Full Text:   <1856>

Summary:  <512>

CLC number: TP242.3

On-line Access: 2014-04-10

Received: 2013-09-16

Revision Accepted: 2013-12-29

Crosschecked: 2014-03-17

Cited: 5

Clicked: 4051

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
1. Reference List
Open peer comments

Journal of Zhejiang University SCIENCE C 2014 Vol.15 No.4 P.275-283

http://doi.org/10.1631/jzus.C1300259


Human-machine interaction force control: using a model-referenced adaptive impedance device to control an index finger exoskeleton


Author(s):  Qian Bi, Can-jun Yang

Affiliation(s):  State Key Laboratory of Fluid Power Transmission and Control, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   biqianmyself@zju.edu.cn, ycj@zju.edu.cn

Key Words:  Interaction force, Adaptive control, Exoskeleton, Human-machine interaction (HMI), Impedance


Qian Bi, Can-jun Yang. Human-machine interaction force control: using a model-referenced adaptive impedance device to control an index finger exoskeleton[J]. Journal of Zhejiang University Science C, 2014, 15(4): 275-283.

@article{title="Human-machine interaction force control: using a model-referenced adaptive impedance device to control an index finger exoskeleton",
author="Qian Bi, Can-jun Yang",
journal="Journal of Zhejiang University Science C",
volume="15",
number="4",
pages="275-283",
year="2014",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1300259"
}

%0 Journal Article
%T Human-machine interaction force control: using a model-referenced adaptive impedance device to control an index finger exoskeleton
%A Qian Bi
%A Can-jun Yang
%J Journal of Zhejiang University SCIENCE C
%V 15
%N 4
%P 275-283
%@ 1869-1951
%D 2014
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1300259

TY - JOUR
T1 - Human-machine interaction force control: using a model-referenced adaptive impedance device to control an index finger exoskeleton
A1 - Qian Bi
A1 - Can-jun Yang
J0 - Journal of Zhejiang University Science C
VL - 15
IS - 4
SP - 275
EP - 283
%@ 1869-1951
Y1 - 2014
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1300259


Abstract: 
exoskeleton robots and their control methods have been extensively developed to aid post-stroke rehabilitation. Most of the existing methods using linear controllers are designed for position control and are not suitable for human-machine interaction (HMI) force control, as the interaction system between the human body and exoskeleton is uncertain and nonlinear. We present an approach for HMI force control via model reference adaptive impedance control (MRAIC) to solve this problem in case of index finger exoskeleton control. First, a dynamic HMI model, which is based on a position control inner loop, is formulated. Second, the theoretical MRAC framework is implemented in the control system. Then, the adaptive controllers are designed according to the Lyapunov stability theory. To verify the performance of the proposed method, we compare it with a proportional-integral-derivative (PID) method in the time domain with real experiments and in the frequency domain with simulations. The results illustrate the effectiveness and robustness of the proposed method in solving the nonlinear HMI force control problem in hand exoskeleton.

人机交互力控制:针对食指外骨骼控制的模型参考自适应阻抗控制策略

研究目的:在手部外骨骼的力控制问题中,由于动作变化引起的模型参数变化以及人手自身非线性的力学特性,常规的线性控制器难以很好地解决外骨骼和人手之间的交互力控制问题。本文提出将阻抗控制与模型参考自适应控制方法结合,即模型参考自适应阻抗控制(MRAIC),以解决上述困难,并进行仿真分析和实验验证。
创新要点:将阻抗控制用于外骨骼人机交互力控制,并与模型参考自适应控制结合,推导出相应的自适应控制律,从而解决动作变化引起的模型参数变化以及人手自身力学阻抗特性的非线性问题。
方法提亮:采用阻抗控制进行力控制,不仅仅控制接触力,也控制力和位移之间的动态关系,更适合于人机交互界面;模型参考自适应控制使得非线性难题得以化解,并且模型参考提升了系统整体性能。
重要结论:方波跟踪实验和正弦跟踪实验表明,与PID线性控制器相比,MRAIC控制器具有更好的稳定性,响应更快;频域仿真分析发现,MRAIC控制器在0.1~5 Hz频段具有较好品质,这与人体动作的常规频率范围相符。

关键词:交互力;自适应控制;外骨骼;人机交互;阻抗

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Azzurra, C., Nicola, V., Francesco, G., et al., 2012. Mechatronic design and characterization of the index finger module of a hand exoskeleton for post-stroke rehabilitation. IEEE/ASME Trans. Mechatron., 17(5):884-894.

[2]Bi, Q., Yang, C.J., Deng, X.L., et al., 2013. Contacting mechanical impedance of human finger based on uncertain system. IEEE/ASME Int. Conf. on Advanced Intelligent Mechatronics, p.1619-1624.

[3]Fang, H.G., Xie, Z.W., Liu, H., et al., 2009. An exoskeleton force feedback master finger distinguishing contact and non-contact mode. IEEE/ASME Int. Conf. on Advanced Intelligent Mechatronics, p.1059-1064.

[4]Hogan, N., 1985. Impedance control: an approach to manipulation: part I—theory. J. Dynam. Syst. Meas. Contr., 107(1):1-7.

[5]Huo, W.G., Huang, J., Wang, Y.J., et al., 2011. Control of upper-limb power-assist exoskeleton based on motion intention recognition. Int. Conf. on Robotics and Automation, p.2243-2248.

[6]Kamal, H.S., Hamid, M., Farrokh, J.S., 2010. Model reference adaptive control design for a teleoperation system with output prediction. J. Intell. Robot. Syst., 59:319-339.

[7]Kamnik, R., Matko, D., Bajd, T., 1998. Application of model reference adaptive control to industrial robot impedance control. J. Intell. Robot. Syst., 22:153-163.

[8]Kiguchi, K., Hayashi, Y., 2012. An EMG-based control for an upper-limb power-assist exoskeleton robot. IEEE Trans. Syst. Man. Cybern. B, 42:1064-1071.

[9]Nakagawara, S., Kajimoto, H., Kawakami, N., et al., 2005. An encounter-type multi-fingered master hand using circuitous joints. Proc. IEEE Int. Conf. on Robotics and Automation, p.2667-2672.

[10]Nicosia, S., Tomei, P., 1984. Model reference adaptive control algorithms for industrial robots. Automatica, 20(5): 635-644.

[11]Pang, Z.H., Chui, H., 2009. System Identification and Adaptive Control. Beijing University of Aeronautics and Astronautics Press, Beijing, p.78-80 (in Chinese).

[12]Polotto, A., Modulo, F., Flumian, F., et al., 2012. Index finger rehabilitation/assistive device. 4th IEEE RAS/EMBS Int. Conf. on Biomedical Robotics and Biomechatronics, p.1518-1523.

[13]Prange, G.B., Jannink M.J.A., Groothuis-Oudshoorn, C.G.M., et al., 2006. Systematic review of the effect of robot-aided therapy on recovery of the hemiparetic arm after stroke. J. Rehabil. Res. Devel., 43(2):171-183.

[14]Schabowsky, C.N., Godfrey, S.B., Holley, R.J., et al., 2010. Development and pilot testing of HEXORR: Hand EXOskeleton Rehabilitation Robot. J. NeuroEng. Rehabil., 7:36.

[15]Seraji, H., Colbaugh, R., 1997. Force tracking in impedance control. Int. J. Robot. Res., 16(1):97-117.

[16]Takahashi, C.D., Der-Yeghiaian, L., Le, V., et al., 2008. Robot-based handmotor therapy after stroke. Brain, 131(2):425-437.

[17]Tjahyono, A.P., Aw, K.C., Devaraj, H., et al., 2013. A five-fingered hand exoskeleton driven by pneumatic artificial muscles with novel polypyrrole sensors. Ind. Robot Int. J., 40(3):251-260.

[18]Ueki, S., Kawasakia, H., Itoa, S., et al., 2012. Development of a hand-assist robot with multi-degrees-of-freedom for rehabilitation therapy. IEEE/ASME Trans. Mechatron., 17(1):136-146.

[19]Wege, A., Kondak, K., Hommel, G., 2006. Force control strategy or a hand exoskeleton based on sliding mode position control. Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, p.4615-4620.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE