Model-Dependent Prosthesis Control with Interaction Force Estimation

TL;DR

This paper introduces a novel model-dependent control approach for prostheses that uses force estimation and control Lyapunov functions to ensure stability, improve tracking, and leverage natural dynamics, demonstrated on hardware.

Contribution

It combines RES-CLFs with force estimation to create stable, model-based controllers for prostheses, advancing beyond heuristic and model-independent methods.

Findings

Achieves formal stability guarantees in hardware implementation

Provides superior trajectory tracking compared to existing methods

Utilizes natural system dynamics for improved control performance

Abstract

Current prosthesis control methods are primarily model-independent - lacking formal guarantees of stability, relying largely on heuristic tuning parameters for good performance, and neglecting use of the natural dynamics of the system. Model-dependence for prosthesis controllers is difficult to achieve due to the unknown human dynamics. We build upon previous work which synthesized provably stable prosthesis walking through the use of rapidly exponentially stabilizing control Lyapunov functions (RES-CLFs). This paper utilizes RES-CLFs together with force estimation to construct model-based optimization-based controllers for the prosthesis. These are experimentally realized on hardware with onboard sensing and computation. This hardware demonstration has formal guarantees of stability, utilizes the natural dynamics of the system, and achieves superior tracking to other prosthesis trajectory tracking control methods.