Improved adaptive lane‐keeping control for four‐wheel steering vehicles without lateral velocity measurements

Abstract

Summary Previously, we developed an adaptive lane‐keeping controller for vehicles without using lateral velocity measurements. However, the construction of the controller is very complex and not suitable for practical applications. To overcome this problem, we propose an improved adaptive lane‐keeping controller that is much simpler than the conventional controller. To obtain the improved controller, we propose an improved vehicle dynamic expression. In this dynamic expression, fewer elements are estimated adaptively as containing unknown vehicle parameters compared with the conventional expression. Moreover, a control performance improvement method is described theoretically. Finally, we report numerous simulations that validated the effectiveness of the improved controller. Copyright © 2017 John Wiley & Sons, Ltd.

Model Predictive Control-Based Integrated Path Tracking and Velocity Control for Autonomous Vehicle with Four-Wheel Independent Steering and Driving

“…Under the assumption of small body slips, the kinematic model can be used to propagate the vehicle states. In addition, the lateral velocity is excluded from the state variable because of small body slip [39]. Based on the kinematic model, the prediction model is defined as (12).…”

“…In particular, small steering angle and constant velocity assumptions were frequently made to linearize a trigonometric function for steering inputs and to ensure the denominator divided by the velocity is constant [19,23,24]. In order to avoid the performance deterioration caused by the model linearization, the nonlinear control method has been introduced in designing the path and velocity tracking controllers [6][7][8][39][40][41][42][43][44][45]. Among the approaches using the non-linear control, a nonlinear MPC (NMPC) has been used to directly utilize the non-linear vehicle model.…”

“…In addition, LQR and H∞ controllers were adopted to determine the optimal state feedback gain [5,6,38]. Adaptive control was also utilized to compensate for the parametric complexity of the 4WS system, which has more parameter than the FWS vehicle [39,40]. An adaptive MPC scheme was developed based on an autoregressive with exogeneous input model to improve the yaw-motion stability [41].…”

“…This approach required the assumptions on the reference path in order to derive the derivatives of the lateral and heading error, which restricts the road conditions where performance can be achieved. In addition, the 4WIS was frequently regarded as a 4WS system, so control inputs were the front and rear steering angles, which were equally applied to the left and right-side wheels [4][5][6]8,36,[38][39][40][41][42][43][44][45]. Furthermore, the effect of the four-wheel independently driving (4WID) on the yaw motion of the vehicle was not considered when designing the path tracking controller.…”

Advanced Estimation Techniques for Vehicle System Dynamic State: A Survey

“…To improve ground vehicle handling stability and passenger safety, a large number of advanced vehicle-active safety dynamic control systems, such as the direct yaw control system (DYC) [1,2,3,4], anti-lock braking systems (ABS) [5,6], four-wheel steering system (4WS) [7,8], active front steering system (AFS) [2,9], active suspension system (ASS) [9,10], adaptive cruise control (ACC) [11], collision avoidance system (CAS) [12], and other advanced driver assistance systems (ADAS) towards a connected and automated driving vehicle have been developed and brought into the market in recent years [13,14,15].…”