Path tracking control method of intelligent vehicle

Abstract

A path tracking control method of an intelligent vehicle comprises the following steps that 1) a two-freedom-degree planar movement model of the vehicle is established and simplified according to control demands to obtain a nonlinear vehicle path tracking control model; 2) an expected yaw angle function psid is constructed so that the yaw angle function psid satisfies that the lateral displacementdeviation Ye of the vehicle is converged to 0 when the yaw angle of the vehicle approaches an expected yaw angle; 3) a yaw angle tracking control problem is converted into a yaw angle calming problem; 4) a nonlinear control including an expected yaw angle module, an expanded state observer and a nonlinear error feedback control law of a non-singular terminal sliding-mode is designed; and 5) pathtracking control is carried out on the vehicle on the basis of the nonlinear controller.

Description

technical field [0001] The invention relates to a path tracking control method for an intelligent vehicle. Background technique [0002] With the development of robot technology and automobile technology, the research of smart car has become a hot spot. Smart vehicles have the advantages of predictable driving behavior, reducing traffic accidents and relieving traffic pressure, etc., and have broad application prospects in future intelligent transportation systems and military fields. As one of the key technologies of intelligent vehicles, the path tracking control of intelligent vehicles studies how to control the steering system of the vehicle under the premise of ensuring driving safety and riding comfort, so that the vehicle can drive along the desired route; The driving conditions of smart cars are complex and changeable, so how to design a path-following control system with good robustness is the focus of research. The highly dynamic nonlinear characteristics and coup...

AI Technical Summary

Problems solved by technology

Most of these control methods are based on precise mathematical models, but the mathematical models of vehicles in reality are highly nonlinear and uncertain

The neural network method in intelligent control requires a lot of offline calculation and training, and its portability is poor; the formulation of rules in fuzzy control depends on experience, and there are no qualitative rules for reference, so there is a certain degree of difficulty in engineering

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