With the rapid development of autonomous driving technology, unmanned ground
vehicles (UGVs) are gradually replacing humans to perform tasks such as
reconnaissance, target tracking, and search in special scenarios.
Omnidirectional mobility based on rapid adjustment of vehicle heading posture
enhances the applicability of UGVs in specialized scenarios. Omnidirectional
mobility signifies the capability for rapid adjustments to the vehicle’s heading
angle, longitudinal velocity, and lateral velocity. Traditional vehicles are
constrained by the limitations of under-actuation, which prevents active
regulation of lateral movement. Instead, they rely on the coordinated regulation
of longitudinal and yaw movements, failing to meet the requirements for
omnidirectional mobility. Distributed vehicles featuring steering distributed
between the front/rear axles and four-wheel independent drive leverage the
over-actuation advantages provided by multi-actuator coordinated control, making
them particularly suitable for omnidirectional mobility at large sideslip
angles. This feature enables the UGVs to achieve rapid adjustment of vehicle
heading posture. However, existing control strategies centered on stabilizing
yaw rate and suppressing sideslip angles cannot adapt to the decoupling control
requirements of such platforms. Additionally, the strong coupling
characteristics between actuator subsystems further exacerbate control
difficulties. To this end, this article proposes a full-state decoupling motion
control strategy, the nonlinear model is locally linearized at each equilibrium
point of the vehicle, and a set of equilibrium state models is derived. The
validity of this local linearization method is verified through phase diagram
analysis and modal analysis. The Bayesian optimization (BO) algorithm is then
employed to optimize and identify the cornering stiffness of the front/rear
axles at each equilibrium point in these locally linearized models, thereby
enhancing the characterization ability of the linear model for the nonlinear
dynamic model at the corresponding equilibrium points. Subsequently, a
full-state decoupling motion controller is designed by integrating the model
predictive control (MPC) algorithm. Finally, the controller presented in this
article is employed on the distributed vehicle experiment platform (DVEP). The
experimental results demonstrate that in two drift-like scenarios with different
sideslip angles, compared with the baseline controller, the path tracking error
of this method is reduced by more than 13%, and the sideslip angle tracking
error is reduced by more than 12%.
