Optimal Walking Assistance Control of Lower Limb Exoskeleton Using Adaptive Learning Approach

Zhinan Peng, Rui Luo, Jiangping Hu, Bijoy Kumar Ghosh, Sing Kiong Nguang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


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.

Original languageEnglish
Title of host publicationProceedings of the 39th Chinese Control Conference, CCC 2020
EditorsJun Fu, Jian Sun
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)9789881563903
StatePublished - Jul 2020
Event39th Chinese Control Conference, CCC 2020 - Shenyang, China
Duration: Jul 27 2020Jul 29 2020

Publication series

NameChinese Control Conference, CCC
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927


Conference39th Chinese Control Conference, CCC 2020


  • Actor-critic network
  • Lower limb exoskeleton
  • Optimal walking assistance control
  • Reinforcement learning


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