基于增强学习的下肢助力外骨骼虚阻抗控制

摘要

提出了一种基于增强学习的变虚阻抗控制算法,其控制器设计为一个结合人机交互模型的虚阻抗控制器.为了适应不同穿戴者所产生的交互力,采用了PI2增强学习算法对控制器中的参数进行在线学习.该控制策略在一自由度外骨骼平台和HUALEX下肢助力外骨骼上进行了实验验证,证明了所提出控制算法的有效性.%This paper presents a novel variable virtual impedance control (VVIC) strategy which can adapt HEI to different pilots with a virtual impedance controller. The controller is model-based with a virtual impedance which models HEI between the pilot and the exoskeleton. To adapt different pilots with different HEI, a reinforcement learning method based on policy improvement and path integrals (PI2) is employed to adjust and optimize parameters of virtual impedance. We demonstrate the efficiency of the proposed VVIC strategy on a single degree-of-freedom (DOF) exoskeleton platform as well as a human-powered augmentation lower exoskeleton (HUALEX) system. Experimental results indicate that the proposed VVIC strategy is able to adapt HEI to different pilots and outperforms traditional model-based control strategies in terms of interaction forces.