Multipoint pre-aiming LQR transverse control method based on Fiala brush tire model

Abstract

The invention discloses a multipoint pre-aiming LQR transverse control method based on a Fiala brush tire model. The method comprises the steps that 1, establishing a vehicle two-degree-of-freedom kinetic model and a tracking error model based on a vehicle striking center; 2, establishing a state space equation of the system; 3, combining the path curvature serving as disturbance quantity with a state vector of the system to construct an augmented LQR control problem; 4, solving a Riccati equation of the augmented LQR system to obtain an optimal feedforward control quantity and an optimal feedback control quantity; and 5, utilizing the obtained optimal control quantity, namely the front wheel lateral deviation force. According to the invention, curvature information of a plurality of roadpoints can be pre-aimed through the LQR controller in a limited time domain; in addition, the front wheel lateral deviation force can be restrained through the front wheel rotating angle under the condition that the vehicle has large transverse acceleration, so that the vehicle can keep good transverse stability and path tracking precision, and the situation that the vehicle is unstable due to tire force saturation under the limit working condition is avoided.

Description

technical field [0001] The technical field of intelligent vehicle lateral control of the present invention, in particular relates to a multi-point preview LQR lateral control method based on a Fiala brush tire model. Background technique [0002] In recent years, the number of automobiles has been increasing, the technical capabilities of transportation have been continuously improved, and the demand for intelligent driving technology of automobiles is also increasing. Based on the actual needs, research and exploration of related technologies for intelligent vehicle motion control have been carried out. Intelligent vehicle motion control technology can be divided into two types: lateral control and longitudinal control according to different control objectives. Among them, lateral control technology is one of the key technologies to realize autonomous driving of intelligent vehicles. The lateral control of intelligent vehicles is to make the vehicle drive along the desired...

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Problems solved by technology

[0003] Some existing lateral control algorithms, such as PID, model predictive control, optimal control and other control algorithms, are basically improving the error accuracy of path tracking, but in real road conditions, vehicles often deviate from the expected path due to some unexpected situations , and when the vehicle speed is too high, the wheel cornering force will be close to saturation so that the vehicle will skid and the vehicle will be prone to instability

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