  Quadruped robots manifest great potential to traverse rough terrains with
payload. Numerous traditional control methods for legged dynamic locomotion are
model-based and exhibit high sensitivity to model uncertainties and payload
variations. Therefore, high-performance model parameter estimation becomes
indispensable. However, the inertia parameters of payload are usually unknown
and dynamically changing when the quadruped robot is deployed in versatile
tasks. To address this problem, online identification of the inertia parameters
and the Center of Mass (CoM) position of the payload for the quadruped robots
draw an increasing interest. This study presents an adaptive controller based
on the online payload identification for the high payload capacity (the ratio
between payload and robot's self-weight) quadruped locomotion. We name it as
Adaptive Controller for Quadruped Locomotion (ACQL), which consists of a
recursive update law and a control law. ACQL estimates the external forces and
torques induced by the payload online. The estimation is incorporated in
inverse-dynamics-based Quadratic Programming (QP) to realize a trotting gait.
As such, the tracking accuracy of the robot's CoM and orientation trajectories
are improved. The proposed method, ACQL, is verified in a real quadruped robot
platform. Experiments prove the estimation efficacy for the payload weighing
from 20 $kg$ to 75 $kg$ and loaded at different locations of the robot's torso.
