AEKF-based trajectory-error compensation of knee exoskeleton for human–exoskeleton interaction control

Yuepeng Zhang, Guangzhong Cao, Linglong Li, Dongfeng Diao
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Abstract

Purpose The purpose of this paper is to design a new trajectory error compensation method to improve the trajectory tracking performance and compliance of the knee exoskeleton in human–exoskeleton interaction motion. Design/methodology/approach A trajectory error compensation method based on admittance-extended Kalman filter (AEKF) error fusion for human–exoskeleton interaction control. The admittance controller is used to calculate the trajectory error adjustment through the feedback human–exoskeleton interaction force, and the actual trajectory error is obtained through the encoder feedback of exoskeleton and the designed trajectory. By using the fusion and prediction characteristics of EKF, the calculated trajectory error adjustment and the actual error are fused to obtain a new trajectory error compensation, which is feedback to the knee exoskeleton controller. This method is designed to be capable of improving the trajectory tracking performance of the knee exoskeleton and enhancing the compliance of knee exoskeleton interaction. Findings Six volunteers conducted comparative experiments on four different motion frequencies. The experimental results show that this method can effectively improve the trajectory tracking performance and compliance of the knee exoskeleton in human–exoskeleton interaction. Originality/value The AEKF method first uses the data fusion idea to fuse the estimated error with measurement errors, obtaining more accurate trajectory error compensation for the knee exoskeleton motion control. This work provides great benefits for the trajectory tracking performance and compliance of lower limb exoskeletons in human–exoskeleton interaction movements.
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基于 AEKF 的膝关节外骨骼轨迹误差补偿,用于人-外骨骼交互控制
本文旨在设计一种新的轨迹误差补偿方法,以提高膝关节外骨骼在人-外骨骼交互运动中的轨迹跟踪性能和顺应性。设计/方法/途径一种基于导纳-扩展卡尔曼滤波器(AEKF)误差融合的轨迹误差补偿方法,用于人-外骨骼交互控制。利用导纳控制器通过反馈的人-外骨骼相互作用力计算轨迹误差调整,并通过外骨骼的编码器反馈和设计的轨迹获得实际轨迹误差。利用 EKF 的融合和预测特性,将计算出的轨迹误差调整和实际误差融合,得到新的轨迹误差补偿,反馈给膝关节外骨骼控制器。研究结果六名志愿者对四种不同运动频率进行了对比实验。实验结果表明,该方法能有效改善人-外骨骼交互中膝关节外骨骼的轨迹跟踪性能和顺应性。原创性/价值AEKF方法首先利用数据融合思想,将估计误差与测量误差进行融合,为膝关节外骨骼运动控制获得了更精确的轨迹误差补偿。这项工作为下肢外骨骼在人机交互运动中的轨迹跟踪性能和顺应性提供了极大的帮助。
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