  This paper presents a neural-network based adaptive feedback control
structure to regulate the velocity of 3D bipedal robots under dynamics
uncertainties. Existing Hybrid Zero Dynamics (HZD)-based controllers regulate
velocity through the implementation of heuristic regulators that do not
consider model and environmental uncertainties, which may significantly affect
the tracking performance of the controllers. In this paper, we address the
uncertainties in the robot dynamics from the perspective of the reduced
dimensional representation of virtual constraints and propose the integration
of an adaptive neural network-based controller to regulate the robot velocity
in the presence of model parameter uncertainties. The proposed approach yields
improved tracking performance under dynamics uncertainties. The shallow
adaptive neural network used in this paper does not require training a priori
and has the potential to be implemented on the real-time robotic controller. A
comparative simulation study of a 3D Cassie robot is presented to illustrate
the performance of the proposed approach under various scenarios.
