Trajectory Planning and Tracking for Four-Wheel Independent Drive Intelligent Vehicle Based on Model Predictive Control

Abstract

<div class="section abstract"><div class="htmlview paragraph">This paper proposes a dynamic obstacle avoidance system to help autonomous vehicles drive on high-speed structured roads. The system is mainly composed of trajectory planning and tracking controllers. The potential field (PF) model is introduced to establish a three-dimensional potential field for structured roads and obstacle vehicles. The trajectory planning problem that considers the vehicle’s and tires’ dynamics constraints is transformed into an optimization problem with muti-constraints by combining the model predictive control (MPC) algorithms. The trajectory tracking controller used in this paper is based on the 7 degrees of freedom (DOF) vehicle model and the UniTire tire model, which was discussed in detail in previous work [<span class="xref">25</span>, <span class="xref">26</span>]. The controller maintains good trajectory tracking performance even under extreme driving conditions, such as roads with poor adhesion conditions, where the car’s tires enter the nonlinear region easily. The innovation of this paper lies in introducing a high-fidelity vehicle model and a muti-conditions UniTire tire model with high precision obtained after parameter identification through tire test data. In addition, the nonlinear relationship between tire lateral force and slip angle is expressed as a linear function. This method updates the equivalent cornering stiffness in the linear function by solving the slope of the secant of the tire cornering characteristic curve at the current moment online, which solves the problem of high computational complexity caused by applying complex tire models in the control system. The co-simulation results of Simulink and CarSim show that the designed system has good dynamic obstacle avoidance and driving stability performance under driving conditions with high speed and poor road adhesion.</div></div>

Planning Speed Mode of All-Wheel Drive Autonomous Vehicles Considering Complete Constraint Set

“…For increasing performance and overcoming physical limitations, a technique can be represented by the augmented Lagrangian framework [6], which refers to iterative LQR (ILQR) and Constrained Iterative LQR (CILQR), respectively. In [12], the sliding mode control (SMC) calculates the total driving force for longitudinal control. In [7], the reference path is followed by low-level tracking using PID controllers.…”

“…In [14], the path-following control strategy was based on the linear quadratic regulator (LQR) to compare the performance of models. In [12], the MPC controller was used to calculate the steering wheel angle and the total yaw moment for lateral control, and the sliding mode control (SMC) was used to calculate the total driving force for longitudinal control.…”

“…The influence of tire nonlinearity on path offset was revealed. In [12], the longitudinal and lateral motion control for the fourwheel independent drive intelligent vehicle was designed: the upper layer was the motion controller, and the lower layer was the control distributor. In [8], complex dynamics in a predictive manner were applied to achieve optimal robot behavior in dynamic scenarios.…”