An iterative learning controller for a cable-driven hand rehabilitation robot

Robots are widely used to help post-stoke patients conduct rehabilitation training for the motor function recovery. Because of the existence of repetitiveness in the rehabilitation training, a high-order iterative learning controller (ILC) is proposed for one hand rehabilitation robot in this paper. A series of tracking experiments are conducted to verify the effectiveness and superiority of the proposed controller by comparing to the PID controller, the P-type ILC, and the PD-type ILC. Experimental results show that: (1) the average tracking errors of the P-type ILC and the PD-type ILC are smaller than that of the PID controller, and the steady-state performance of the PD-type ILC is better than that of the P-type ILC; and (2) compared to the PD-type ILC, the average transient performance index of the high-order ILC is decreased by 33.9%. The mean value and variance of the tracking error are decreased by 21.1% and 14.4%, respectively.

[1]  Shuguo Wang,et al.  Design and development of a hand exoskeleton for rehabilitation of hand injuries , 2014 .

[2]  Jörg Raisch,et al.  Iterative learning control of a drop foot neuroprosthesis — Generating physiological foot motion in paretic gait by automatic feedback control , 2016 .

[3]  Chris Freeman,et al.  Newton-method based iterative learning control for robot-assisted rehabilitation using FES , 2014 .

[4]  Andrew G. Alleyne,et al.  A Norm Optimal Approach to Time-Varying ILC With Application to a Multi-Axis Robotic Testbed , 2011, IEEE Transactions on Control Systems Technology.

[5]  Yong Jiang,et al.  Prevalence, Incidence, and Mortality of Stroke in China: Results from a Nationwide Population-Based Survey of 480 687 Adults , 2017, Circulation.

[6]  Kevin C. Galloway,et al.  Soft robotic glove for hand rehabilitation and task specific training , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[7]  Marcia Kilchenman O'Malley,et al.  An index finger exoskeleton with series elastic actuation for rehabilitation: Design, control and performance characterization , 2015, Int. J. Robotics Res..

[8]  Derek G. Kamper,et al.  Design and Development of the Cable Actuated Finger Exoskeleton for Hand Rehabilitation Following Stroke , 2014, IEEE/ASME Transactions on Mechatronics.

[9]  Il Hong Suh,et al.  An iterative learning control method with application to robot manipulators , 1988, IEEE J. Robotics Autom..

[10]  Long Cheng,et al.  Preliminary study on the design and control of a pneumatically-actuated hand rehabilitation device , 2017, 2017 32nd Youth Academic Annual Conference of Chinese Association of Automation (YAC).

[11]  Wei Meng,et al.  Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation , 2015 .

[12]  M. Fornage,et al.  Heart Disease and Stroke Statistics—2017 Update: A Report From the American Heart Association , 2017, Circulation.

[13]  A.G. Alleyne,et al.  A survey of iterative learning control , 2006, IEEE Control Systems.

[14]  Yan Yan,et al.  Towards Robot-Assisted Post-Stroke Hand Rehabilitation: Fugl-Meyer Gesture Recognition Using sEMG , 2017, 2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER).

[15]  T. Milner,et al.  HandCARE: A Cable-Actuated Rehabilitation System to Train Hand Function After Stroke , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[16]  Tom Oomen,et al.  Rational Basis Functions in Iterative Learning Control—With Experimental Verification on a Motion System , 2015, IEEE Transactions on Control Systems Technology.

[17]  Kevin L. Moore,et al.  Iterative Learning Control: Brief Survey and Categorization , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[18]  Christopher T. Freeman,et al.  Upper Limb Electrical Stimulation Using Input-Output Linearization and Iterative Learning Control , 2015, IEEE Transactions on Control Systems Technology.

[19]  Robert J. Wood,et al.  Soft robotic glove for combined assistance and at-home rehabilitation , 2015, Robotics Auton. Syst..

[20]  Nikos G. Tsagarakis,et al.  A novel exoskeleton robotic system for hand rehabilitation - Conceptualization to prototyping , 2014 .

[21]  Fengfeng Xi,et al.  Calibration-Based Iterative Learning Control for Path Tracking of Industrial Robots , 2015, IEEE Transactions on Industrial Electronics.

[22]  Katie Meadmore,et al.  Using Functional Electrical Stimulation Mediated by Iterative Learning Control and Robotics to Improve Arm Movement for People With Multiple Sclerosis , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[23]  Joonbum Bae,et al.  Design and control of a wearable hand exoskeleton with force-controllable and compact actuator modules , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[24]  Hong Kai Yap,et al.  Design of a wearable FMG sensing system for user intent detection during hand rehabilitation with a soft robotic glove , 2016, 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob).

[25]  Alistair C. McConnell,et al.  Robotic devices and brain-machine interfaces for hand rehabilitation post-stroke. , 2017, Journal of rehabilitation medicine.

[26]  Mario Cortese,et al.  A Powered Finger–Thumb Wearable Hand Exoskeleton With Self-Aligning Joint Axes , 2015, IEEE/ASME Transactions on Mechatronics.