TY - GEN
T1 - Research on trajectory tracking control of lower extremity exoskeleton robot
AU - Zhang, Pengfei
AU - Guo, Yifeng
AU - Li, Jian
AU - Gao, Xueshan
AU - Li, Simin
AU - Luo, Dingji
AU - Miao, Mingda
AU - Cong, Peichao
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - This paper proposes a trajectory tracking control algorithm for lower extremity exoskeleton robot, which simplifies the lower extremity exoskeleton into a two degree of freedom linkage mechanism and uses Lagrangian method to model the dynamics of the linkage mechanism. Automatic adjustment of PID parameters is achieved by selecting membership functions, setting fuzzy rules and defuzzification. The Simulink fuzzy control block diagram is constructed to compare the tracking effect of the classical PID algorithm and the fuzzy adaptive PID algorithm on the input curve. Through the simulation curve, compared with the traditional PID, the fuzzy adaptive PID has the advantages of small overshoot, fast response and good stability. Finally, through the exoskeleton fuzzy PID hip sinusoidal curve experiment, the maximum error between the experimental data and the simulation results is 5%, which basically realizes the effective tracking of the trajectory.
AB - This paper proposes a trajectory tracking control algorithm for lower extremity exoskeleton robot, which simplifies the lower extremity exoskeleton into a two degree of freedom linkage mechanism and uses Lagrangian method to model the dynamics of the linkage mechanism. Automatic adjustment of PID parameters is achieved by selecting membership functions, setting fuzzy rules and defuzzification. The Simulink fuzzy control block diagram is constructed to compare the tracking effect of the classical PID algorithm and the fuzzy adaptive PID algorithm on the input curve. Through the simulation curve, compared with the traditional PID, the fuzzy adaptive PID has the advantages of small overshoot, fast response and good stability. Finally, through the exoskeleton fuzzy PID hip sinusoidal curve experiment, the maximum error between the experimental data and the simulation results is 5%, which basically realizes the effective tracking of the trajectory.
UR - http://www.scopus.com/inward/record.url?scp=85073223188&partnerID=8YFLogxK
U2 - 10.1109/ICARM.2019.8833777
DO - 10.1109/ICARM.2019.8833777
M3 - Conference contribution
AN - SCOPUS:85073223188
T3 - 2019 4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019
SP - 481
EP - 485
BT - 2019 4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019
Y2 - 3 July 2019 through 5 July 2019
ER -