TY - GEN
T1 - Artificial Neural Network based dynamic modelling of indigenous pneumatic muscle actuators
AU - Jamwal, Prashant K.
AU - Xie, Sheng Quan
PY - 2012/10/19
Y1 - 2012/10/19
N2 - Robots are increasingly becoming popular medical devices, helping surgeons and practitioners as surgical, rehabilitation or service robots. Robots have been proved commendable in working together with patients and practitioners to achieve the common goal of well-being. Apart from high power to weight ratio and accuracy, robots are expected to be safe and flexible. At The University of Auckland we had earlier developed robots for ankle joint and lower limb rehabilitation using McKibben pneumatic muscle actuators (PMA) which were safe and flexible. However, these actuators had larger response time and hysteresis apart from compromised actuation limits. As a result of our further research we have been able to develop inhouse pleated PMA (PPMA) in our laboratory which show improved response time with low hysteresis. The newly developed actuators have larger actuation as well. In order to cope with the non-linear and transient nature of these actuators, this paper further proposes a new Artificial Neural Network (ANN) based approach. To optimize ANN model parameters a hybrid approach combing back propagation (BP) algorithm with Modified Genetic Algorithm (MGA) is developed. Results show that the hybrid approach is able to model the PPMA behaviour closely.
AB - Robots are increasingly becoming popular medical devices, helping surgeons and practitioners as surgical, rehabilitation or service robots. Robots have been proved commendable in working together with patients and practitioners to achieve the common goal of well-being. Apart from high power to weight ratio and accuracy, robots are expected to be safe and flexible. At The University of Auckland we had earlier developed robots for ankle joint and lower limb rehabilitation using McKibben pneumatic muscle actuators (PMA) which were safe and flexible. However, these actuators had larger response time and hysteresis apart from compromised actuation limits. As a result of our further research we have been able to develop inhouse pleated PMA (PPMA) in our laboratory which show improved response time with low hysteresis. The newly developed actuators have larger actuation as well. In order to cope with the non-linear and transient nature of these actuators, this paper further proposes a new Artificial Neural Network (ANN) based approach. To optimize ANN model parameters a hybrid approach combing back propagation (BP) algorithm with Modified Genetic Algorithm (MGA) is developed. Results show that the hybrid approach is able to model the PPMA behaviour closely.
KW - Artificial Neural Networks
KW - Dynamic Modelling
KW - Pleated Pneumatic Muscle Actuators
UR - http://www.scopus.com/inward/record.url?scp=84867453551&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84867453551&partnerID=8YFLogxK
U2 - 10.1109/MESA.2012.6275560
DO - 10.1109/MESA.2012.6275560
M3 - Conference contribution
AN - SCOPUS:84867453551
SN - 9781467323475
T3 - Proceedings of 2012 8th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, MESA 2012
SP - 190
EP - 195
BT - Proceedings of 2012 8th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, MESA 2012
T2 - 2012 8th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, MESA 2012
Y2 - 8 July 2012 through 10 July 2012
ER -