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Model-Based Lower-Limb Powered Prosthesis Control: Developing and Realizing Nonlinear Subsystem Control Methods for Generalizable Prosthesis Control


Gehlhar, Rachel (2023) Model-Based Lower-Limb Powered Prosthesis Control: Developing and Realizing Nonlinear Subsystem Control Methods for Generalizable Prosthesis Control. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/6724-6e14.


While there are over 600,000 lower-limb amputees in the US, commercially available prostheses remain limited to mostly passive devices. People that walk with a passive prosthesis experience an increase in energy expenditure, a decrease in comfortable walking speed, and gait asymmetry which leads to degenerative conditions. To address these limitations, researchers have developed powered prostheses with the aim of replicating the net positive energy biological limbs supply to humans in walking. These active devices have been shown to decrease users' metabolic cost and increase their comfortable walking speed. However, the control methods to achieve these results typically require hours of heuristic tuning for every user and every behavior. This motivates developing more formal prosthesis control methods that generalize between users.

Formal nonlinear control methods have been developed to realize energy efficient, human-like walking on bipedal robots. These model-based approaches provide a systematic approach to generate and realize provably stable walking gaits. However, these methods cannot be directly applied to prostheses since they depend on a dynamic model of the entire system, and in the case of the prosthesis, the human dynamics are unknown.

To address this challenge, we develop a theoretical framework to translate model-based bipedal control methods to prostheses with the aim of realizing a generalizable prosthesis control method. We separate the prosthesis subsystem from the remaining human portion of the system and model the human's impact on the prosthesis dynamics with a measure of the interaction forces between the human and the prosthesis. We theoretically prove that a model-based controller developed in this separable subsystem framework is equivalent to one developed with knowledge of the full-order human-prosthesis system. With control Lyapunov functions, we develop a wider class of subsystem controllers that solely depend on local information but provide full-order system guarantees, even in the presence of force estimate errors. This work bridges the gap between bipedal control methods and prostheses, allowing us to leverage the benefits of model-based approaches on prostheses.

We demonstrated a controller of this class through an online optimization-based approach on a powered knee-ankle prosthesis, realizing the first model-dependent lower-limb prosthesis controller that accounts for the interaction force between the human and the prosthesis. For a first pass, a force-estimation method was used that yields improved tracking of the desired trajectories over model-independent prosthesis control methods. Then, we incorporated a load cell into the prosthesis platform at the human-prosthesis attachment point to measure the interaction forces, and an inertial measurement to measure the rotation and velocity of the human's thigh. These two sensors completed the prosthesis dynamics model. A pressure sensor incorporated into the prosthesis' shoe measured the ground reaction forces, enabling the prosthesis to respond to its real-world environment, proving robust to 4 different terrains. We extended this controller to a multi-domain hybrid system approach to model the changing contact points occurring in human heel-toe roll. By allowing the prosthesis to sense the human's large varying dynamic load and respond accordingly, this model-based prosthesis controller emulated subject-specific human kinematic trends on a knee-ankle prosthesis for two subjects with no tuning in between, suggesting this approach could yield a method that generalizes between users.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:prostheses, nonlinear control, robotics
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Mechanical Engineering
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Ames, Aaron D.
Thesis Committee:
  • Burdick, Joel Wakeman (chair)
  • Murray, Richard M.
  • Dickinson, Michael H.
  • Ames, Aaron D.
Defense Date:2 December 2022
Record Number:CaltechTHESIS:01072023-214003146
Persistent URL:
Related URLs:
URLURL TypeDescription's personal research website. adapted for Chapter 8 adapted for Chapter 6 adapted for Chapter 7 adapted for Chapter 5
Gehlhar, Rachel0000-0002-4838-8839
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:15082
Deposited By: Rachel Gehlhar
Deposited On:17 Jan 2023 16:56
Last Modified:24 Jan 2023 16:22

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