Integrated Vehicle Controller for Path Tracking with Rollover Prevention of Autonomous Articulated Electric Vehicle Based on Model Predictive Control

This paper presents an integrated controller for an autonomous articulated electric vehicle (AAEV) for path tracking and rollover prevention. The AAEV is vulnerable to rollover due to the characteristics of the articulated frame steering (AFS) mechanism, which shows improved maneuverability and agility but not front wheel steering. In addition, the ratio between height and track width is high, so the AAEV is prone to rolling over. Therefore, the proposed controller was designed to achieve the two goals, following the reference path and managing the velocity to improve the safety of the AAEV. Vehicle behavior was modeled by a kinematic model with actuation delay. A local linearization was used to improve the accuracy of the vehicle model and reduce the computational load. Reference states of the position and heading were determined to follow the reference path and prevent the rollover. A model predictive control (MPC)-based reference state tracker was designed to optimize the articulation angle rate and longitudinal acceleration commands. The simulation study was conducted to evaluate the proposed algorithm with a comparison of the base algorithms. The reference path for the simulation was an S-shaped path with discontinuous curvature. Simulation results showed that the proposed algorithm reduces the path tracking error and load-transfer ratio.

Automated summary

This paper presents an integrated controller for an autonomous articulated electric vehicle (AAEV) to improve path tracking and prevent rollover. The AAEV, which has an articulated frame steering (AFS) mechanism, is susceptible to rollover due to its lack of front wheel steering and high height-to-track width ratio. The proposed controller aims to achieve path following and manage velocity to enhance the safety of the AAEV. A kinematic model with actuation delay is used to model the vehicle behavior, and a local linearization approach is employed to improve accuracy and reduce computation load. A model predictive control (MPC)-based reference state tracker is designed to optimize articulation angle rate and longitudinal acceleration commands. Simulation results demonstrate that the proposed algorithm reduces path tracking error and load-transfer ratio.