Adaptive Estimation of Human-Robot Interaction Force for Lower Limb Rehabilitation

被引：1

作者：

Liang, Xu ^{[1
,2
]}

Wang, Weiqun ^{[1
]}

Hou, Zengguang ^{[1
,2
,3
]}

Ren, Shixin ^{[1
,2
]}

Wang, Jiaxing ^{[1
,2
]}

Shi, Weiguo ^{[1
,2
]}

Su, Tingting ^{[4
]}

机构：

[1] Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China

[2] Univ Chinese Acad Sci, Beijing 100149, Peoples R China

[3] CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China

[4] North China Univ Technol, Beijing 100144, Peoples R China

来源：

NEURAL INFORMATION PROCESSING (ICONIP 2019), PT IV | 2019年 / 1142卷

关键词：

Human-robot interaction; State estimation; Rehabilitation robot; Interaction force estimation;

D O I：

10.1007/978-3-030-36808-1_59

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Human-robot interaction force information is of great significance for realizing safe, compliant and efficient rehabilitation training. In order to accurately estimate the interaction force during human-robot interaction, an adaptive method for estimation of human-robot interaction force is proposed in this paper. Firstly, the dynamics of human-robot system are modeled, which allows to establish a state space equation. Then, the interaction force is described by a polynomial function of time, and is introduced into the state space equation as a system state. Meanwhile, the Kalman filter is adopted to estimate the extended state of system online. Moreover, in order to deal with the uncertainty of system noise covariance matrix, sage-husa adaptive Kalman filter is used to correct the covariance matrices of system noises online. Finally, experiments were carried out on a lower limb rehabilitation robot, and the results show that the proposed method can precisely estimate the interaction force and also has good real-time performance.

引用

页码：540 / 547

页数：8