TY - GEN
T1 - Adaptive Event-Triggered Motion Tracking Control Strategy for a Lower Limb Rehabilitation Exoskeleton
AU - Peng, Zhinan
AU - Cheng, Hong
AU - Huang, Rui
AU - Hu, Jiangping
AU - Luo, Rui
AU - Shi, Kaibo
AU - Ghosh, Bijoy Kumar
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - In recent years, lower limb exoskeleton has attracted extensive attention in academic and engineering research. In the rehabilitation motion training scenario, it is of concern that the exoskeleton should have the ability to control its own leg movements in order to assist a patient with natural and anthropomorphic gaits. A patient with paralysis in leg, is incapable of controlling and coordinating with the exoskeleton well enough to produce a desirable gait. Thus, a critical problem in this scenario is that the exoskeleton needs to track a desired gait in order for the patient to adopt and produce different walking patterns. To this end, this paper proposes an adaptive tracking controller for the exoskeleton. Although many adaptive control methods exist, a traditional control design utilizes time-triggered method, namely, the system data is periodically sampled and controller parameters are updated at specific time instances. To implement the update mechanism, in this paper, an aperiodically adaptive controller based on policy iteration is developed by integrating event triggering mechanism and reinforcement learning. Further, in order to achieve online learning and adaptation, an actor-critic neural network is employed, and a novel event triggered tuning law is designed to learn the controller signals. We conduct simulations and perform experiments with a lower limb rehabilitation exoskeleton robot. Experimental results demonstrate that the proposed event triggered control strategy can reduce control updates when compared with many traditional time triggered control methods, for a guaranteed control performance.
AB - In recent years, lower limb exoskeleton has attracted extensive attention in academic and engineering research. In the rehabilitation motion training scenario, it is of concern that the exoskeleton should have the ability to control its own leg movements in order to assist a patient with natural and anthropomorphic gaits. A patient with paralysis in leg, is incapable of controlling and coordinating with the exoskeleton well enough to produce a desirable gait. Thus, a critical problem in this scenario is that the exoskeleton needs to track a desired gait in order for the patient to adopt and produce different walking patterns. To this end, this paper proposes an adaptive tracking controller for the exoskeleton. Although many adaptive control methods exist, a traditional control design utilizes time-triggered method, namely, the system data is periodically sampled and controller parameters are updated at specific time instances. To implement the update mechanism, in this paper, an aperiodically adaptive controller based on policy iteration is developed by integrating event triggering mechanism and reinforcement learning. Further, in order to achieve online learning and adaptation, an actor-critic neural network is employed, and a novel event triggered tuning law is designed to learn the controller signals. We conduct simulations and perform experiments with a lower limb rehabilitation exoskeleton robot. Experimental results demonstrate that the proposed event triggered control strategy can reduce control updates when compared with many traditional time triggered control methods, for a guaranteed control performance.
KW - Event-Triggered Mechanism
KW - Lower Limb Rehabilitation Exoskeleton
KW - Motion Tracking Control
KW - Neural Network
KW - Reinforcement Learning
UR - http://www.scopus.com/inward/record.url?scp=85126042180&partnerID=8YFLogxK
U2 - 10.1109/CDC45484.2021.9682822
DO - 10.1109/CDC45484.2021.9682822
M3 - Conference contribution
AN - SCOPUS:85126042180
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 1795
EP - 1801
BT - 60th IEEE Conference on Decision and Control, CDC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 60th IEEE Conference on Decision and Control, CDC 2021
Y2 - 13 December 2021 through 17 December 2021
ER -