Feed-forward friction and inertia compensation for improving backdrivability of motors

Backdrivability is an important property in applications like haptics, where force or torque is exerted by the user onto the motor. Gears cause higher friction, which results in a reduction of the backdrivability. This paper investigates how the backdriving torque can be reduced without the additional use of expensive force-torque sensors. The friction compensation uses a predetermined mapping, that adapts the motor's supporting torque depending on the measured velocity. The inertia compensation depends on the acceleration multiplied by the motor's moment of inertia. The method was objectively evaluated by using a robot. Kinetic friction compensation with inertia compensation significantly reduced the backdriving torque by 66.67 % over all median, and 23.58 % over all average values from measurements with different velocity and acceleration profiles. However, the variance and torque peaks were increased. The inertia compensation showed slight benefits in comparison to kinetic compensation alone, but not throughout all measurements.

[1]  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.

[2]  Tobias Nef,et al.  Improving backdrivability in geared rehabilitation robots , 2009, Medical & Biological Engineering & Computing.

[3]  Carlos Canudas de Wit,et al.  Adaptive Friction Compensation in Robot Manipulators: Low Velocities , 1991, Int. J. Robotics Res..

[4]  José Luis Pons Rovira,et al.  Rehabilitation Robotics: a Wearable Exo-Skeleton for Tremor Assessment and Suppression , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[5]  Rahsaan J. Holley,et al.  Development and pilot testing of HEXORR: Hand EXOskeleton Rehabilitation Robot , 2010, Journal of NeuroEngineering and Rehabilitation.

[6]  A. Inoue,et al.  A 3-D rehabilitation system for upper limbs developed in a 5-year NEDO project and its clinical testing , 2005, 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005..

[7]  Hong Z. Tan,et al.  HUMAN FACTORS FOR THE DESIGN OF FORCE-REFLECTING HAPTIC INTERFACES , 1994 .

[8]  E. Burdet,et al.  Robot-assisted rehabilitation of hand function. , 2010, Current opinion in neurology.

[9]  Antonio Frisoli,et al.  A force-feedback exoskeleton for upper-limb rehabilitation in virtual reality , 2009 .

[10]  Carlos Canudas de Wit,et al.  Friction Models and Friction Compensation , 1998, Eur. J. Control.

[11]  R. Riener,et al.  Shoulder actuation mechanisms for arm rehabilitation exoskeletons , 2008, 2008 2nd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics.

[12]  M. Indri,et al.  Friction Compensation in Robotics: an Overview , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.