Decoupling motion tracking control for 4WD autonomous vehicles based on the path correction

Abstract

Since one control loop input disturbs the control of another loop, the dynamic coupling of the longitudinal and lateral directions adversely affects the motion tracking accuracy of autonomous vehicles. With the ability to minimize the interactions between the longitudinal and lateral dynamics, the inverse system learned by the neural network is an effective way to decouple vehicle dynamics. After tracking the vehicle states projected from the desire motion, the dynamic decoupling and the motion tracking are both realized. However, the accumulation of vehicle state tracking errors causes the stable yaw tracking error and the lateral tracking divergence. To solve the accompanying problem, a path correction model is designed to periodically update the desired vehicle states. Moreover, the applicability of the inverse system decoupling method is improved in this paper, because the method usually adopted in distributed drive electric vehicles is applied to four-wheel driving vehicles representing the traditional driving form. Simulation results indicate that the decoupling motion tracking method with the path correction model is suitable for long-distance and complex conditions and has the highest comprehensive tracking accuracy compared with the integrated MPC (model predictive control) and the pure pursuit in the dynamic coupling conditions.

Model Predictive Control-Based Integrated Path Tracking and Velocity Control for Autonomous Vehicle with Four-Wheel Independent Steering and Driving

“…The control algorithm for the over-actuated vehicle with 4WIS/4WID is designed with MPC. MPC has the advantage that an optimization has been performed for each moment while keeping future behavior in the account [9,10,[48][49][50][51][52]59]. This advantage is realized by performing the optimization for a finite horizon and then optimizing repeatedly for every sampling time of the controller.…”

“…In this approach, MPC distributed the yaw moment and SMC calculated the longitudinal forces [50]. Decoupling motion tracker for 4WD was designed based on the inverse model learned by the neural network [51]. This method corrected the reference path to improve the yaw tracking and lateral tacking.…”