Steering control method based on vehicle kinematics model under dynamic constraints

Abstract

The invention discloses a steering control method based on vehicle kinematics model under dynamic constraints. The method comprises the following steps: firstly, kinematics modeling of a vehicle modelis performed, and then a kinematic differential equation of the vehicle is linearly discretized, because a model predictive control algorithm is generally a sampling control algorithm, and when a solution is optimized, a computer is also discrete; finally, an objective function and constraints are designed, and the objective function is transformed into a standard quadratic form to be solved. According to the method, dynamic constraint parameters are converted into a control quantity expression derived by using a kinematic model; compared with the existing method by using the kinematic modelas a prediction model, a controller design method considering the dynamic constraints proposed by the invention, can avoid the problem of high possibility of sideslip and rollover when the vehicle isrunning at a high speed, so that the tracking accuracy and the stability of the kinematic model can be ensured at the high speed.

Description

technical field [0001] The invention belongs to the technical field of automobile safety, in particular to a steering control method based on a vehicle kinematics model under dynamic constraints. Background technique [0002] The model describing the vehicle running state can be divided into kinematics model and dynamics model according to specific functions. Relevant research shows that at low speed, the vehicle kinematics characteristics are more prominent; at high speed, the vehicle dynamics characteristics are more prominent. In practical applications, the increase in model complexity usually does not lead to an increase in accuracy, on the contrary, it will lead to a decrease in the real-time performance of the algorithm. Establishing a reasonable vehicle system model is not only the premise of designing a model predictive controller, but also the realization of vehicle road conditions. The basis of the trace function. How to consider dynamic constraints on the basis o...

AI Technical Summary

Problems solved by technology

In practical applications, the increase in model complexity usually does not lead to an increase in accuracy, on the contrary, it will lead to a decrease in the real-time performance of the algorithm. Establishing a reasonable vehicle system model is not only the premise of designing a model predictive controller, but also the realization of vehicle road conditions. The basics of the trace function

Images