Dynamic modeling of pneumatic muscles using modified fuzzy inference mechanism

Prashant K. Jamwal, Shahid Hussain, Sheng Quan Xie

Research output: A Conference proceeding or a Chapter in BookConference contribution

6 Citations (Scopus)

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 languageEnglish
Title of host publication2009 IEEE International Conference on Robotics and Biomimetics, ROBIO 2009
Pages1451-1456
Number of pages6
DOIs
Publication statusPublished - 19 Dec 2009
Externally publishedYes
Event2009 IEEE International Conference on Robotics and Biomimetics, ROBIO 2009 - Guilin, China
Duration: 19 Dec 200923 Dec 2009

Conference

Conference2009 IEEE International Conference on Robotics and Biomimetics, ROBIO 2009
CountryChina
CityGuilin
Period19/12/0923/12/09

Fingerprint

Fuzzy inference
Pneumatics
Muscle
Actuators
Genetic algorithms
Linear actuators
Artificial intelligence
Nonlinear systems
Numerical methods
Robotics
Neural networks

Cite this

Jamwal, P. K., Hussain, S., & Xie, S. Q. (2009). Dynamic modeling of pneumatic muscles using modified fuzzy inference mechanism. In 2009 IEEE International Conference on Robotics and Biomimetics, ROBIO 2009 (pp. 1451-1456). [5420384] https://doi.org/10.1109/ROBIO.2009.5420384
Jamwal, Prashant K. ; Hussain, Shahid ; Xie, Sheng Quan. / Dynamic modeling of pneumatic muscles using modified fuzzy inference mechanism. 2009 IEEE International Conference on Robotics and Biomimetics, ROBIO 2009. 2009. pp. 1451-1456
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Jamwal, PK, Hussain, S & Xie, SQ 2009, Dynamic modeling of pneumatic muscles using modified fuzzy inference mechanism. in 2009 IEEE International Conference on Robotics and Biomimetics, ROBIO 2009., 5420384, pp. 1451-1456, 2009 IEEE International Conference on Robotics and Biomimetics, ROBIO 2009, Guilin, China, 19/12/09. https://doi.org/10.1109/ROBIO.2009.5420384

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: A Conference proceeding or a Chapter in BookConference contribution

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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.

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Jamwal PK, Hussain S, Xie SQ. Dynamic modeling of pneumatic muscles using modified fuzzy inference mechanism. In 2009 IEEE International Conference on Robotics and Biomimetics, ROBIO 2009. 2009. p. 1451-1456. 5420384 https://doi.org/10.1109/ROBIO.2009.5420384