Abstract
The preview model is one of the common methods used in trajectory tracking. The traditional fixed preview time is not adaptable to most speeds and road conditions, which not only reduces the tracking accuracy but also reduces the vehicle stability. Therefore, a controller can be designed to determine the adaptive preview time based on an optimization function of the lateral deviation, the road boundary, and the road boundary of the whole vehicle motion response characteristics. Traditional optimal preview control theory predicts the next state of the vehicle by the assumption of constant transverse pendulum angular velocity. In this paper, an expectation-based approach is used to find the ideal steering wheel turning angle based on the adaptive preview time, and a single-point preview model is established. Based on the two-degree-of-freedom dynamics model, a sliding mode controller is designed for control, and the low-pass filters are designed to suppress jitter in the sliding mode controller. Simulation results with different preview times, different speeds and different road adhesion coefficients prove that the controller has a good control effect and has good effectiveness and adaptability to speed and adhesion coefficient.
Highlights
The autonomous vehicle is a new future concept that fulfills people’s vision of the future
By combining By thecombining advantages ofadvantages sliding mode controlmode and the preview control algo-control a the of sliding control and the preview rithm, a slidingrithm, modeacontroller based on preview control is designed, which can reflect sliding mode controller based on preview control is designed, which can ref both the driver’s operating characteristics and boundaryand constraints, well as theasmotion both the driver’s operating characteristics boundaryasconstraints, well as the mo response characteristics of the whole vehicle
Function, the sampling period used in the simulation tests is 0.001 s, and the designed sliding mode controller is added to the model as follows in Figure 9: adaptive preview distance
Summary
The autonomous vehicle is a new future concept that fulfills people’s vision of the future. The current research in the field of vehicle control focuses on establishing an efficient and reasonable lateral stability control strategy [3], and the main lateral control algorithms include classical PID (Proportional Integral Derivative) control methods [4], optimal pre‐. The literature uses lane line detection techniques combined with model predictive control design controllers [12]; uses particle swarms to optimize higher‐order sliding mode con‐. Zhang et al [15] designed a signed a path control trackingstrategy control strategy withrobustness strong robustness and without chattering path tracking with strong and without chattering basedbased on a on a sliding mode technique with conditional integrators. The model equations for the two degrees of freedom of the vehicle with respect to the transverse pendulum angle ω and the lateral declination angle β of the center of mass can be obtained by Equations (1), (2) and (4):
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