Trajectory Tracking Control of Intelligent Electric Vehicles Based on the Adaptive Spiral Sliding Mode

Yanxin Nie,Minglu Zhang,Xiaojun Zhang

doi:10.3390/app112411739

Abstract

Aiming at the multi-objective control problem of the tracking effect and vehicle stability in the process of intelligent vehicle trajectory tracking, a coordinated control strategy of the trajectory tracking and stability of intelligent electric vehicles is proposed based on the hierarchical control theory. The vehicle dynamics model and trajectory tracking model are established. In order to tackle the chattering problem in the traditional sliding mode controller, an Adaptive Spiral Sliding Mode controller is designed by taking the derivative of the controller as the upper controller, which is intended to reduce the heading deviation and lateral deviation in the trajectory tracking process whilst ensuring the stability of the vehicle itself. In the lower controller, a four-wheel tire force optimal distribution method is designed. According to the requirements of the upper controller, combined with the yaw stability of the vehicle, the directional control distribution of the four-wheel tire force is realized. A joint simulation model was built based on CarSim and Simulink, and simulation experiments were performed. The results show that the proposed control strategy can effectively control the heading deviation and lateral deviation in the vehicle trajectory tracking while ensuring the lateral stability of the vehicle.

Highlights

Steven Wilkins and MohamedWith the increasing maturity of control technology and the continuous improvement of drivers’ requirements for safety, maneuverability and ride comfort, the intelligent research of vehicles has received extensive attention [1]
In addition to the proof of the stability, it was proven that the cost functions do not have local minima, such that the coefficients in the tracking-error learning control (TELC) algorithm guarantee that the global minimum is reached
In order to compare the effectiveness of the controller designed in the previous section, a joint simulation platform based on CarSim and Simulink was established, in which

Summary

Introduction

Steven Wilkins and MohamedWith the increasing maturity of control technology and the continuous improvement of drivers’ requirements for safety, maneuverability and ride comfort, the intelligent research of vehicles has received extensive attention [1]. Cai et al [2] considered the longitudinal, lateral, yaw, and quasi-static roll motion of intelligent vehicles, designed a trajectory tracking control system based on model predictive control, and proposed a vehicle active and safe steering control method. In [3], a tracking-error learning control (TELC) algorithm was presented. In the TELC algorithm, the feedforward control actions are updated by using the tracking error dynamics, and the plant–model mismatch problem is discarded. The experimental results show that the TELC algorithm results in better path-tracking performance than the traditional tracking error-based control method. In [4], a path-tracking method based on model predictive control with a variable predictive horizon is proposed. Based on the designed model’s predictive controller for path tracking, the response analysis of the path-tracking control

Methods

Results

Conclusion