上海交通大学学报(英文版) ›› 2017, Vol. 22 ›› Issue (2): 224-232.doi: 10.1007/s12204-017-1825-5

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Hierarchical Control Strategy of Trajectory Tracking for Intelligent Vehicle

ZHANG Qian* (张茜), LIU Zhiyuan (刘志远)   

  1. (Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China)
  • 出版日期:2017-03-31 发布日期:2017-04-04
  • 通讯作者: ZHANG Qian (张茜) E-mail: zhangqianty0305@sina.com

Hierarchical Control Strategy of Trajectory Tracking for Intelligent Vehicle

ZHANG Qian* (张茜), LIU Zhiyuan (刘志远)   

  1. (Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China)
  • Online:2017-03-31 Published:2017-04-04
  • Contact: ZHANG Qian (张茜) E-mail: zhangqianty0305@sina.com

摘要: In order to track the desired trajectory for intelligent vehicle, a new hierarchical control strategy is presented. The control structure consists of two layers. The high-level controller adopts the model predictive control (MPC) to calculate the steering angle tracking the desired yaw angle and the lateral position. The low-level controller is designed as a gain-scheduling controller based on linear matrix inequalities. The desired longitudinal velocity and the yaw rate are tracked by the adjustment of each wheel torque. The simulation results via the high-fidelity vehicle dynamics simulation software veDYNA show that the proposed strategy has a good tracking performance and can guarantee the yaw stability of intelligent vehicle.

关键词: trajectory tracking control, model predictive control (MPC), linear parameter varying (LPV), gainscheduling control

Abstract: In order to track the desired trajectory for intelligent vehicle, a new hierarchical control strategy is presented. The control structure consists of two layers. The high-level controller adopts the model predictive control (MPC) to calculate the steering angle tracking the desired yaw angle and the lateral position. The low-level controller is designed as a gain-scheduling controller based on linear matrix inequalities. The desired longitudinal velocity and the yaw rate are tracked by the adjustment of each wheel torque. The simulation results via the high-fidelity vehicle dynamics simulation software veDYNA show that the proposed strategy has a good tracking performance and can guarantee the yaw stability of intelligent vehicle.

Key words: trajectory tracking control, model predictive control (MPC), linear parameter varying (LPV), gainscheduling control

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