Lane-keeping control for commercial vehicles with an MPC algorithm considering parameter estimation
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.
lane-keepingparameter estimationmodel predictive control(MPC)recursive least squares(RLS)extended Kalman filter(EKF)
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