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中南大学学报(英文版)

Journal of Central South University

Vol. 27    No. 12    December 2020

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MPC-based path tracking with PID speed control for high-speed autonomous vehicles considering time-optimal travel
CHEN Shu-ping(陈舒平)1, XIONG Guang-ming(熊光明)1, CHEN Hui-yan(陈慧岩)1, NEGRUT Dan2

1. School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China;
2. Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA

Abstract:In order to track the desired path as fast as possible, a novel autonomous vehicle path tracking based on model predictive control (MPC) and PID speed control was proposed for high-speed automated vehicles considering the constraints of vehicle physical limits, in which a forward-backward integration scheme was introduced to generate a time-optimal speed profile subject to the tire-road friction limit. Moreover, this scheme was further extended along one moving prediction window. In the MPC controller, the prediction model was an 8-degree-of-freedom (DOF) vehicle model, while the plant was a 14-DOF vehicle model. For lateral control, a sequence of optimal wheel steering angles was generated from the MPC controller; for longitudinal control, the total wheel torque was generated from the PID speed controller embedded in the MPC framework. The proposed controller was implemented in MATLAB considering arbitrary curves of continuously varying curvature as the reference trajectory. The simulation test results show that the tracking errors are small for vehicle lateral and longitudinal positions and the tracking performances for trajectory and speed are good using the proposed controller. Additionally, the case of extended implementation in one moving prediction window requires shorter travel time than the case implemented along the entire path.

 

Key words: model predictive control; path tracking; minimum-time speed profile; vehicle dynamics; arbitrary path

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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