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4180
Author(s):  
Chenxi Guo ◽  
Xinhong Hao ◽  
Ping Li
Keyword(s):  
Estimation Method ◽  
Sparse Recovery ◽  
Estimation Methods ◽  
Reconstruction Method ◽  
Angle Estimation ◽  
Trilinear Decomposition ◽  
Model Based ◽  
Planar Arrays ◽  
Trilinear Model ◽  
Virtual Array

Angle estimation methods in two-dimensional co-prime planar arrays have been discussed mainly based on peak searching and sparse recovery. Peak searching methods suffer from heavy computational complexity and sparse recovery methods face some problems in selecting the regularization parameters. In this paper, we propose an improved trilinear model-based method for angle estimation for co-prime planar arrays in the view of trilinear decomposition, namely parallel factor analysis. Due to the principle of trilinear decomposition, our method does not require peak searching and can conduct auto-pairing easily, which can reduce the computational loads and avoid parameter selection problems. Furthermore, we exploit the virtual array concept of the whole co-prime planar array through the cross-correlation matrix obtained from the received signal data and present a matrix reconstruction method using the Khatri–Rao product to tackle the matrix rank deficiency problem in the virtual array condition. The simulation results show that our proposed method can not only achieve high estimation accuracy with low complexity compared to other similar approaches, but also utilize limited sensor number to implement the angle estimation tasks.

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