Journal
SENSORS
Volume 23, Issue 1, Pages -Publisher
MDPI
DOI: 10.3390/s23010412
Keywords
autonomous vehicles; path tracking control; particle swarm optimization; model predictive control
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This paper develops a weight adaptive model prediction control system (AMPC) based on PSO-BP neural network to improve the tracking adaptability of autonomous vehicles under different vehicle speeds and road curvature. The system consists of a dynamics-based model prediction controller (MPC) and an optimal weight adaptive regulator. The control strategy was verified on a simulation platform and an autonomous vehicle test platform, showing better tracking adaptation capability and improved tracking accuracy.
In order to improve the tracking adaptability of autonomous vehicles under different vehicle speeds and road curvature, this paper develops a weight adaptive model prediction control system (AMPC) based on PSO-BP neural network, which consists of a dynamics-based model prediction controller (MPC) and an optimal weight adaptive regulator. Based on the application of MPC to achieve high-precision tracking control, the optimal weight under different operating conditions obtained by automated simulation is used to train the PSO-BP neural network offline to achieve online adjustment of MPC weight. The validation results of the Prescan-Carsim-Simulink joint simulation platform show that the adaptive control system has better tracking adaptation capability compared with the original classical MPC control. The control strategy was also verified on an autonomous vehicle test platform, and the test results showed that the adaptive control strategy improved tracking accuracy while meeting the vehicle's requirements for real-time control and lateral stability.
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