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| PSO-Based Parameter Adaptive MPC for Trajectory Tracking Control |
| LI Shihao,GAN Shukun*,LV Xuefei
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School of Mechanical and Electrical Engineering, Jilin Institute of Chemical Technology,
Jilin City 132022,China
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Abstract To address the challenges of low efficiency and significant errors in parameter tuning of prediction horizon for traditional model predictive controllers (MPC), this paper proposes a dynamic horizon adaptive MPC controller integrated with a particle swarm optimization (PSO) algorithm (PSO-MPC). The method establishes a PSO-MPC collaborative framework that dynamically searches for optimal prediction horizon parameters based on the vehicle’s real-time state within each control cycle, thereby constructing a time-varying prediction model to enhance trajectory tracking adaptability. The simulation results show that in the double lane change and lane change scenarios, compared with the MPC method with a fixed prediction time domain, the trajectory tracking error of the proposed controller is reduced by 35% and 37% respectively, verifying that the dynamic prediction time domain optimization mechanism can significantly improve the tracking accuracy and dynamic adaptability of complex trajectories, providing a new technical path for the design of autonomous driving control strategies.
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Published: 20 December 2025
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YAN Weifeng, GOU Yanfei, GAN Shukun, LV Xuefei. Robotic arm grasping pose detection based on RGB-D camera[J]. Journal of Jilin Institute of Chemical Technology, 2025, 42(5): 77-83. |
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WEN Hao , WANG Haibo , WANG Ronglin , LIU Chunjie . Multi-strategy Improved Subtraction Average-based Optimizer Algorithm[J]. Journal of Jilin Institute of Chemical Technology, 2025, 42(3): 30-37. |
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