TY - GEN
T1 - Optimal Walking Assistance Control of Lower Limb Exoskeleton Using Adaptive Learning Approach
AU - Peng, Zhinan
AU - Luo, Rui
AU - Hu, Jiangping
AU - Kumar Ghosh, Bijoy
AU - Kiong Nguang, Sing
N1 - Funding Information:
This work is partially supported by the Sichuan Science and Technology Program under Grant 2020YFSY0012, the National Natural Science Foundation of China under Grants No. 61473061, No. 61104104, and the Program for New Century Excellent Talents in University under Grant No. NCET-13-0091.
Publisher Copyright:
© 2020 Technical Committee on Control Theory, Chinese Association of Automation.
PY - 2020/7
Y1 - 2020/7
N2 - In this paper, an adaptive reinforcement learning (RL) based controller is developed to solve assistance control problem for Lower Limb Exoskeleton (LLE) to aid hemiplegic individuals in walking. The communication interaction relation between both two lower-limbs and patient's unaffected leg is modelled in the context of leader-follower (LF) framework. The walking assistance control problem of LLE with patients is converted to optimal control problem. To handle the optimal control problem, a discounted cost function is designed in terms of the local tracking error, and then a policy iteration algorithm (PI) is proposed to generate an optimal control policy, followed by the convergence analysis of the presented algorithm. Further, in order to improve the adaption of the controller to different patients, on the basis of the PI algorithm, an actor-critic-based neural network (AC/NN) architecture is introduced to implement the presented control method in an online-learning fashion. Finally, simulation scenarios are established to test the effectiveness of the proposed walking assistance control approaches.
AB - In this paper, an adaptive reinforcement learning (RL) based controller is developed to solve assistance control problem for Lower Limb Exoskeleton (LLE) to aid hemiplegic individuals in walking. The communication interaction relation between both two lower-limbs and patient's unaffected leg is modelled in the context of leader-follower (LF) framework. The walking assistance control problem of LLE with patients is converted to optimal control problem. To handle the optimal control problem, a discounted cost function is designed in terms of the local tracking error, and then a policy iteration algorithm (PI) is proposed to generate an optimal control policy, followed by the convergence analysis of the presented algorithm. Further, in order to improve the adaption of the controller to different patients, on the basis of the PI algorithm, an actor-critic-based neural network (AC/NN) architecture is introduced to implement the presented control method in an online-learning fashion. Finally, simulation scenarios are established to test the effectiveness of the proposed walking assistance control approaches.
KW - Actor-critic network
KW - Lower limb exoskeleton
KW - Optimal walking assistance control
KW - Reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85091401292&partnerID=8YFLogxK
U2 - 10.23919/CCC50068.2020.9189515
DO - 10.23919/CCC50068.2020.9189515
M3 - Conference contribution
AN - SCOPUS:85091401292
T3 - Chinese Control Conference, CCC
SP - 4825
EP - 4830
BT - Proceedings of the 39th Chinese Control Conference, CCC 2020
A2 - Fu, Jun
A2 - Sun, Jian
PB - IEEE Computer Society
Y2 - 27 July 2020 through 29 July 2020
ER -