Full-State Decoupling Motion Control Strategy for Distributed Vehicle via Bayesian Parameter Optimization

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%.