Cooperative optimization of vehicle lateral and longitudinal trajectory tracking based on LPOA-MPC

Abstract:

Aiming at the problem that it is difficult to select the weight matrix parameters in the vehicle model predictive control(MPC) trajectory tracking controller, which makes the stability and accuracy of the vehicle trajectory tracking control insufficient, this research propose a latin-pelican algorithm(LPOA) that integrates multiple mechanisms to optimize the weight matrix parameters of the vehicle lateral and longitudinal joint model predictive trajectory tracking controller. Firstly, the vehicle transverse MPC controller, the longitudinal MPC upper controller and the lower controller based on the acceleration-drive inverse dynamics model are designed respectively based on the vehicle single-track model;Secondly, a Latin Pelican Optimization Algorithm is proposed to improve the efficiency of the pelican algorithms searching in the solution space. The hierarchical hunting mechanism of the gray wolf algorithm is introduced to reconfigure the prey localization model of the POA, and the convergence speed of the algorithm is improved by the α-pelican guidance strategy. Thus, a dynamic stochastic search strategy is incorporated to enhance the algorithms ability to escape from local extremes in the late iteration by using its heavy-tailed distribution characteristics. Finally, the parameters of the horizontal and vertical MPC controller weight matrices are optimized using the optimization capability of LPOA; and the proposed horizontal, vertical, and horizontal-longitudinal joint optimization control methods are verified through co-simulation on CarSim and Simulink platforms. Results show that the LPOA-MPC controller proposed in this research can effectively improve the stability and accuracy of vehicle trajectory tracking control in horizontal, longitudinal and transverse-longitudinal joint control.