東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Real-time embedded framework for for...

Potluri, Chandrasekhar.

Linked to FindBook

Google Book

Amazon

博客來

Real-time embedded framework for force and position, estimation and control strategies for the smart prostheses.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Real-time embedded framework for force and position, estimation and control strategies for the smart prostheses./
Author:	Potluri, Chandrasekhar.
Description:	124 p.
Notes:	Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.
Contained By:	Dissertation Abstracts International74-09B(E).
Subject:	Engineering, Electronics and Electrical. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3570278
ISBN:	9781303138294

Real-time embedded framework for force and position, estimation and control strategies for the smart prostheses.
Potluri, Chandrasekhar.

Real-time embedded framework for force and position, estimation and control strategies for the smart prostheses. - 124 p.

Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.

Thesis (Ph.D.)--Idaho State University, 2013.

The main objective of this research is to obtain a dynamic relationship between the surface Electromyography (sEMG) signal and corresponding skeletal muscle force and joint angle, merge the data from different sensors using fusion based algorithms and feed this signal to a two stage real-time embedded controller. The proposed embedded system is capable of decoding the prerecorded surface electromyography as well as the sensory force feedback signal to control the force of the prosthetic hand prototype using a Programmable Intelligent Controller (PIC) such as 32MX360F512L. A fusion algorithm is developed to establish the correlation between the sEMG signal and the skeletal muscle force. Different control strategies: an entropy threshold value-based Proportional Integral (PI) controller, Model Reference Adaptive controller (MRAC) and optimal controller, Linear Quadratic Gaussian (LQG) controller along with a two-stage embedded design, are developed and used for the force control of the prosthetic hand. In summary, this research presents the results obtained and provides information on the proposed activities to study sEMG -- skeletal muscle force and angle relationship, implementation on the test bed and controller design. This research work can also be extensible to microcontroller-based data processing and hardware architecture, an important area in computer engineering.

ISBN: 9781303138294Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

Real-time embedded framework for force and position, estimation and control strategies for the smart prostheses.
LDR:02338nam a2200301 4500 001 1960774
005 20140624210006.5
008 150210s2013 ||||||||||||||||| ||eng d
020 $a 9781303138294
035 $a (MiAaPQ)AAI3570278
035 $a AAI3570278
040 $a MiAaPQ $c MiAaPQ
100 1 $a Potluri, Chandrasekhar. $3 2096495
245 1 0 $a Real-time embedded framework for force and position, estimation and control strategies for the smart prostheses.
300 $a 124 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.
500 $a Adviser: Steve Chiu.
502 $a Thesis (Ph.D.)--Idaho State University, 2013.
520 $a The main objective of this research is to obtain a dynamic relationship between the surface Electromyography (sEMG) signal and corresponding skeletal muscle force and joint angle, merge the data from different sensors using fusion based algorithms and feed this signal to a two stage real-time embedded controller. The proposed embedded system is capable of decoding the prerecorded surface electromyography as well as the sensory force feedback signal to control the force of the prosthetic hand prototype using a Programmable Intelligent Controller (PIC) such as 32MX360F512L. A fusion algorithm is developed to establish the correlation between the sEMG signal and the skeletal muscle force. Different control strategies: an entropy threshold value-based Proportional Integral (PI) controller, Model Reference Adaptive controller (MRAC) and optimal controller, Linear Quadratic Gaussian (LQG) controller along with a two-stage embedded design, are developed and used for the force control of the prosthetic hand. In summary, this research presents the results obtained and provides information on the proposed activities to study sEMG -- skeletal muscle force and angle relationship, implementation on the test bed and controller design. This research work can also be extensible to microcontroller-based data processing and hardware architecture, an important area in computer engineering.
590 $a School code: 0320.
650 4 $a Engineering, Electronics and Electrical. $3 626636
650 4 $a Computer Science. $3 626642
650 4 $a Engineering, Biomedical. $3 1017684
650 4 $a Engineering, Robotics. $3 1018454
690 $a 0544
690 $a 0984
690 $a 0541
690 $a 0771
710 2 $a Idaho State University. $3 1023169
773 0 $t Dissertation Abstracts International $g 74-09B(E).
790 $a 0320
791 $a Ph.D.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3570278