Application of asymmetric barrier Lyapunov function using ADRC approach for development of autonomous vehicle lateral control

Abstract

This paper presents a control scheme for performance improvement in lane-keeping activity of autonomous vehicle, within guaranteed specified deviations. Such feature will provide better and safe maneuvering for autonomous vehicle. The proposed control scheme consists of Asymmetric Barrier Lyapunov Function (ABLF) based backstepping method for ensuring stability and constraints satisfaction. Since autonomous vehicle dynamics is subjected to unknown disturbances and also the states, it has been shown that the vehicle performance can be improved by the application of Active Disturbance Rejection Control (ADRC) which uses the Extended State Observer (ESO). For the proposed control scheme, the asymptotic stability and convergence of error dynamics have been established. The simulation results have been presented to illustrate the performance robustness and accuracy provided by the proposed control scheme. Further, it has been show that the vehicle motion performance is better in comparison to the performances obtained from two existing control methods.

Optimal robust path tracking control for multi-constrained underactuated vehicles based on uncertainty orthogonal decomposition

“…Autonomous driving can significantly improve traffic efficiency and driving safety, has become the focus of research in industry and academia (Parkash and Swarup (2022)), and is the development trend and ultimate goal of intelligent vehicles. Path tracking not only frees the driver from arduous and complex driving tasks, but also improves vehicle stability and maneuverability (Lee et al (2019)).…”