Abstract
Pneumatic muscle actuators (PMA) show great potential in wearable and compliant rehabilitation devices as they are flexible and lightweight. However, the varying and non-linear behavior of the actuators imposes modeling and control challenges, which are difficult to comprehend. This research proposes a new wearable ankle rehabilitation robot, first of its kind in the world driven by PMAs in a parallel form. The focus of this presented work is to develop an iterative controller to overcome the challenges for PMA driven devices. A fuzzy feedforward controller is proposed to accurately predict the behavior of PMA. A modified Genetic Algorithm (GA) is developed to identify the optimal set of parameters for the fuzzy controller. The iterative controller has been tested on the proposed PMA driven ankle rehabilitation robot, and is found capable of mapping the complex relationship in length, force and pressure of the PMA with high accuracy. Experimental results show excellent trajectory tracking performance of the controller when given various desired trajectories.
| Original language | English |
|---|---|
| Pages (from-to) | 8128-8137 |
| Number of pages | 10 |
| Journal | Expert Systems with Applications |
| Volume | 38 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - Jul 2011 |
| Externally published | Yes |
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Keywords
- Fuzzy control
- Genetic Algorithm
- Pneumatic muscle actuators
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Engineering(all)
Cite this
An iterative fuzzy controller for pneumatic muscle driven rehabilitation robot. / Xie, S. Q.; Jamwal, P. K.
In: Expert Systems with Applications, Vol. 38, No. 7, 07.2011, p. 8128-8137.Research output: Contribution to journal › Article
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TY - JOUR
T1 - An iterative fuzzy controller for pneumatic muscle driven rehabilitation robot
AU - Xie, S. Q.
AU - Jamwal, P. K.
PY - 2011/7
Y1 - 2011/7
N2 - Pneumatic muscle actuators (PMA) show great potential in wearable and compliant rehabilitation devices as they are flexible and lightweight. However, the varying and non-linear behavior of the actuators imposes modeling and control challenges, which are difficult to comprehend. This research proposes a new wearable ankle rehabilitation robot, first of its kind in the world driven by PMAs in a parallel form. The focus of this presented work is to develop an iterative controller to overcome the challenges for PMA driven devices. A fuzzy feedforward controller is proposed to accurately predict the behavior of PMA. A modified Genetic Algorithm (GA) is developed to identify the optimal set of parameters for the fuzzy controller. The iterative controller has been tested on the proposed PMA driven ankle rehabilitation robot, and is found capable of mapping the complex relationship in length, force and pressure of the PMA with high accuracy. Experimental results show excellent trajectory tracking performance of the controller when given various desired trajectories.
AB - Pneumatic muscle actuators (PMA) show great potential in wearable and compliant rehabilitation devices as they are flexible and lightweight. However, the varying and non-linear behavior of the actuators imposes modeling and control challenges, which are difficult to comprehend. This research proposes a new wearable ankle rehabilitation robot, first of its kind in the world driven by PMAs in a parallel form. The focus of this presented work is to develop an iterative controller to overcome the challenges for PMA driven devices. A fuzzy feedforward controller is proposed to accurately predict the behavior of PMA. A modified Genetic Algorithm (GA) is developed to identify the optimal set of parameters for the fuzzy controller. The iterative controller has been tested on the proposed PMA driven ankle rehabilitation robot, and is found capable of mapping the complex relationship in length, force and pressure of the PMA with high accuracy. Experimental results show excellent trajectory tracking performance of the controller when given various desired trajectories.
KW - Fuzzy control
KW - Genetic Algorithm
KW - Pneumatic muscle actuators
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UR - http://www.scopus.com/inward/citedby.url?scp=79952450172&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2010.12.154
DO - 10.1016/j.eswa.2010.12.154
M3 - Article
AN - SCOPUS:79952450172
VL - 38
SP - 8128
EP - 8137
JO - Expert Systems with Applications
JF - Expert Systems with Applications
SN - 0957-4174
IS - 7
ER -