An integrated path-tracking and control allocation method for autonomous racing electric vehicles | |
Article; Early Access | |
关键词: MODEL-PREDICTIVE CONTROL; YAW-MOMENT CONTROL; STEERING CONTROL; OPTIMIZATION; | |
DOI : 10.1080/00423114.2023.2242533 | |
来源: SCIE |
【 摘 要 】
In recent years, path-tracking controllers for autonomous passenger vehicles and Control Allocation (CA) methods for handling and stability control have both received extensive discussion in the literature. However, the integration of the path-tracking control with CA methods for autonomous racing vehicles has not attracted much attention. In this study, we design an integrated path-tracking and CA method for a prototype autonomous racing electric vehicle with a particular focus on the maximising the turning speed in tight cornering. The proposed control strategy has a hierarchical structure to improve the computational efficiency: the high-level path-tracking Model Predictive Control (MPC) based on a rigid body model is designed to determine the virtual control forces according to the desired path and desired maximum velocity profile, while the low-level CA method uses a Quadratically Constrained Quadratic Programming (QCQP) formulation to distribute the individual control actuator according to the desired virtual control values. The proposed controller is validated in a high-fidelity simulation vehicle model with the computational time of the optimisation controller presented to demonstrate the real-time control performance.
【 授权许可】
Free