A lateral control method, system and vehicle for a vehicle

Abstract

The invention provides a lateral control method, system and vehicle for a vehicle, and relates to the field of vehicle electronic appliances. The control method includes collecting the current information of the vehicle, calculating the forward-sight distance according to the current speed information and the preset expected path curvature, determining the forward-sight point coordinates and the expected path according to the forward-sight distance, and calculating the lateral displacement deviation and direction of the vehicle according to the expected path Deviation, calculate Kp0, Ki0 and Kd0 according to the pose, look up the fuzzy rule table according to the pose and its rate of change to obtain the correction parameters △Kp, △Ki and △Kd, correct Kp0, Ki0 and Kd0 according to △Kp, △Ki and △Kd Get Kp, Ki and Kd, calculate the steering wheel angle of the vehicle according to the pose e and its rate of change de, Kp, Ki and Kd, and control the steering of the vehicle according to the steering wheel angle. The lateral control method for the vehicle provided by the present invention has high robustness.

Description

technical field [0001] The invention relates to the field of vehicle electronic appliances, in particular to a lateral control method, system and vehicle for vehicles. Background technique [0002] In intelligent vehicle control, lateral control refers to controlling the vehicle to automatically track the driving route and maintain certain comfort and stability requirements under different conditions such as vehicle speed, load, road conditions, and wind resistance. In fact, it is vehicle steering control. [0003] Compared with general outdoor wheeled mobile robots, intelligent vehicles have higher longitudinal speed and larger fluctuation ranges of speed, load, and tire cornering stiffness, so lateral control is more difficult. [0004] The basic design idea of ​​the current lateral control is: the method of designing the controller based on the mathematical model of the vehicle and using the principle of automatic control. That is, the lateral control model of the vehicl...

AI Technical Summary

Problems solved by technology

The advantage of the traditional PID algorithm is that it does not require accurate controlled objects, but it is quite cumbersome to adjust Kp, Ki, and Kd parameters, and is particularly affected by factors such as speed and road curvature, and its robustness is not high; while based on The control accuracy of the model predictive control algorithm is more accurate, but for the general vehicle model, due to the ever-changing factors such as the actual load of the vehicle and road conditions, it is difficult to accurately determine the parameters in the model such as the moment of inertia of the vehicle body and the cornering stiffness of the tire. Therefore, The accuracy of the prediction model is not high, resulting in insufficient accuracy of the control algorithm, which is difficult to apply to the application of real vehicles

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