Quadratic programming and impedance control for transfemoral prosthesis

This paper presents a novel optimal control strategy combining control Lyapunov function (CLF) based quadratic programs with impedance control, with the goal of improving both tracking performance and the stability of controllers implemented on transfemoral prosthesis. CLF based quadratic programs have the inherent capacity to optimally track a desired trajectory. This property is used in congruence with impedance control - implemented as a feedforward term - to realize significantly small tracking errors, while simultaneously yielding bipedal walking that is both stable and robust to disturbances. Moreover, instead of experimentally validating this on human subjects, a virtual prosthesis is attached to a robotic testbed, AMBER. The authors claim that the walking of AMBER is human like and therefore form a suitable substitute to human subjects on which a prosthetic control can be tested. Based on this idea, the proposed controller was first verified in simulation, then tested on the physical robot AMBER. The results indicate improved tracking performance, stability, and robustness to unknown disturbances.

[1]  Neville Hogan,et al.  Impedance Control: An Approach to Manipulation , 1984, 1984 American Control Conference.

[2]  Aaron D. Ames,et al.  Motion primitives for human-inspired bipedal robotic locomotion: walking and stair climbing , 2012, 2012 IEEE International Conference on Robotics and Automation.

[3]  J.K. Hitt,et al.  Control of a Regenerative Braking Powered Ankle Foot Orthosis , 2007, 2007 IEEE 10th International Conference on Rehabilitation Robotics.

[4]  Aaron D. Ames,et al.  First Steps toward Automatically Generating Bipedal Robotic Walking from Human Data , 2012 .

[5]  Aaron D. Ames,et al.  From Formal Methods to Algorithmic Implementation of Human Inspired Control on Bipedal Robots , 2012, WAFR.

[6]  Charles W. Radcliffe.  A BIOMECHANICAL BASIS FOR THE DESIGN OF PROSTHETIC KNEE MECHANISMS , 1980 .

[7]  Stefan Schaal,et al.  Robot Learning From Demonstration , 1997, ICML.

[8]  Timothy Bretl,et al.  Learning impedance controller parameters for lower-limb prostheses , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  R Jiménez-Fabián,et al.  Review of control algorithms for robotic ankle systems in lower-limb orthoses, prostheses, and exoskeletons. , 2012, Medical engineering & physics.

[10]  Michael Goldfarb,et al.  Design and Control of a Powered Transfemoral Prosthesis , 2008, Int. J. Robotics Res..

[11]  Hugh M. Herr,et al.  Powered ankle-foot prosthesis to assist level-ground and stair-descent gaits , 2008, Neural Networks.

[12]  Aaron D. Ames,et al.  Speed regulation in 3D robotic walking through motion transitions between Human-Inspired partial hybrid zero dynamics , 2013, 2013 IEEE International Conference on Robotics and Automation.

[13]  Aaron D. Ames,et al.  Control lyapunov functions and hybrid zero dynamics , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[14]  Dan B. Marghitu,et al.  Rigid Body Collisions of Planar Kinematic Chains With Multiple Contact Points , 1994, Int. J. Robotics Res..

[15]  J. Weber,et al.  Design of an agonist-antagonist active knee prosthesis , 2008, 2008 2nd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics.

[16]  Michael Goldfarb,et al.  Upslope Walking With a Powered Knee and Ankle Prosthesis: Initial Results With an Amputee Subject , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[17]  Daniel Wollmann,et al.  CSCM: EXPERIMENTAL AND SIMULATION RESULTS , 2014 .

[18]  Dejan B. Popovic,et al.  Control Aspects of Active Above-Knee Prosthesis , 1991, Int. J. Man Mach. Stud..

[19]  H. Herr,et al.  Adaptive control of a variable-impedance ankle-foot orthosis to assist drop-foot gait , 2004, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[20]  Thomas Sugar,et al.  Dynamically Controlled Ankle-Foot Orthosis (DCO) with Regenerative Kinetics: Incrementally Attaining User Portability , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[21]  Aaron D. Ames,et al.  Human-inspired control of bipedal robots via control lyapunov functions and quadratic programs , 2013, HSCC '13.

[22]  Aaron D. Ames,et al.  Human‐inspired motion primitives and transitions for bipedal robotic locomotion in diverse terrain , 2014 .

[23]  Aaron D. Ames,et al.  Towards the Unification of Locomotion and Manipulation through Control Lyapunov Functions and Quadratic Programs , 2013, CPSW@CISS.

[24]  Jun Morimoto,et al.  Learning from demonstration and adaptation of biped locomotion , 2004, Robotics Auton. Syst..

[25]  Somanath Majhi,et al.  Limitations of PID controllers , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

[26]  Christine Chevallereau,et al.  3D Bipedal Robotic Walking: Models, Feedback Control, and Open Problems , 2010 .

[27]  P. Bonato,et al.  An EMG-position controlled system for an active ankle-foot prosthesis: an initial experimental study , 2005, 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005..