Robust steering control for trajectory following in road traffic environments

Abstract

The process of modelling vehicle motion in a road traffic environment requires the integration of trajectory generation with vehicle control. The steps involved here are generating a feasible trajectory based on the existing traffic and tracking the trajectory to control it with a steering angle input. Since the parameters of a physical system vary with changes in operating conditions, it is important to consider robustness when designing controllers. This article aims at developing a trajectory-following model with robust steering control strategies to accurately follow a generated trajectory. In this study, performance-based proportional, robust proportional and sliding mode control strategies are designed for trajectory following. The robustness of the proportional controller is established using Kharitonov’s theorem, which is compared with a proportional controller tuned for performance. Sliding mode control is designed for robustness and chattering elimination using two kinds of reaching laws – a constant reaching law and a novel power rate exponential reaching law. The controllers are designed using a dynamic bicycle model considering the error with respect to the trajectory. The controllers are then evaluated in IPG CarMaker®. The resulting trajectories and control inputs are compared for the considered control methodologies using the ISO double lane change and the Slalom tests. Sliding mode control with power rate exponential reaching law is concluded to be more robust as compared to the other controllers, with lower response times, up to 84% lower heading angle deviations from the trajectory and an overshoot of only 3.2% in lane changing.

Analysis and Modelling of Passing Sight Distance Using Vehicle Dynamic Response

“…Here, θ i is the initial heading angle which is the slope of the trajectory, (x p , y p ) is the final desired waypoint, and the heading angle at the desired waypoint is assumed to be zero [51].…”

Robust sliding mode prediction path tracking control for intelligent vehicle

“…In equation (12), the first term reflects the tracking accuracy of the sliding mode, and the second item of the formula reflects the amplitude of the system control input, which is mainly used to suppress the control quantity and prevent damage of the actuator caused by the drastic change. The cost function not only ensures that the future output of the system can track the target value well but also avoids drastic changes in the control input.…”

“…Soudbakhsh and Eskandarian 8 design a trajectory following controller based on LQR control, which can effectively avoid obstacles. The literature [9][10][11][12][13][14] adopt SMC to design trajectory tracking controllers, and the simulation results verify that the designed controller can make the vehicle track the given trajectory well while keeping stability. The literature [15][16][17] takes vehicle kinematics and dynamics constraints into consideration and uses an Model Predictive Control (MPC) method to implement the vehicle's path following control.…”