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Automotive Engineering ›› 2025, Vol. 47 ›› Issue (9): 1655-1664.doi: 10.19562/j.chinasae.qcgc.2025.09.002

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FPGA Hardware-Accelerated Implementation of Model Predictive Path Tracking Control for Autonomous Vehicles

Wenchang Li,Zhiguo Zhao(),Kaichong Liang,Kun Zhao,Qin Yu   

  1. College of Automotive Studies,Tongji University,Shanghai 201804
  • Received:2025-03-14 Revised:2025-04-17 Online:2025-09-25 Published:2025-09-19
  • Contact: Zhiguo Zhao E-mail:zhiguozhao@tongji.edu.cn

Abstract:

For the high complexity of online solving in model predictive control (MPC) and its challenges in real-time implementation on existing autonomous vehicle onboard controllers, an FPGA hardware-accelerated implementation of MPC-based path tracking method is proposed in this paper. Firstly, an MPC-based path tracking controller for autonomous vehicles is designed. Then, to simplify the solution process, the MPC problem is transformed into a constrained quadratic programming problem, and the Hildreth method is introduced for solving it. Furthermore, to improve the real-time performance and deployment efficiency of the control algorithm, a convenient FPGA implementation scheme for the MPC path tracking algorithm is developed based on the Xilinx System Generator tool. Finally, MATLAB/Simulink-CarSim co-simulation and hardware-in-the-loop (HIL) tests are conducted under different conditions. The results show that the proposed method enables autonomous vehicles to accurately track the desired path, with an average FPGA computation time of less than 0.1 ms, validating its effectiveness and real-time performance.

Key words: autonomous vehicles, model predictive control, FPGA implementation, path tracking