Xianchen Liu
ABSTRACT
An adaptive parametric model predictive control algorithm is proposed for heavy driverless vehicles. Based on the three-axis six-wheeled heavy vehicle dynamics model and MPC control algorithm to establish the controller, and supplement the vehicle dynamics constraints, output the optimal front wheel steering angle. On this basis, we analyzed the influence of the prediction time domain and the control time domain on the control index. In view of the problem of mismatch between the parameters in the MPC model and the actual needs, design a controller for solving the optimal control parameters based on the current driving speed of the vehicle in order to improve the accuracy of trajectory tracking. Carry out simulation experiments based on the Simulink and Trucksim joint simulation platform to compare the trajectory tracking effects of vehicles equipped with adaptive MPC controllers and non-adaptive MPC controllers. Simulation results show that the adaptive model predictive controller significantly improves the trajectory tracking accuracy of heavy vehicles.
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Index Terms
- Trajectory Tracking Control of Heavy Vehicles Based on Adaptive Model Predictive Control Algorithm
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