Abstract
Rehabilitation robot can assist post-stroke patients during rehabilitation therapy. The movement control of the robot plays an important role in the process of functional recovery training. Owing to the change of the arm impedance of the post-stroke patient in the passive recovery training, the conventional movement control based on PI controller is difficult to produce smooth movement to track the designed trajectory set by the rehabilitation therapist. In this paper, we model the dynamics of post-stroke patient arm as an impedance model, and an adaptive control scheme which consists of an adaptive PI control algorithm and a damp control algorithm is proposed to control the rehabilitation robot moving along predefined trajectories stably and smoothly. An equivalent 2-port circuit of the rehabilitation robot and human arm is built, and passivity theory of circuit is used to analyze the stability and smoothness performance of the robot. A slide least mean square with adaptive window (SLMS-AW) method is presented to online estimate the parameters of the arm impedance model, which is used for adjusting the gains of PI-damp controller. In this paper, the Barrett WAM Arm manipulator is used as the main hardware platform for the functional recovery training of the post-stroke patient. Passive recovery training has been implemented on the WAM Arm. Experimental results demonstrate the effectiveness and potential of the proposed adaptive control strategies.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Homepage of Chinese Ministry of Health. www.moh.gov.cn/public/
Michel VEG, Driessen BJF, Michel D et al (2005) A Motorized gravity compensation mechanism used for Active Rehabilitation of upper limbs. In: Proceedings of the 2005 IEEE 9th international conference on rehabilitation robotics, Chicago, pp 152–155
Burgar CG, Lum PS, Shor PC et al (2000) Development of robots for rehabilitation therapy: the Polo Altova/Stanford experience. J Rehabil Res Dev 37(6):663–673
Reinkensmeyer DJ, Kahn LE, Arerbuch M et al (2000) Understanding and treating arm movement impairment after chronic brain injury: Progress with the ARM Guide. J Rehabil Res Dev 37(6):653–662
Krebs HI, Volpe BT, Aisen ML, Hogan N (2000) Increasing productivity and quality of care: robot-aided neuro-rehabilitation. J Rehabil Res Dev 37(6):639–652
Zhang YB, Wang ZX, Ji LH (2005) The clinical application of the upper extremity compound movements rehabilitation training robot. In: Proceedings of the 2005 IEEE 9th international conference on rehabilitation robotics, Chicago, pp 91–94
Krebs HI, Hogan N, Aisen ML, Volpe BT (1998) Robot-aided neurorehabilitation. IEEE Trans On Rehab Eng 6:75–87
Kahn LE, Rymer WZ, Reinkensmeyer DJ (2004) Adaptive assistance for guided force training in chronic stroke. In: Proceedings of the 26th annual international conference of the IEEE EMBS, San Francisco, pp 2722–2725
Lindberg P, Schmitz C, Forssberg H (2004) Engardt: Effects of passive-active movement training on upper limb motor function and cortical activation in chronic patients with stroke: a pilot study. J Rehabil Med 36:117–123
O’Malley MK, Sledd A, Gupta A (2006) The rice wrist: a distal upper extremity rehabilitation robot for stroke therapy. In: Proceedings IMECE, Chicago, pp 1–10
Duygun E, Vishnu M, Nilanjan S et al (2005) A new control approach to robot assisted rehabilitation. In: Proceedings of the 2005 IEEE 9th international conference on rehabilitation robotics, Chicago, pp 323–328
Xu GZ, Song AG, Li HJ (2011) Control system design for an upper-limb rehabilitation robot. Adv Rob 25(1):229–251
Xu GZ, Song AG, Li HJ (2011) Adaptive impedance control for upper-limb rehabilitation robot using evolutionary dynamic recurrent fuzzy neural networks. J Intell Rob Syst 62(2):501–525
Anderson RJ, Spong MW (1989) Bilateral control of teleoperators with time delay. IEEE Trans Autom Control 34(5):494–501
Li HJ, Song AG (2007) Virtual-environment modeling and correction for force-reflecting teleoperation with time delay. IEEE Trans Ind Elec 54(2):1227–1233
Lin CC, Ju MS, Lin CW et al (2003) The pendulum test for evaluating spasticity of the elbow joint. Arch Phys Med Rehabil 84:69–74
Noritsugu T, Tanaka T (1997) Application of rubber artificial muscle manipulator as a rehabilitation robot. IEEE/ASME Trans Mechatronics 2(4):259–267
Song AG, Wu J, Qin G, Huang WY (2007) A novel self-decoupled four degree-of-freedom wrist force/torque sensor. Measurement 40(9): 883–889
Acknowledgments
This work was supported by the National Natural Science Foundation of China (No. 61272379, 61104206), the Natural Science Foundation of JiangSu Province (BK2010063), and Foundation of ChangZhou (CE20120085).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Song, A., Pan, L., Xu, G., Li, H. (2014). Impedance Identification and Adaptive Control of Rehabilitation Robot for Upper-Limb Passive Training. In: Sun, F., Li, T., Li, H. (eds) Foundations and Applications of Intelligent Systems. Advances in Intelligent Systems and Computing, vol 213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37829-4_58
Download citation
DOI: https://doi.org/10.1007/978-3-642-37829-4_58
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37828-7
Online ISBN: 978-3-642-37829-4
eBook Packages: EngineeringEngineering (R0)