A model predictive speed tracking control approach for autonomous ground vehicles

Min Zhu, Huiyan Chen*, Guangming Xiong

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

140 Citations (Scopus)

Abstract

This paper presents a novel speed tracking control approach based on a model predictive control (MPC) framework for autonomous ground vehicles. A switching algorithm without calibration is proposed to determine the drive or brake control. Combined with a simple inverse longitudinal vehicle model and adaptive regulation of MPC, this algorithm can make use of the engine brake torque for various driving conditions and avoid high frequency oscillations automatically. A simplified quadratic program (QP) solving algorithm is used to reduce the computational time, and the approach has been applied in a 16-bit microcontroller. The performance of the proposed approach is evaluated via simulations and vehicle tests, which were carried out in a range of speed-profile tracking tasks. With a well-designed system structure, high-precision speed control is achieved. The system can robustly model uncertainty and external disturbances, and yields a faster response with less overshoot than a PI controller.

Original languageEnglish
Pages (from-to)138-152
Number of pages15
JournalMechanical Systems and Signal Processing
Volume87
DOIs
Publication statusPublished - 15 Mar 2017

Keywords

  • Autonomous ground vehicles
  • Model predictive control
  • Real-time optimization
  • Speed control
  • Speed tracking

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