  Model-based controllers using a linearized model around the system's
equilibrium point is a common approach in the control of a wheeled humanoid due
to their less computational load and ease of stability analysis. However,
controlling a wheeled humanoid robot while it lifts an unknown object presents
significant challenges, primarily due to the lack of knowledge in object
dynamics. This paper presents a framework designed for predicting the new
equilibrium point explicitly to control a wheeled-legged robot with unknown
dynamics. We estimated the total mass and center of mass of the system from its
response to initially unknown dynamics, then calculated the new equilibrium
point accordingly. To avoid using additional sensors (e.g., force torque
sensor) and reduce the effort of obtaining expensive real data, a data-driven
approach is utilized with a novel real-to-sim adaptation. A more accurate
nonlinear dynamics model, offering a closer representation of real-world
physics, is injected into a rigid-body simulation for real-to-sim adaptation.
The nonlinear dynamics model parameters were optimized using Particle Swarm
Optimization. The efficacy of this framework was validated on a physical
wheeled inverted pendulum, a simplified model of a wheeled-legged robot. The
experimental results indicate that employing a more precise analytical model
with optimized parameters significantly reduces the gap between simulation and
reality, thus improving the efficiency of a model-based controller in
controlling a wheeled robot with unknown dynamics.
