智能汽车轨迹跟踪与横摆稳定性协同控制

为了提高四轮独立驱动智能电动汽车在变曲率弯道下的轨迹跟踪精度和横摆稳定性，提出了一种模型预测控制与直接横摆力矩控制协同的综合控制方法。建立了横纵向耦合的车辆动力学模型，采用2阶龙格库塔离散法保证了离散模型的精度，并基于简化的2自由度动力学模型推导了车辆横摆稳定性约束，设计了非线性模型预测控制器；利用直接横摆力矩控制能够改变车辆横摆角速度和航向角的特点，考虑模型预测控制器的预测状态、控制量以及跟踪误差，设计了协同控制规则。仿真结果表明，协同控制方法解决了考虑横摆稳定性约束的模型预测控制器中存在的稳定性约束与控制精度相矛盾的问题，并补偿了模型预测控制器没有可行解时对横摆稳定性的约束，同时提高了智能汽车的轨迹跟踪精度和横摆稳定性。

Collaborative Control over Trajectory Tracking and Yaw Stability for Intelligent Vehicles

In order to improve the trajectory tracking accuracy and yaw stability of intelligent four-wheel independent drive electric vehicles subject to varied road curvature, a comprehensive control method combining model predictive control with direct yaw moment control is proposed. Firstly the vehicle dynamics model considering lateral and longitudinal coupling is established, and the second-order Runge Kutta discrete method is applied to enhance the accuracy of the discrete model. Based on the simplified two-degree-of freedom dynamics model, the vehicle yaw stability constraints are derived and the nonlinear model predictive controller is designed. Then, due to the fact that the direct yaw moment control can change the vehicle yaw rate and the heading angle, the collaborative control rules are designed, taking into account the prediction state, control command and tracking error of the controller. The simulation results show that the proposed collaborative control solves the contradiction between the stability constraint and control accuracy,compensates the yaw stability constraint if no feasible solution is found, and improves the trajectory tracking accuracy and yaw stability of intelligent vehicles.