Adaptive trajectory tracking control strategy of intelligent vehicle

Abstract

The trajectory tracking control strategy for intelligent vehicle is proposed in this article. Considering the parameters perturbations and external disturbances of the vehicle system, based on the vehicle dynamics and the preview follower theory, the lateral preview deviation dynamics model of the vehicle system is established which uses lateral preview position deviation, lateral preview velocity deviation, lateral preview attitude angle deviation, and lateral preview attitude angle velocity deviation as the tracking state variables. For this uncertain system, the adaptive sliding mode control algorithm is adopted to design the preview controller to eliminate the effects of uncertainties and realize high accuracy of the target trajectory tracking. According to the real-time deviations of lateral position and lateral attitude angle, the feedback controller is designed based on the fuzzy control algorithm. For improving the adaptability to the multiple dynamic states, the extension theory is introduced to design the coordination controller to adjusting the control proportions of the preview controller and the feedback controller to the front wheel steering angle. Simulation results verify the adaptability, robustness, accuracy of the control strategy under which the intelligent vehicle has good handling stability.

Research on Trajectory Tracking of Sliding Mode Control Based on Adaptive Preview Time

“…In this Figure : a is the distance from the center of mass to the front axle; b is the distance from the center of mass to the rear axle; F yf is the lateral force of the front wheel; F yr is the lateral force of the rear wheel; v x is the longitudinal speed; v y is the lateral speed; ω is the angular velocity of transverse pendulum; β is the lateral deflection angle of the center of mass. 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 preview control methods [5,6], robust control [7], sliding mode control methods [8], modern control algorithm MPC (Model Predictive Control) methods [9,10], fuzzy control methods [11], and so on, and the optimization strategies of various methods are innumerable. The literature uses lane line detection techniques combined with model predictive control to design controllers [12]; uses particle swarms to optimize higher-order sliding mode control parameters [13]; and designs controllers based on adaptive preview with directional error compensation [14].…”

Navigation Path Extraction and Experimental Research of Pusher Robot Based on Binocular Vision

“…The lateral deviation and angular deviation of the robot was obtained through the fitting of the cowshed fence, the extraction of the operation route and the determination of the robot's pose state [19]. The motion deviation of the pusher robot is shown in Figure 3.…”

Trajectory tracking control for four-wheel independent drive intelligent vehicle based on model predictive control and sliding mode control

“…Traditional vehicle emissions can also cause environmental pollution. 1 However, the intelligence of automobiles can solve this problem well. They can be widely used in transportation, agriculture, planetary exploration, military, and other aspects.…”

“…9 Zhang et al 10 proposed a robust control scheme that combines backstepping method, neural network, and SMC to achieve underactuated vehicle trajectory tracking under parameter uncertainty and external interference. Shuo Zhang et al 1 proposed a preview controller based on adaptive SMC and a feedback controller based on fuzzy control. The extension theory is introduced to adjust the control ratio of the two controllers, which improves the adaptability and robustness of the algorithm.…”