To enhance intelligent vehicle path tracking accuracy and adaptability across different road conditions and speeds, this paper introduces a parameter-adaptive MPC method combined with a PSO-BP neural network. The MPC algorithm was formulated with path tracking accuracy and control increment as the key components of its cost function. The PSO-BP neural network was employed to dynamically adjust the weights of the MPC cost function in real time. The controller was implemented using a co-simulation framework built with CarSim and MATLAB/Simulink. Simulation results under different road adhesion levels and vehicle speeds demonstrated the proposed algorithm's effectiveness.
Intelligent Vehicle Path Tracking Control based on Adaptive Model Predictive Control
25.10.2024
1040490 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Adaptive Nonlinear Model Predictive Path Tracking Control for a Fixed-Wing Unmanned Aerial Vehicle
British Library Conference Proceedings | 2009
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