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: 8800
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.
[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.
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