Autonomous Robots

, Volume 41, Issue 3, pp 725–742 | Cite as

First steps toward translating robotic walking to prostheses: a nonlinear optimization based control approach

  • Huihua ZhaoEmail author
  • Jonathan Horn
  • Jacob Reher
  • Victor Paredes
  • Aaron D. Ames


This paper presents the first steps toward successfully translating nonlinear real-time optimization based controllers from bipedal walking robots to a self-contained powered transfemoral prosthesis: AMPRO, with the goal of improving both the tracking performance and the energy efficiency of prostheses control. To achieve this goal, a novel optimization-based optimal control strategy combining control Lyapunov function based quadratic programs with impedance control is proposed. This optimization-based optimal controller is first verified on a human-like bipedal robot platform, AMBER. The results indicate improved (compared to variable impedance control) tracking performance, stability and robustness to unknown disturbances. To translate this complete methodology to a prosthetic device with an amputee, we begin by collecting reference locomotion data from a healthy subject via inertial measurement units (IMUs). This data forms the basis for an optimization problem that generates virtual constraints, i.e., parameterized trajectories, specifically for the amputee . A online optimization based controller is utilized to optimally track the resulting desired trajectories. An autonomous, state based parameterization of the trajectories is implemented through a combination of on-board sensing coupled with IMU data, thereby linking the gait progression with the actions of the user. Importantly, the proposed control law displays remarkable tracking and improved energy efficiency, outperforming PD and impedance control strategies. This is demonstrated experimentally on the prosthesis AMPRO through the implementation of a holistic sensing, algorithm and control framework, resulting in dynamic and stable prosthetic walking with a transfemoral amputee.


Transfemoral prosthesis control Real-time optimal control Hybrid systems Quadratic program Optimization problem 



This research is supported under: NSF CAREER Award CN-S-0953823 and Texas Emerging Technology Fund 11062013. This research has approval from the Institutional Review Board from Texas A&M University with IRB2014-0382F for testing with human subjects.

Supplementary material

Supplementary material 1 (mp4 9363 KB)


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Huihua Zhao
    • 1
    Email author
  • Jonathan Horn
    • 2
  • Jacob Reher
    • 1
  • Victor Paredes
    • 2
  • Aaron D. Ames
    • 1
  1. 1.Mechanical EngineeringGeorgia Institute of TechnologyAtlantaUSA
  2. 2.Mechanical Engineering Texas A&M UniversityCollege StationUSA

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