基于Pareto最优均衡理论的无人驾驶汽车稳定跟踪控制研究

Research on Stable Tracking Control Method of Autonomous Vehicle Based on Pareto Optimal Equilibrium Theory

针对无人驾驶车辆在高速转弯工况下轨迹跟踪精度及稳定性下降的问题,通过将轨迹跟踪控制和稳定性控制视为动态博弈过程中的参与者,提出了一种基于Pareto最优均衡理论的轨迹跟踪与稳定性协同控制策略。首先,基于模型预测控制,设计了车辆轨迹跟踪控制及稳定性控制模型。其次,根据轨迹跟踪与稳定性控制的代价函数设计了全局代价函数,通过Pareto最优均衡理论对两者的权重进行动态博弈,在满足约束条件下实现目标任务全局性能最优。最后通过Simulink&CarSim建立联合仿真模型,在双移线工况下对所提方法进行对比验证。仿真结果表明,所提方法相较于定权重路径跟踪与稳定性协同控制器,能有效提升车辆轨迹跟踪控制精度并保证较好的行驶稳定性。

Aiming at the lowering of the trajectory tracking accuracy and lateral stability,the trajectory tracking control and stability control are considered as the participants in the dynamic game process.Based on the Pareto optimal equilibrium theory,a cooperative control strategy of trajectory tracking and stability is proposed.Firstly,vehicle trajectory tracking control model and stability control model are designed based on the model predictive control algorithm.Secondly,the global cost function is designed according to the cost function of trajectory tracking and stability control.The Pareto optimal equilibrium theory is used to game the weights of the trajectory tracking and stability control for the global optimization.In the end,Simulink/CarSim joint simulation is conducted with a result showing that compared with fixed weight path tracking and stability cooperative controller,the proposed method can improve the accuracy of the vehicle trajectory tracking and driving stability.