基于路面附着系数估计的车辆轨迹跟踪控制

Vehicle Trajectory Tracking Control Based on Road Adhesion Coefficient Estimation

针对车辆在高速转向和不同路面附着系数下的轨迹跟踪控制问题,基于模型预测控制理论提出了一种考虑路面附着系数的变侧偏角约束MPC控制策略。根据魔术公式轮胎模型分析轮胎的侧偏特性以及不同附着系数对轮胎侧偏角-侧向力线性区的影响,建立轮胎侧偏角约束与不同路面附着系数的函数关系;采用遗传算法(GA)优化BP神经网络模型设计路面附着系数估计器,将估计结果作为与轮胎侧偏角约束相关的变量传递到MPC控制器中;最后在MPC控制器中建立系统控制量约束、控制增量约束,以及考虑路面附着系数的变侧偏角约束,将不同路面附着系数工况下的轨迹跟踪问题转化为多约束条件下最优值求解问题,实现轨迹跟踪和车辆稳定性控制。仿真和试验结果表明,考虑路面附着系数变化的MPC控制方法相对传统MPC控制方法在各种工况下具有更高的轨迹跟踪精度和更好的车辆稳定性,GA-BP神经网络路面系数估计方法具有很高的估计精度。

For the trajectory tracking control problem of vehicles under high speed steering and different road adhesion coefficients,a variable sideslip angle constrained MPC control strategy is proposed based on model predictive control theory considering road adhesion coefficients.According to the magic formula tire model,the tire cornering property as well as the influence of different adhesion coefficients on the tire slip angle-lateral force linear region is analysed.Then the function relationship between tire slip angle constraint and different road adhesion coef⁃ficients is established.The genetic algorithm(GA)is used to optimize the BP neural network model to design the road adhesion coefficient estimator,and the estimation results are transmitted to the MPC controller as variables re⁃lated to the tire slip constraint.Finally,the system control quantity constraint,the control increment constraint,and the variable sideslip angle constraint considering the road adhesion coefficient are established in the MPC controller.The trajectory tracking problem under different road adhesion conditions is transformed into the optimal value solu⁃tion problem under various constraints to realize trajectory tracking and vehicle stability control.The simulation and experimental results show that the MPC control method considering the change of road adhesion coefficient has high⁃er trajectory tracking accuracy and better vehicle stability than the traditional MPC control method under various working conditions,with high estimation accuracy of the GA-BP neural network road coefficient estimation method.