Trajectory tracking control of driverless vehicle based on road adaptive model predictive control
1. Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China; 2. School of Mechanical and Electrical Engineering, China University of Petroleum (East China), Qingdao, Shandong 266580, China
Abstract：To solve the problems of narrow driving condition range, partial evaluation method and insufficient road adaptive control of trajectory tracking, the trajectory tracking accuracy and driving safety under full speed and full road adhesion coefficient were investigated. In the road adaptive model predictive control(MPC), the discrete linear timevarying predictive model was obtained by the threedegreeoffreedom nonlinear dynamic model of the vehicle. The road adaptive speed range matching was realized according to the road adhesion coefficient detected by sensors, and the constraint condition of the speed increment was formulated. The maximum and standard deviation of path tracking error, lateral acceleration, centroid sideslip angle and front wheel sideslip angle with the change of velocity and road adhesion coefficient were obtained by integrating CarSim and Matlab softwares. Combined with the mechanism of tire sideslip, the driving safety was evaluated，and the trajectory tracking stability/ instability area of vehicle under all working conditions was divided. The results show that the vehicle speed can be adaptively adjusted according to the road information to achieve excellent trajectory tracking accuracy and driving safety under all working conditions.
YUAN C C, SONG J H, HE Y G, et al. Active collision avoidance algorithm of autonomous vehicle based on pedestrian trajectory prediction\[J\]. Journal of Jiangsu University (Natural Science Edition), 2021, 42(1): 1-8. (in Chinese)
［2］
ZHOU X B, YU X, ZHANG Y M, et al. Trajectory planning and tracking strategy applied to an unmanned ground vehicle in the presence of obstacles\[J\]. IEEE Transactions on Automation Science and Engineering, 2021, 18(4): 1575-1589.
LI Y Z, LI G, ZHANG Z H. Lateral tracking control of a driverless fourwheel steering vehicle under extreme conditions\[J\]. Journal of Chongqing University of Technology(Natural Science),2023,37(1): 66-74. (in Chinese)
［5］
SONG P, ZONG C F, TOMIZUKA M. Combined longitudinal and lateral control for automated lane guidance of full drivebywire vehicles[J]. SAE International Journal of Passenger CarsElectronic and Electrical Systems, 2015, 8(2): 419-424.
［6］
WANG H Y, LIU B, PING X Y, et al. Path tracking control for autonomous vehicles based on an improved MPC[J]. IEEE Access, 2019, 7: 161064-161073.
［7］
JI J, KHAJEPOUR A, MELEK W W, et al. Path planning and tracking for vehicle collision avoidance based on model predictive control with multiconstraints[J].IEEE Transactions on Vehicular Technology, 2017, 66(2): 952-964.
［8］
KAZEMI H, MAHJOUB H N, TAHMASBISARVESTANI A, et al. A learningbased stochastic MPC design for cooperative adaptive cruise control to handle interfering vehicles[J].IEEE Transactions on Intelligent Vehicles, 2018, 3(3): 266-275.
HU J M, HU Y H, CHEN H Y, et al. Research on trajectory tracking of unmanned tracked vehicles based on model predictive control[J]. Acta Armamentarii, 2019, 40(3): 456-463. (in Chinese)
LIU K, WANG W, GONG J W, et al. Dynamic modeling and trajectory tracking of intelligent vehicles in offroad terrain[J]. Transactions of Beijing Institute of Technology, 2019, 39(9): 933-937. (in Chinese)
[12]
TAN Q F, DAI P L, ZHANG Z H, et al. MPC and PSO based control methodology for path tracking of 4WS4WD vehicles[J]. Applied Sciences, doi: 10.3390/app8061000.
WU H D, SI Z L. Intelligent vehicle trajectory tracking control based on linear matrix inequality[J]. Journal of Zhejiang University (Engineering Science), 2020, 54(1): 110-117. (in Chinese)
YUAN C C, SONG J H, HE Y G, et al. Active collision avoidance algorithm of autonomous vehicle based on pedestrian trajectory prediction\[J\]. Journal of Jiangsu University (Natural Science Edition), 2021, 42(1): 1-8. (in Chinese)
［2］
ZHOU X B, YU X, ZHANG Y M, et al. Trajectory planning and tracking strategy applied to an unmanned ground vehicle in the presence of obstacles\[J\]. IEEE Transactions on Automation Science and Engineering, 2021, 18(4): 1575-1589.
LI Y Z, LI G, ZHANG Z H. Lateral tracking control of a driverless fourwheel steering vehicle under extreme conditions\[J\]. Journal of Chongqing University of Technology(Natural Science),2023,37(1): 66-74. (in Chinese)
［5］
SONG P, ZONG C F, TOMIZUKA M. Combined longitudinal and lateral control for automated lane guidance of full drivebywire vehicles[J]. SAE International Journal of Passenger CarsElectronic and Electrical Systems, 2015, 8(2): 419-424.
［6］
WANG H Y, LIU B, PING X Y, et al. Path tracking control for autonomous vehicles based on an improved MPC[J]. IEEE Access, 2019, 7: 161064-161073.
［7］
JI J, KHAJEPOUR A, MELEK W W, et al. Path planning and tracking for vehicle collision avoidance based on model predictive control with multiconstraints[J].IEEE Transactions on Vehicular Technology, 2017, 66(2): 952-964.
［8］
KAZEMI H, MAHJOUB H N, TAHMASBISARVESTANI A, et al. A learningbased stochastic MPC design for cooperative adaptive cruise control to handle interfering vehicles[J].IEEE Transactions on Intelligent Vehicles, 2018, 3(3): 266-275.
HU J M, HU Y H, CHEN H Y, et al. Research on trajectory tracking of unmanned tracked vehicles based on model predictive control[J]. Acta Armamentarii, 2019, 40(3): 456-463. (in Chinese)
LIU K, WANG W, GONG J W, et al. Dynamic modeling and trajectory tracking of intelligent vehicles in offroad terrain[J]. Transactions of Beijing Institute of Technology, 2019, 39(9): 933-937. (in Chinese)
[12]
TAN Q F, DAI P L, ZHANG Z H, et al. MPC and PSO based control methodology for path tracking of 4WS4WD vehicles[J]. Applied Sciences, doi: 10.3390/app8061000.
WU H D, SI Z L. Intelligent vehicle trajectory tracking control based on linear matrix inequality[J]. Journal of Zhejiang University (Engineering Science), 2020, 54(1): 110-117. (in Chinese)
