| Driverless cars are the inevitable trend of the development of future vehicles,and the realization of vehicle intelligence is an effective way to improve road safety and transport efficiency.However,there are still many technical problems to be solved in order to realize the commercialization of driverless cars,so it is crucial to ensure the reliability of driverless cars in complex environments.Path tracking algorithm is one of the core algorithms of unmanned vehicle system.In order to realize unmanned driving under full speed condition,a path tracking algorithm for critical driving maneuvers should be established.This paper focuses on the research of vehicle under limit conditions of path tracking algorithm.And it sets up a kind of linear time-varying model predictive control algorithm framework based on yaw acceleration nullcline stability domain.By considering the tire time-varying cornering stiffness,model mismatch problem is corrected,which is one of the disadvantages of linear model predictive control algorithm under critical driving maneuvers.The main contents and research results of this paper are as follows:(1)Based on the analysis of the vehicle dynamics model,the three DOF single track model and the three-DOF dual track model are established.Based on magic formula,the brush tire model was calibrated.(2)In order to solve the problem of nonlinear vehicle system state estimation under critical driving maneuvers,an algorithm framework of vehicle state estimation was established based on the vehicle three DOF dual-track model.An unscented kalman filter algorithm based on unscented transformation is derived,and the method of linearization of nonlinear function in extended kalman filter is abandoned.The lateral forces of the front/rear axles and the lateral/longitudinal velocity of the vehicle are estimated by using the observed quantities easily obtained by vehicle mounted sensors such as lateral/longitudinal acceleration and yaw velocity of the vehicle.(3)The 3DOF single-track dynamic model and brush tire model were used to analyze the- phase plane of the vehicle under different working conditions.And the influences of front wheel Angle,longitudinal speed and road adhesion coefficient on the phase trajectory and equilibrium point are analyzed.In order to solve the yaw stability problem of driverless vehicles under critical driving maneuvers,a stability domain model with yaw acceleration nullcline and saturated boundary of rear wheel was proposed.In this way,the dynamic constraints of the existing path tracking model predictive control algorithm are improved to avoid the excessive limitation of the steady yaw velocity stability domain to the yaw velocity transient process,so as to expand the feasible range of vehicle state.In the Carsim/Simulink co-simulation platform,the elk test(double lane change)was introduced to conduct the comparison simulation test of high-speed multi-scene,and the control performance of the algorithm in high-speed scenario was verified.(4)On the basis of the aforementioned predictive control algorithm,the lateral nonlinear characteristics of tires under the limit condition are fully considered.In this paper,an adaptive forgetting factor recursive least square method is proposed to estimate the vehicle’s lateral stiffness in real time,which takes into account the time delay of tire lateral deflection.An adaptive model predictive control algorithm based on deterministic equivalence principle is introduced,and the cornering stiffness in the current prediction model is adjusted in real time based on the past and future state information of the vehicle.Comparing with the conventional model prediction controller and the aforementioned model prediction control algorithm,the performance of this method on variable attachment coefficient roads is verified. |
