TY - GEN
T1 - A Robust Control Scheme for Autonomous Vehicles Path Tracking under Unreliable Communication
AU - Zhang, Kun
AU - Zhang, Huaguang
AU - Xue, Wenchao
AU - Zhang, Ran
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - This paper investigates the intelligent driving control problem for a class of autonomous vehicles by using a policy iteration method. Firstly, we analyze the autonomous vehicle's motion with respect to its linear and rotational velocities under unreliable communication. By this way, the dynamic function based on kinematic equation is built, then, combining the desired reference and the autonomous vehicle's trajectories, the tracking error system is constructed with uncertainty. Secondly, according to the robust optimal control method, a performance index and an auxiliary system are designed, which convert the tracking driving problem into an optimal control problem. Besides, by iterating the Hamiltonian function and control policy, the control law is addressed, which is proved stabilizing the tracking driving dynamic. Finally, the simulation is implemented to verify the robust tracking control scheme, and the results demonstrate its effectiveness.
AB - This paper investigates the intelligent driving control problem for a class of autonomous vehicles by using a policy iteration method. Firstly, we analyze the autonomous vehicle's motion with respect to its linear and rotational velocities under unreliable communication. By this way, the dynamic function based on kinematic equation is built, then, combining the desired reference and the autonomous vehicle's trajectories, the tracking error system is constructed with uncertainty. Secondly, according to the robust optimal control method, a performance index and an auxiliary system are designed, which convert the tracking driving problem into an optimal control problem. Besides, by iterating the Hamiltonian function and control policy, the control law is addressed, which is proved stabilizing the tracking driving dynamic. Finally, the simulation is implemented to verify the robust tracking control scheme, and the results demonstrate its effectiveness.
KW - Autonomous vehicle
KW - intelligent driving
KW - robust control
KW - tracking control
UR - https://www.scopus.com/pages/publications/85137746349
U2 - 10.1109/DDCLS55054.2022.9858512
DO - 10.1109/DDCLS55054.2022.9858512
M3 - 会议稿件
AN - SCOPUS:85137746349
T3 - Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022
SP - 1413
EP - 1418
BT - Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022
A2 - Sun, Mingxuan
A2 - Chen, Zengqiang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2022
Y2 - 3 August 2022 through 5 August 2022
ER -