| Intelligent vehicle trajectory tracking control,as the motion control and execution link of the autopilot vehicle,directly determines the overall performance of the intelligent vehicles.It has extremely important significance in improving the safety of driving and relieving traffic pressure.At present,there are still some technical bottlenecks in the research of trajectory tracking technology.In order to satisfy people's higher pursuit for the comprehensive performance of intelligent vehicles,it is very important to deeply analyze the trajectory tracking control technology.In the existing research,it is difficult to obtain accurate vehicle dynamic model parameters,and there is no simple and effective method for the identification process of actual vehicle parameters,which results in great discrepancy between simulation results and actual vehicle test results.In addition,in speed tracking control,simple arbitration strategy is usually used to switch the driving and braking control modes,which often leads to irregular switching process,and few control algorithms can better consider and solve the occupant comfort problem.Thirdly,most of the control algorithms in path tracking control are based on single-point position tracking,which fails to make full use of path continuity information,and often affects the smoothness of steering control due to the discontinuity of learning jumps in single-point position.In addition,the trajectory tracking control under extreme conditions is also very challenging.At this time,the tire longitudinal and lateral forces are highly coupled,and the resultant force is close to or beyond the tire force limit(frictional circle boundary).The traditional linear control method not only has poor control accuracy,but also may lead to vehicle instability.Therefore,it is of great significance to study the trajectory tracking control technology of intelligent vehicles.Based on the national key R&D project "Human-Computer Interaction Theory of Intelligent Electric Vehicle"(project number: 2016YFB0100904)and the National Natural Science Foundation project "Integrative Modeling and Integrated Control Method of Intelligent Electric Vehicle"(project number: U1564211),this paper puts forward the trajectory tracking control algorithm of intelligent vehicle,and carries out the work of algorithm simulation and real vehicle test.Specific research contents include the following five aspects:(1)Modeling of vehicle dynamics and kinematics.Firstly,through the establishment of vehicle longitudinal dynamics and lateral dynamics model,the transfer relationship between the output of the controlled object and the control quantity is deeply analyzed.Then,the tire model based on magic formula is established,and the expressions of longitudinal force and lateral force of vehicle are deduced,which provides theoretical support for the study of trajectory tracking control considering the boundary condition of tire friction circle under extreme conditions.Thirdly,the vehicle kinematics model is established,which provides an accurate controlled object model for the trajectory tracking control research in this paper.(2)Research on speed tracking control algorithm.Firstly,a speed tracking control architecture based on hierarchical control idea is proposed.The expected acceleration is calculated by using the PID and MPC algorithms respectively,and the comparison between them is made.Then,the driving/braking mode is arbitrated by introducing the taxiing curve of the vehicle,so that the two modes can be switched smoothly.Thirdly,deceleration adjustment factor is introduced in the braking process to improve ride comfort.Finally,the desired acceleration/deceleration is converted into the actual control quantity by establishing the vehicle longitudinal dynamics inverse model.(3)Research on path tracking control algorithm.Firstly,a path tracking control architecture based on hierarchical control is proposed.Then,the transformation model of GPS longitude and latitude to geodetic coordinates is deduced,which provides position coordinates for calculating path curvature.Thirdly,a constrained arc fitting method is proposed to calculate the path curvature,and the fitting accuracy is analyzed.On this basis,a steering wheel angle control method based on curvature feedforward plus error feedback control is proposed.Finally,a new method of dynamic sub path updating is proposed,which integrates all the algorithms to track the desired path accurately and stably.(4)Research on trajectory tracking control algorithm under extreme conditions.Firstly,the limit conditions are analyzed and selected,and the limit conditions are distinguished by the mathematical description of the tire friction circle boundary.Then,a three-degree-of-freedom non-linear dynamic model is established to calculate the tire vertical force,longitudinal force and lateral force.Then,the road adhesion coefficient is identified to calculate the boundary of each tire friction circle.Finally,based on the calculated stability boundary,the desired speed and the desired path tracking control are modified to ensure that the vehicle has a high track tracking accuracy in stable driving state.(5)Trajectory tracking control algorithm simulation and vehicle verification.Firstly,based on the vehicle model established in this paper and Pano Sim intelligent simulation platform,the trajectory tracking control algorithms are fully simulated and validated,and compared with the existing control algorithm.Then,the trajectory tracking control algorithm under extreme conditions is simulated and validated,and the results show that the vehicle can maintain stable driving state at all times.Then,a real vehicle platform of intelligent vehicle is built,and some parameters of the model are identified.The path tracking control algorithm and the speed tracking control algorithm of intelligent vehicle platform are tested under different conditions.The results show that the trajectory tracking control algorithm proposed in this paper has good accuracy and stability of real vehicle control. |
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