The joint of the upper limb rehabilitation robot, which is designed and built in our lab, is driven by pneumatic muscles (PMs) in an opposing pair configuration. Each PM drives the robotic joint through a steel wire with a flexible sleeve and a tension device, which causes delay and various frictions as disturbances to the robotic joint system. These factors make the rehabilitation robotic joint very complex to model and control. Especially in position control, the overshoot is difficult to deal with when the directions of the friction forces are changing. The main purpose of this paper is to enhance the position control performance of the robotic joint by neuron PI and feedforward. Neuron PI control has a powerful capability of learning, adaptation, and tackling nonlinearity, and feedforward control demonstrates good performance in dealing with frictions, which cause overshoot. The results of the experiments indicate that the proposed controller, which combines neuron PI and feedforward, can enhance the performance in position control of the robotic joint, especially on dealing with overshoot.

References

1.
Volpe
,
B. T.
,
Krebs
,
H. I.
, and
Hogan
,
N.
, 2003, “
Robot-Aided Sensorimotor Training in Stroke Rehabilitation
,”
Adv. Neurol.
,
92
, pp.
429
33
.
2.
Cozens
,
J. A
, 1999, “
Robotic Assistance of an Active Upper Limb Exercise in Neurologically Impaired Patients
,”
IEEE Trans. Rehabil. Eng.
,
7
(
2
), pp.
254
256
.
3.
He
,
J.
,
Koeneman.
E. J.
,
Schultz
,
R. S.
,
Huang
,
H.
,
Wanberg
,
J.
,
Herring
,
D. E.
,
Sugar
,
T.
,
Herman
,
R.
, and
Koeneman
,
J. B.
, 2005, “
Design of a Robotic Upper Extremity Repetitive Therapy Device
,”
Proceedings of the 9th International Conference on Rehabilitation Robotics (ICORR)
,
Chicago
,
IL
, pp.
95
98.
4.
Repperger
,
D. W.
,
Phillips
,
C. A.
,
Neidhard-Doll
,
A.
,
Reynolds
,
D. B.
, and
Berlin
,
J.
, 2006, “
Actuator Design Using Biomimicry Methods and a Pneumatic Muscle System
,”
Control Eng. Pract.
,
14
(
9
), pp.
999
1009
.
5.
Repperger
,
D. W.
,
Phillips
,
C. A.
,
Johnson
,
D. C.
,
Harmon
,
R. D.
, and
Johnson
,
K.
, 1997, “
A Study of Pneumatic Muscle Technology for Possible Assistance in Mobility
,”
Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
,
5
, pp.
1884
1887.
6.
Chan
,
S. W.
,
Lilly
,
J. H.
,
Repperger
,
D. W.
, and
Berlin
,
J. E.
, 2003, “
Fuzzy PD+I Learning Control for a Pneumatic Muscle
,”
Proceedings of the 12th IEEE International Conference on Fuzzy Systems
,
1
, pp.
278
283.
7.
Tsagarakis
,
N.
, and
Calwell
,
D. G.
, 2000, “
Improved Modeling and Assessment of Pneumatic Muscle Actuators
,”
Proceedings of the IEEE International Conference on Robotics and Automation
,
4
, pp.
3641
3646
.
8.
Lilly
,
J. H.
, 2003, “
Adaptive Tracking for Pneumatic Muscle Actuators in Bicep and Tricep Configurations
,”
IEEE Trans. Neural Syst. Rehabil. Eng.
,
11
(
3
), pp.
333
339
.
9.
Nagaoka
,
T.
,
Konishi
,
Y.
, and
Ishigaki
,
H.
, 1995, “
Nonlinear Optimal Predictive Control of Rubber Artificial Muscle
,”
Proc. SPIE
,
2595
, pp.
54
61
.
10.
Ju
,
M.-S.
,
Lin
,
C.-C.
,
Lin
,
D.-H.
,
Hwang
,
I.-S.
, and
Chen
,
S.-M.
, 2005, “
A Rehabilitation Robot With Force-Position Hybrid Fuzzy Controller: Hybrid Fuzzy Control of Rehabilitation Robot
,”
IEEE Trans. Neural Syst. Rehabil. Eng.
,
13
(
3
), pp.
349
358
.
11.
Balasubramanian
,
K.
, and
Rattan
,
K. S.
, 2005, “
Trajectory Tracking Control of a Pneumatic Muscle System Using Fuzzy Logic
,”
Proceedings of the Annual Meeting of the North American Fuzzy Information Processing Society
, pp.
472
477.
12.
Chang
,
M.-K.
, and
Yuan
,
T.-H.
, 2008, “
Experimental Implementations of Adaptive Self-Organizing Fuzzy Slide Mode Control to a 3-DoF Rehabilitation Robot
,”
Proceedings of the 3rd International Conference on Innovative Computing Information and Control
, pp.
503
503
.
13.
Xu
,
G.
, and
Song
,
A.
, 2009, “
Adaptive Impedance Control Based on Dynamic Recurrent Fuzzy Neural Network for Upper-Limb Rehabilitation Robot
,”
Proceedings of the IEEE International Conference on Control and Automation
, pp.
1376
1381.
14.
Furqan
,
Iqbal
,
J.
,
Malik
,
A. N.
, and
Haider
,
W.
, 2010, “
Neural Network Based Aircraft Control
,”
Proceedings of the 2010 IEEE Student Conference on Research and Development (SCOReD 2010)
, pp.
262
266
.
15.
Thanh
,
T. D. C.
, and
Ahn
,
K. K.
, 2006, “
Nonlinear PID Control to Improve the Control Performance of 2 Axes Pneumatic Artificial Muscle Manipulator Using Neural Network
,”
Mechatronics
,
16
(
9
), pp.
577
587
.
16.
Zheng
,
C.
,
Fan
,
J.
, and
Fei
,
M.-R.
, 2009, “
PID Neural Network Control Research Based on Fuzzy Neural Network Model
,”
Proceedings of the International Conference on Computational Intelligence and software Engineering
, pp.
1
4
.
17.
Vilanova
,
R.
, 2007, “
Feedforward Control for Uncertain Systems: Internal Model Control Approach
,”
Proceedings of the IEEE Conference on Emerging Technologies and Factory Automation
, pp.
418
425
.
18.
Manuello
Bertetto
,
A.
, and
Ruggiu
,
M.
, 2004, “
Characterization and Modeling of Air Muscles
,”
Mech. Res. Commun.
,
31
(
2
), pp.
185
194
.
19.
Schindele
,
D.
, and
Aschemann
,
H.
, 2008, “
Nonlinear Model Predictive Control of a High-Speed Linear Axis Driven by Pneumatic Muscles
,”
Proceedings of the American Control Conference (ACC)
,
Seattle
,
WA
, pp.
3017
3022
.
20.
Zhang
,
J.-F.
,
Yang
,
C.-J.
,
Chen
,
Y.
,
Zhang
,
Y.
, and
Dong
,
Y.-M.
, 2008, “
Modeling and Control of a Curved Pneumatic Muscle Actuator for Wearable Elbow Exoskeleton
,”
Mechatronics
,
18
(
8
), pp.
448
457
.
21.
Yanagawa
,
S.
, and
Miki
,
I.
, 1992, “
PID Auto-Tuning Controller Using a Single Neuron for DC Servomotor
,”
Proceedings of the IEEE International Symposium on Industrial Electronics
, pp.
277
280
.
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