Neural Network Based Robust Lateral Control for an Autonomous Vehicle

Subrat Kumar Swain,Kalyana C Veluvolu,Jagat J Rath

doi:10.3390/electronics10040510

Abstract

The lateral motion of an Automated Vehicle (AV) is highly affected by the model’s uncertainties and unknown external disturbances during its navigation in adverse environmental conditions. Among the variety of controllers, the sliding mode controller (SMC), known for its robustness towards disturbances, is considered to generate a robust control signal under uncertainties. However, conventional SMC suffers from the issue of high frequency oscillations, called chattering. To address the issue of chattering and reduce the effect of unknown external disturbances in the absence of precise model information, a radial basis function neural network (RBFNN) is employed to estimate the equivalent control. Further, a higher order sliding mode (HOSM) based switching control is proposed in this paper to compensate for the effect of external disturbances. The effectiveness of the proposed controller in terms of lane-keeping and lateral stability is demonstrated through simulation in a high-fidelity Carsim-Matlab Simulink environment under a variety of road and environmental conditions.

Highlights

The technological progress in the field of transportation has called for the need of a safe and hassle free driving experience in the presence of diverse, challenging environments
The tire cornering stiffness exhibits strong uncertainty under challenging driving conditions in the presence of unknown external disturbances. To deal with such types of conditions, this paper proposes a HOSMC based radial basis function neural network (RBFNN) to maintain lateral control and yaw stability of the vehicle
The present study is based on the estimation of the equivalent control by the proposed RBFNN on the account of unmeasured uncertainties and crosswinds as disturbances

Summary

Introduction

The technological progress in the field of transportation has called for the need of a safe and hassle free driving experience in the presence of diverse, challenging environments. For the lane-keeping purpose, the path following controllers designed using PID [10], sliding mode [8,11] and Model Predictive Control (MPC) [7,12,13] have been discussed in the literature. A nonlinear model predictive control (NMPC) approach for the purpose of yaw motion control by utilizing the C/GMRES algorithm for distributed drive electric vehicles was proposed in [15]. The issue of path following control by considering the vehicle constraints such as yaw-rate, steering angle, lateral position error and side-slip angle was addressed in [16]. A comparative study evaluating the performance of an unmanned surface vehicle (USV) in terms of station keeping heading and position using a nonlinear proportional derivative, sliding mode, and backstepping feedback controllers in the presence of wind disturbances was proposed in [19]. The type of autonomous vehicles taken into consideration in this paper was marine vehicles

Results

Discussion

Conclusion