Model-Dependent Prosthesis Control with Interaction Force Estimation
This work addresses the challenge of reliable and efficient prosthesis control for amputees, representing an incremental improvement by building on prior stability-focused methods.
The paper tackled the problem of achieving stable and high-performance prosthesis control by developing model-dependent optimization-based controllers using rapidly exponentially stabilizing control Lyapunov functions and force estimation, which were experimentally demonstrated on hardware to provide formal stability guarantees and superior tracking compared to other methods.
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.