Regulating speed and generating large speed transitions in a neuromuscular human walking model

Abstract

Although current humanoid controllers can rely
on inverse kinematics or dynamics of the full humanoid system,
powered prosthetic legs or assistive devices cannot, because they
do not have access to the full states of the human system. This
limitation creates the need for alternative control strategies.
One strategy is to embed fundamental knowledge about legged
dynamics and control in local feedback. In a previous paper,
we have developed a control model of human locomotion which
relies mostly on local feedback. The model can robustly walk at
normal walking speeds. Here we extend this model to adapt to
a wide range of walking speeds and to generate corresponding
speed transitions. We use optimization of the model’s control
parameters and find key parameters responsible for steady
walking between 0.8ms−1 and 1.8ms−1 , covering the range of
speed at which humans normally walk. Using these parameters,
we demonstrate speed transitions between slow and fast walking. In addition, we discuss how the speed-dependent changes
of the identified control parameters connect to biped walking
dynamics, and suggest how these changes can be integrated in
local feedback control.