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Research on Path Tracking Control of Autonomous Vehicles Based on PSO-BP Optimized MPC
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Peilong Shi1, Hong Chang1, 2, Cairui Wang1, Qiang Ma1, Meng Zhou1
Automobile Technology | 2023, (7) : 38 - 46
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Automobile Technology | 2023, (7): 38-46
• Special Topic on Vehicle Trajectory Prediction and Path Tracking •
Research on Path Tracking Control of Autonomous Vehicles Based on PSO-BP Optimized MPC
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Peilong Shi1, Hong Chang1, 2, Cairui Wang1, Qiang Ma1, Meng Zhou1
Affiliations
  • 1 Chang’an University, Xi’an 710064
  • 2 BYD Auto Co., Ltd., Xi’an 710119
Published: 2023-07-24 doi: 10.19620/j.cnki.1000-3703.20220941
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To solve the problem that the path tracking controller has a large tracking error under different road adhesion coefficients and vehicle speeds based on traditional Model Predictive Control (MPC), this paper proposed a path tracking control strategy for autonomous vehicles based on Particle Swarm Optimization (PSO)-BP neural network optimization MPC. Firstly, a path tracking controller based on MPC was designed; Secondly, PSO-BP was used to optimize MPC, and the controller accuracy and vehicle stability were taken as evaluation functions to obtain the offline optimal time domain parameters of PSO. Finally four conditions were selected for comparison and simulation verification of double shift lane tracking. The results show that the lateral control accuracy of double shift lane tracking under the 4 conditions, including low adhesion at low speed, high adhesion at low speed, high adhesion at high speed and medium adhesion at medium speed, is improved by 50%, 55%, 9% and 20% respectively.

Autonomous vehicle  /  Path tracking control  /  Model Predictive Control (MPC)  /  Particle Swarm Optimization (PSO)  /  Back Propagation (BP) neural network
Peilong Shi, Hong Chang, Cairui Wang, Qiang Ma, Meng Zhou. Research on Path Tracking Control of Autonomous Vehicles Based on PSO-BP Optimized MPC[J]. Automobile Technology, 2023 , (7) : 38 -46 . DOI: 10.19620/j.cnki.1000-3703.20220941
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doi: 10.19620/j.cnki.1000-3703.20220941
  • Online Date：2025-12-07
  • Published：2023-07-24
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  • Revised：2022-10-25
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    1 Chang’an University, Xi’an 710064
    2 BYD Auto Co., Ltd., Xi’an 710119
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