Controller design for trajectory tracking of autonomous passenger vehicles

Abstract

This paper presents controllers design procedure for dynamic trajectory tracking of a highly automated vehicle. The main objective is to follow the planned trajectories generated by a co-pilot module in the safe way despite the presence of vehicle model uncertainties and also to guarantee a passenger comfort by generating soft actions on the steering wheel and accelerations. A decoupled design approach of longitudinal and lateral controller is adopted. For the longitudinal controller, a proportional including a feedforward terms is adopted. On the other hand, an adaptive backstepping approach is used in lateral case to deal with model nonlinearities and parameter uncertainties. The developed controller is integrated and tested in simulation environment. Performance of this controller are presented to demonstrate the effectiveness of the proposed controllers. NOMENCLATURE u/vLongitudinal/lateral vehicle speeds m Vehicle mass I z Inertia moment about the yaw axis through the vehicle center of gravity l f /l r Distances of the front and rear tires from vehicle's center of gravity c f /c r Cornering stiffness of the front and rear tires δ f Steering angle

Comparison of trajectory tracking controllers for autonomous vehicles

“…Control methods include sliding mode control [1], [2], [3], flatness-based control [4], [5], optimal linear-quadratic control [6], backstepping-based strategies [7], [8], [9], optimal preview control [10] and optimization-based methods like model predictive control (MPC) [11], [12].…”

A Waypoint Tracking Controller for Autonomous Road Vehicles Using ROS Framework

“…When high robustness to tracking disturbances is required, a good choice is using adaptive and intelligent control [36,37], although these methods usually take a lot of computational effort. On the other extreme, classical controllers [38,39] can be easily applied to steering actuation, but their adjustment may need complex derivations and selections. Optimal control [40,41] has demonstrated to be a suitable choice for robust applications and allows a very intuitive adjustment of its parameters through the use of cost functions.…”

Composite Nonlinear Feedback Control for Path Following of Four-Wheel Independently Actuated Autonomous Ground Vehicles

“…There are plentiful path following control strategies proposed for AGVs, such as linear matrix inequalities (LMI) [9], model predictive control (MPC) [10], optimal control [11], adaptive control [12], sliding mode control (SMC) [13], phase portrait analysis [14]. Nevertheless, few literatures dealt with the transient performance improvement problem in path following control.…”