In this paper, a new coupled lateral and longitudinal controller based on model predictive control (MPC) framework was proposed for an autonomous vehicle to track the desired trajectory and speed. Considering the constraints of control input limit and state output admissible, we used a spatial-based 8 degrees of freedom (DOF) vehicle model as the prediction model and used a high-fidelity model, i.e., a 14-DOF vehicle model as the plant model in the formulation of MPC algorithm. For the lateral control, the MPC controller generates the optimal road-wheel steering angle; for the longitudinal control, the PID controller embedded in the optimization solution generates the total driving or braking wheel torque. All these control inputs were passed to the plant simultaneously. The developed vehicle models were simulated with step steering input and compared with the simulation result of CarSim vehicle model for validation. We implemented the proposed controller for path tracking and speed control with MATLAB considering an 8-shaped curved trajectory as the reference. The simulation results showed that the path tracking and speed tracking performance were good using the combined lateral and longitudinal control strategy.

This paper presents a model predictive control (MPC) scheme for the stabilization of high-speed autonomous ground vehicles (AGVs) considering the effect of road topography. Accounting for the road curvature and bank angle, a single-track dynamic model with roll dynamics is derived. Variable time steps are utilized for vehicle model discretization, enabling collision avoidance in the long-term without compromising the prediction accuracy in the near-term. Accordingly, safe driving constraints including the sideslip envelope, zero-moment-point and lateral safety corridor are developed to handle stability and obstacle avoidance. Taking these constraints into account, an MPC problem is formulated and solved at each step to determine the optimal steering control commands. Moreover, feedback corrections are integrated into the MPC to compensate the unmodeled dynamics and parameter uncertainties. Comparative simulations validate the capability and real-time ability of the proposed control scheme.

Most controllers concerning lateral stability and rollover prevention for autonomous vehicles are designed separately and used simultaneously. However, roll motion influences lateral stability in cornering maneuvers, especially at high speed. Typical rollover prevention control stabilizes the vehicle with differential braking to create an understeering condition. Although this method can prevent rollover, it can also lead to deviation from a reference path specified for an autonomous vehicle. This contribution proposes and implements a coupled longitudinal and lateral controller for path tracking via model predictive control (MPC) to simultaneously enforce constraints on control input, state output, lateral stability, and rollover prevention. To demonstrate the approach in simulation, an 8 degrees of freedom (DOF) vehicle model is used as the MPC prediction model, and a high-fidelity 14-DOF model as the plant. The MPC-based lateral control generates a sequence of optimal steering angles, while a PID speed controller adjusts the driving or braking torque. The lateral stability envelope is determined by the phase plane of yaw rate and lateral velocity, while the roll angle threshold is derived from the load transfer ratio (LTR) and tire vertical force under the condition of quasi-steady-state rollover. To track the desired trajectory as fast as possible, a minimum-time velocity profile is determined using a forward-backward integration approach, subject to tire friction limit constraints. We demonstrate the approach in simulation, by having the vehicle track an arbitrary course of continuously varying curvature thus highlighting the accuracy of the controller and its ability to satisfy lateral and roll stability requirements. The MATLAB^® code for the 8-DOF and 14-DOF vehicle models, along with the implementation of the proposed controller are available as open source in the public domain.