Abstract
Pneumatic muscle actuators (PMA), owing to their obvious advantages over conventional linear actuators and pneumatic cylinders, have been recently used in the medical and industrial robotic applications. However, their potential has not been fully exploited due to their highly nonlinear and time dependent behavior. An attempt is being made in the proposed work to accurately predict the uncertain and ambiguous characteristics of PMA. It was revealed from a scrupulous review of the previous work that conventional tools such as analytical and numerical methods can model a nonlinear system but the time dependent behavior cannot be accurately modeled. In the present research, Artificial Intelligence (AI) based techniques such as Neural Network (NN) and Fuzzy Inference System (FIS) have been used and their results are analyzed. It was found that FIS based on Takagi-Sugeno-Kang inference mechanism provides better accuracy and can model the time dependency of PMA. However, to achieve higher accuracy from the Fuzzy model, its parameters are required to be optimized. Three different approaches, namely, gradient descent method (GD), genetic algorithms (GA) and Modified Genetic Algorithm (MGA) have been used to identify the fuzzy parameters. Results clearly illustrate the improved prediction performance of the MGA based fuzzy inference system. Compared to the previous research in dynamic modeling of PMA, the proposed fuzzy inference system is found to provide better prediction accuracy.
| Original language | English |
|---|---|
| Title of host publication | 2009 IEEE International Conference on Robotics and Biomimetics, ROBIO 2009 |
| Pages | 1451-1456 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2009 |
| Externally published | Yes |
| Event | 2009 IEEE International Conference on Robotics and Biomimetics, ROBIO 2009 - Guilin, China Duration: Dec 19 2009 → Dec 23 2009 |
Other
| Other | 2009 IEEE International Conference on Robotics and Biomimetics, ROBIO 2009 |
|---|---|
| Country | China |
| City | Guilin |
| Period | 12/19/09 → 12/23/09 |
Fingerprint
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Human-Computer Interaction
- Biomaterials
Cite this
Dynamic modeling of pneumatic muscles using modified fuzzy inference mechanism. / Jamwal, Prashant K.; Hussain, Shahid; Xie, Sheng Quan.
2009 IEEE International Conference on Robotics and Biomimetics, ROBIO 2009. 2009. p. 1451-1456 5420384.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Dynamic modeling of pneumatic muscles using modified fuzzy inference mechanism
AU - Jamwal, Prashant K.
AU - Hussain, Shahid
AU - Xie, Sheng Quan
PY - 2009
Y1 - 2009
N2 - Pneumatic muscle actuators (PMA), owing to their obvious advantages over conventional linear actuators and pneumatic cylinders, have been recently used in the medical and industrial robotic applications. However, their potential has not been fully exploited due to their highly nonlinear and time dependent behavior. An attempt is being made in the proposed work to accurately predict the uncertain and ambiguous characteristics of PMA. It was revealed from a scrupulous review of the previous work that conventional tools such as analytical and numerical methods can model a nonlinear system but the time dependent behavior cannot be accurately modeled. In the present research, Artificial Intelligence (AI) based techniques such as Neural Network (NN) and Fuzzy Inference System (FIS) have been used and their results are analyzed. It was found that FIS based on Takagi-Sugeno-Kang inference mechanism provides better accuracy and can model the time dependency of PMA. However, to achieve higher accuracy from the Fuzzy model, its parameters are required to be optimized. Three different approaches, namely, gradient descent method (GD), genetic algorithms (GA) and Modified Genetic Algorithm (MGA) have been used to identify the fuzzy parameters. Results clearly illustrate the improved prediction performance of the MGA based fuzzy inference system. Compared to the previous research in dynamic modeling of PMA, the proposed fuzzy inference system is found to provide better prediction accuracy.
AB - Pneumatic muscle actuators (PMA), owing to their obvious advantages over conventional linear actuators and pneumatic cylinders, have been recently used in the medical and industrial robotic applications. However, their potential has not been fully exploited due to their highly nonlinear and time dependent behavior. An attempt is being made in the proposed work to accurately predict the uncertain and ambiguous characteristics of PMA. It was revealed from a scrupulous review of the previous work that conventional tools such as analytical and numerical methods can model a nonlinear system but the time dependent behavior cannot be accurately modeled. In the present research, Artificial Intelligence (AI) based techniques such as Neural Network (NN) and Fuzzy Inference System (FIS) have been used and their results are analyzed. It was found that FIS based on Takagi-Sugeno-Kang inference mechanism provides better accuracy and can model the time dependency of PMA. However, to achieve higher accuracy from the Fuzzy model, its parameters are required to be optimized. Three different approaches, namely, gradient descent method (GD), genetic algorithms (GA) and Modified Genetic Algorithm (MGA) have been used to identify the fuzzy parameters. Results clearly illustrate the improved prediction performance of the MGA based fuzzy inference system. Compared to the previous research in dynamic modeling of PMA, the proposed fuzzy inference system is found to provide better prediction accuracy.
UR - http://www.scopus.com/inward/record.url?scp=77951481723&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77951481723&partnerID=8YFLogxK
U2 - 10.1109/ROBIO.2009.5420384
DO - 10.1109/ROBIO.2009.5420384
M3 - Conference contribution
AN - SCOPUS:77951481723
SN - 9781424447756
SP - 1451
EP - 1456
BT - 2009 IEEE International Conference on Robotics and Biomimetics, ROBIO 2009
ER -