考虑参数估计的MPC算法的商用车车道保持控制

Lane-keeping control for commercial vehicles with an MPC algorithm considering parameter estimation

设计了一种考虑参数估计的模型预测控制(MPC)算法的、智能辅助驾驶的商用车的车道保持算法,对于难以直接测量的质量和横向速度进行估计。建立车辆动力学模型和状态误差方程,通过扩展Kalman滤波(EKF)和递推最小二乘法(RLS)分别对车辆的横向速度、质量进行估计。基于估计得到的车辆参数,设计MPC车道保持控制器。构建硬件在环(HIL)仿真平台,设置不同的测试工况对车道保持算法进行了验证。结果表明:与普通MPC相比,在偏移回正工况中,车辆纠偏消耗的时间减少28.6%,并且超调量更小;高速路工况的横向位置偏差的均方根误差减小了4.2 cm。该方法提升了纠偏能力和跟踪精度,降低了传感器成本。

A lane-keeping algorithm was designed with the model predictive control(MPC)algorithm for commercial vehicles equipped with intelligent assisted driving.This algorithm took account parameter estimation and was capable of estimating mass and lateral velocity,which were difficult to directly measure.The extended Kalman filter(EKF)and recursive least squares(RLS)were used to estimate the lateral velocity and the mass of the vehicle,respectively.An MPC lane-keeping controller based on the estimated parameters was designed.A hardware-in-the-loop(HIL)was constructed.Different test conditions were established to verify the lane keeping algorithm.The results show that compared with the ordinary MPC,the time for vehicle correction is reduced by 28.6%and the overshoot is smaller in the offset return condition.In the highway condition,the root mean square of the lateral error is reduced by 4.2 cm.At low sensor costs,the correction ability and tracking accuracy are improved.