TY - JOUR
T1 - Forward and Inverse Predictive Model for the Trajectory Tracking Control of a Lower Limb Exoskeleton for Gait Rehabilitation
T2 - 4th Asia Pacific Conference on Manufacturing Systems and the 3rd International Manufacturing Engineering Conference, APCOMS-iMEC 2017
AU - Zakaria, M. A.
AU - Majeed, A. P.P.A.
AU - Taha, Z.
AU - Alim, M. M.
AU - Baarath, K.
N1 - Funding Information:
The authors would like to thank Universiti Malaysia Pahang for the financial aid under research grant RDU160343 and RDU170731.
Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2018/3/21
Y1 - 2018/3/21
N2 - The movement of a lower limb exoskeleton requires a reasonably accurate control method to allow for an effective gait therapy session to transpire. Trajectory tracking is a nontrivial means of passive rehabilitation technique to correct the motion of the patients' impaired limb. This paper proposes an inverse predictive model that is coupled together with the forward kinematics of the exoskeleton to estimate the behaviour of the system. A conventional PID control system is used to converge the required joint angles based on the desired input from the inverse predictive model. It was demonstrated through the present study, that the inverse predictive model is capable of meeting the trajectory demand with acceptable error tolerance. The findings further suggest the ability of the predictive model of the exoskeleton to predict a correct joint angle command to the system.
AB - The movement of a lower limb exoskeleton requires a reasonably accurate control method to allow for an effective gait therapy session to transpire. Trajectory tracking is a nontrivial means of passive rehabilitation technique to correct the motion of the patients' impaired limb. This paper proposes an inverse predictive model that is coupled together with the forward kinematics of the exoskeleton to estimate the behaviour of the system. A conventional PID control system is used to converge the required joint angles based on the desired input from the inverse predictive model. It was demonstrated through the present study, that the inverse predictive model is capable of meeting the trajectory demand with acceptable error tolerance. The findings further suggest the ability of the predictive model of the exoskeleton to predict a correct joint angle command to the system.
UR - http://www.scopus.com/inward/record.url?scp=85045618704&partnerID=8YFLogxK
U2 - 10.1088/1757-899X/319/1/012052
DO - 10.1088/1757-899X/319/1/012052
M3 - Conference article
AN - SCOPUS:85045618704
SN - 1757-8981
VL - 319
JO - IOP Conference Series: Materials Science and Engineering
JF - IOP Conference Series: Materials Science and Engineering
IS - 1
M1 - 012052
Y2 - 7 December 2017 through 8 December 2017
ER -