Path tracking control of multi-axle vehicles using a neural network controller based on multiple look-ahead points

Abstract

Enhancing the operational speed and control accuracy of autonomous transport vehicles is crucial for meeting the efficiency and safety demands in cargo transportation. Although Nonlinear Model Predictive Control (NMPC), based on vehicle dynamics models and multi-point look-ahead rolling optimization, offers high precision, it suffers from poor real-time performance, making it unsuitable for medium- to high-speed conditions. Compared to two-axle vehicles, multi-axle vehicles have more complex dynamics models and constraints, which increase the computational burden of NMPC. To address these issues, a neural network-based trajectory tracking controller for multi-axle vehicles under medium- to high-speed conditions has been proposed, using NMPC as the training sample generator. The learning samples were generated by NMPC based on the dynamics and multi-point look-ahead rolling optimization of multi-axle vehicles. Additionally, to prevent the failure of the network controller due to vehicle position information deviating from the sample space under the presence of positioning errors, a sample fusion method was employed to enhance the network controller’s robustness to localization disturbances. The neural network controller was obtained through offline training and validated using a MATLAB/Simulink-TruckSim co-simulation platform, where it was compared with other controllers. The simulation results indicated that the control accuracy of the neural network controller is very close to that of NMPC, with a nearly twofold improvement in real-time performance.