Yaw stability control method for representing nonlinear characteristics of automobile

Abstract

Disclosed is a yaw stability control method for representing nonlinear characteristics of an automobile. The method is characterized in that a reference model, a tire lateral force and cornering stiffness processor, an MPC and a Carsim automobile model. The reference model is used for determining the expected yaw velocity of the automobile; the tire lateral force and cornering stiffness processoris used for determining the side slip angle, lateral force and cornering stiffness of a tire; the Carsim automobile model is used for outputting actual motion state information of the automobile, andthe information comprises the longitudinal velocity, yaw velocity, centroid slip angle and road adhesion coefficient of the automobile; the MPC selects a prediction model according to the cornering stiffness of the tire, combines the expected yaw velocity of the automobile and the actual motion state information of the automobile to optimally calculate the front-wheel additional rotation angle ofthe automobile, superimposes the front-wheel additional rotation angle with a front-wheel rotation angle generated according to steering input of a driver and outputs a superimposition result to the Carsim automobile model to achieve yaw stability control over the automobile.

Description

technical field [0001] The invention relates to the field of vehicle yaw stability control, in particular to a yaw stability control method reflecting the nonlinear characteristics of the vehicle. Background technique [0002] As people pay more and more attention to the driving safety of automobiles, the active safety system of automobiles has been developed rapidly, among which Active Front Steering (AFS) technology is widely used as an effective yaw stability control system. At present, the control methods adopted by AFS mainly include PID control, sliding mode variable structure control and Model Predictive Control (MPC), among which MPC can better handle multi-objective tasks and system constraints. The field of stability control has been widely used. [0003] According to the different prediction models and optimization methods used, MPC can be divided into linear MPC and nonlinear MPC. Linear MPC is widely used because of its low computational burden and fast calcul...

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Problems solved by technology

[0004] In order to solve the problem that the existing linear MPC method cannot fully characterize the nonlinear dynamic characteristics of the vehicle, which leads to low control accuracy and narrow stability domain of the controller

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