electronics Article Neural Network Based Robust Lateral Control for an Autonomous Vehicle Subrat Kumar Swain 1 , Jagat J. Rath 2 and Kalyana C. Veluvolu 3, *   Citation: Swain, S.K.; Rath, J.J.; Veluvolu, K.C. Neural Network Based Robust Lateral Control for an Autonomous Vehicle. Electronics 2021, 10, 510. https://doi.org/10.3390/ electronics10040510 Academic Editor: Arturo de la Escalera Hueso Received: 26 January 2021 Accepted: 16 February 2021 Published: 22 February 2021 Publisher’s Note: MDPI stays neu- tral with regard to jurisdictional clai- ms in published maps and institutio- nal affiliations. Copyright: © 2021 by the authors. Li- censee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and con- ditions of the Creative Commons At- tribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Graduate School of Electronics and Electrical Engineering, Kyungpook National University, Daegu 41566, Korea; skswain@knu.ac.kr 2 Department of Mechanical and Aero-Space Engineering, Institute of Infrastructure Technology Research and Management, Gujarat 380026, India; jagatjyoti.rath@gmail.com 3 School of Electronics Engineering, Kyungpook National University, Daegu 41566, Korea * Correspondence: veluvolu@ee.knu.ac.kr Abstract: The lateral motion of an Automated Vehicle (AV) is highly affected by the model’s un- certainties and unknown external disturbances during its navigation in adverse environmental conditions. Among the variety of controllers, the sliding mode controller (SMC), known for its ro- bustness 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 effec- tiveness 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. Keywords: Automated Vehicle; higher order sliding mode; radial basis function neural network; lane-keeping; lateral stability 1. 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. Driverless cars have proved to be a remarkable step towards automation, marking a paradigm shift from the manual driving (MD) scenario. MD is often prone to human- centric errors occurring due to the carelessness and inattentiveness of the driver leading to a risk for the individual and traffic safety [1]. Automated vehicles (AV) on the other hand, equipped with state-of-the-art sensors, are expected to reduce the driver burden along with ensuring driver comfort and vehicle safety [2–4]. AV have shown promising progress in path planning [5], path tracking [6–8] and decision making fields [9] that are crucial for autonomous driving. Lateral and longitudinal control are the two major areas regulating the overall motion of AV. Lane-keeping and lateral stability are the two primary objectives for the lateral control whereas the velocity control is the primary aspect for the longitudinal control of the vehicle. 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. In order to address the path tracking objective involving complex maneuvers, the design of a PID controller employing various design approaches to adjust the controller parameters was discussed in [14]. The lateral control of autonomous vehicles in the event of unknown road curvature using a nested PID steering control was proposed in [10]. However, the above studies did not take into account the impact of Electronics 2021, 10, 510. https://doi.org/10.3390/electronics10040510 https://www.mdpi.com/journal/electronics