| With the continuous progress of automobile industry and the improvement of people’s demand for driving experience,intelligent vehicle has become one of the hot research objects.Among them,the path tracking control technology as a key part of the unmanned driving technology has also become a key research direction.At present,most of the research on path tracking control is limited to improving the path tracking accuracy,without considering the vehicle handling stability and riding comfort.In view of the above research deficiencies,this paper proposes an active optimization method of preview distance based on the analysis of multi parameter changing simulation conditions,which ensures the path tracking accuracy of intelligent vehicles and the ride comfort.The main work is as follows:Firstly,based on the idea of modularization,a seven degree of freedom vehicle dynamics model and a nonlinear tire model are established.At the same time,taking the driver model as the reference object,the preview error model with preview distance and road curvature as input is established.Secondly,the lateral motion controller is designed based on the sliding mode variable structure control theory,and the path tracking control system is constructed by combining the vehicle monorail model and preview error model.On the premise of verifying the tracking effect of the controller,a variety of variable parameter comparison conditions are set,and the influence of vehicle speed,road curvature and preview distance on vehicle path tracking accuracy and handling stability is analyzed,as well as the coupling mechanism between them.The simulation results show that with the increase of vehicle speed,the path tracking accuracy has no obvious change,and the handling stability becomes worse;With the increase of road curvature,the path tracking accuracy decreases and the handling stability becomes worse;The longer the preview distance,the lower the path tracking accuracy and the better the handling stability.Then,an active optimization method of preview distance is designed.Considering the "Person-Vehicle-Road" system of intelligent transportation system,the critical values of collision,instability and comfort are set.Combined with the lateral displacement and azimuth deviation,which represent the path tracking accuracy,and the lateral acceleration,which represent the handling stability and human comfort,all the simulated road curvature and speed conditions are divided into three groups: safe area(S),changeable area(C),dangerous area(D).At the same time,the threshold range of road curvature and vehicle speed is obtained.According to the coupling mechanism of path tracking accuracy and handling stability,particle swarm optimization algorithm is used to optimize the preview distance in the changeable area(C).The simulation results show that the optimized vehicle tracking accuracy is significantly improved,and the ride comfort is within the acceptable range.Finally,the HIL platform test is carried out,and the control algorithm and controlled model are introduced into the controller and NI simulator.The real-time performance and effectiveness of the controller are verified by comparing the simulation results with the experimental results.At the same time,two groups of working condition contrast tests before and after optimizing the preview distance are carried out.Simulation and experiments show that the active optimization method of preview distance can achieve the optimal comprehensive performance of intelligent vehicle path tracking accuracy and ride comfort.HIL test results show that this method can improve the path tracking accuracy by 71% and 48% respectively,and the ride comfort is deteriorated by less than 10%,and can meet the requirements of ride comfort,which proves the superiority of the active optimization method of preview distance. |
