Robust Iterative Feedback Tuning Control of a Compliant Rehabilitation Robot for Repetitive Ankle Training

Robot-assisted rehabilitation offers benefits, such as repetitive, intensive, and task-specific training, as compared to traditional manual manipulation performed by physiotherapists. In this paper, a robust iterative feedback tuning (IFT) technique for repetitive training control of a compliant parallel ankle rehabilitation robot is presented. The robot employs four parallel intrinsically compliant pneumatic muscle actuators that mimic skeletal muscles for ankle's motion training. A multiple degrees-of-freedom normalized IFT technique is proposed to increase the controller robustness by obtaining an optimal value for the weighting factor and offering a method with learning capacity to achieve an optimum of the controller parameters. Experiments with human participants were conducted to investigate the robustness as well as to validate the performance of the proposed IFT technique. Results show that the normalized IFT scheme will achieve a better and better tracking performance during the robot repetitive control and provides more robustness to the system by adapting to various situations in robotic rehabilitation.

[1]  Claudia-Adina Dragos,et al.  Data-Driven Reference Trajectory Tracking Algorithm and Experimental Validation , 2013, IEEE Transactions on Industrial Informatics.

[2]  C. Phillips,et al.  Modeling the Dynamic Characteristics of Pneumatic Muscle , 2003, Annals of Biomedical Engineering.

[3]  Mingming Zhang,et al.  Effectiveness of robot-assisted therapy on ankle rehabilitation – a systematic review , 2013, Journal of NeuroEngineering and Rehabilitation.

[4]  Eric Rogers,et al.  Functional electrical stimulation mediated by iterative learning control and 3D robotics reduces motor impairment in chronic stroke , 2011, Journal of NeuroEngineering and Rehabilitation.

[5]  Judith E. Deutsch,et al.  A Stewart Platform-Based System for Ankle Telerehabilitation , 2001, Auton. Robots.

[6]  Danwei Wang,et al.  A Data-Driven Iterative Feedback Tuning Approach of ALINEA for Freeway Traffic Ramp Metering With PARAMICS Simulations , 2013, IEEE Transactions on Industrial Informatics.

[7]  S. Kissling,et al.  Application of iterative feedback tuning (IFT) to speed and position control of a servo drive , 2009 .

[8]  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).

[9]  T. Fukuda,et al.  Iterative feedback tuning of controllers for a two-mass-spring system with friction , 2003 .

[10]  Ali Utku Pehlivan,et al.  A Subject-Adaptive Controller for Wrist Robotic Rehabilitation , 2015, IEEE/ASME Transactions on Mechatronics.

[11]  Svante Gunnarsson,et al.  Iterative feedback tuning: theory and applications , 1998 .

[12]  Mario Cortese,et al.  A Mechatronic System for Robot-Mediated Hand Telerehabilitation , 2015, IEEE/ASME Transactions on Mechatronics.

[13]  Chao Deng,et al.  Optimal Normalized Weighting Factor in Iterative Feedback Tuning of Step Input Responses , 2014 .

[14]  E. Rogers,et al.  Iterative Learning Control in Health Care: Electrical Stimulation and Robotic-Assisted Upper-Limb Stroke Rehabilitation , 2012, IEEE Control Systems.

[15]  S. Gunnarsson,et al.  A convergent iterative restricted complexity control design scheme , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[16]  Shahid Hussain,et al.  An Adaptive Wearable Parallel Robot for the Treatment of Ankle Injuries , 2014, IEEE/ASME Transactions on Mechatronics.

[17]  H. Krebs,et al.  Effects of Robot-Assisted Therapy on Upper Limb Recovery After Stroke: A Systematic Review , 2008, Neurorehabilitation and neural repair.

[18]  Jian S. Dai,et al.  Control Strategies for Patient-Assisted Training Using the Ankle Rehabilitation Robot (ARBOT) , 2013, IEEE/ASME Transactions on Mechatronics.

[19]  Zhuo Wang,et al.  From model-based control to data-driven control: Survey, classification and perspective , 2013, Inf. Sci..

[20]  Marcel François Heertjes,et al.  Constrained Iterative Feedback Tuning for Robust Control of a Wafer Stage System , 2016, IEEE Transactions on Control Systems Technology.

[21]  Magnus Mossberg,et al.  Iterative feedback tuning of PID parameters: comparison with classical tuning rules , 2003 .

[22]  Shahid Hussain,et al.  Robust Nonlinear Control of an Intrinsically Compliant Robotic Gait Training Orthosis , 2013, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[23]  Mingming Zhang,et al.  A Robot-Driven Computational Model for Estimating Passive Ankle Torque With Subject-Specific Adaptation , 2016, IEEE Transactions on Biomedical Engineering.

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

[25]  T Claire Davies,et al.  A novel assessment technique for measuring ankle orientation and stiffness. , 2015, Journal of biomechanics.

[26]  R. Riener,et al.  Iterative Learning Synchronization of Robotic Rehabilitation Tasks , 2007, 2007 IEEE 10th International Conference on Rehabilitation Robotics.

[27]  N. K. Poulsen,et al.  Improving Convergence of Iterative Feedback Tuning , 2009 .

[28]  Aiguo Song,et al.  Adaptive Impedance Control for Upper-Limb Rehabilitation Robot Using Evolutionary Dynamic Recurrent Fuzzy Neural Network , 2011, J. Intell. Robotic Syst..

[29]  Shengquan Xie,et al.  MIMO Actuator Force Control of a Parallel Robot for Ankle Rehabilitation , 2013 .

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

[31]  F. De Bruyne Iterative feedback tuning for internal model controllers , 2003 .

[32]  Jiping He,et al.  RUPERT: An exoskeleton robot for assisting rehabilitation of arm functions , 2008, 2008 Virtual Rehabilitation.

[33]  Håkan Hjalmarsson,et al.  Iterative feedback tuning—an overview , 2002 .

[34]  Kazuto Kora,et al.  Automatic tuning with feedforward compensation of the HuREx rehabilitation system , 2014, 2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

[35]  Cz Lu Advanced Iterative Learning Algorithm for Control of Rehabilitation Robots , 2015 .

[36]  Qingsong Ai,et al.  Bio-Inspired Design and Iterative Feedback Tuning Control of a Wearable Ankle Rehabilitation Robot , 2016, J. Comput. Inf. Sci. Eng..

[37]  A. J. McDaid,et al.  Control of IPMC Actuators for Microfluidics With Adaptive “Online” Iterative Feedback Tuning , 2012, IEEE/ASME Transactions on Mechatronics.