Automobile stability control method based on tire non-linear features

Abstract

The present invention relates to an automobile stability control method based on tire non-linear features. The devices used in the method comprise a reference model, a tire side force and cornering stiffness processor, an MPC (Model Predictive Control) controller, a brake distribution module and a Carsim automatic model. The reference model is configured to determine an expected automobile yaw velocity and an expected automobile side slip angle; the tire side force and cornering stiffness processor is configured to determine the side slip angle, the side force and the cornering stiffness of the tire; the Carsim automatic model is configured to output actual motion state information of the automobile; the MPC controller selects a prediction model according to the cornering stiffness of thetire and performs optimization to obtain a front-wheel extraordinary angle and a compensation yawing moment of the automobile; the front-wheel extraordinary angle and a front-wheel rotating angle generated by a driver's steering input are superposed and then are directly output to the Carsim automatic model, the compensation yawing moment is output to the brake distribution module, the brake distribution module is employed to determine brake torques of four wheels and output the brake torques of the four wheels to the Carsim automatic model so as to achieve stability control.

Description

technical field [0001] The invention relates to the field of automobile stability control, in particular to an automobile stability control method based on tire nonlinear characteristics. Background technique [0002] With the continuous development of vehicle chassis dynamics control, integrated control has become the direction of future development, and the combination of active front wheel steering and direct yaw moment to achieve vehicle stability has been widely studied. At present, the control methods involved in the field of vehicle stability control mainly include robust control, neural network control, and model predictive control (Model Predictive Control, MPC), among which model predictive control can better handle multi-objective tasks and system constraints. , has been widely used in the field of vehicle stability control. [0003] According to different prediction models and optimization methods used, MPC can be divided into linear MPC and nonlinear MPC. Line...

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Problems solved by technology

[0004] In order to solve the problem that the existing linear MPC control method cannot fully characterize the nonlinear characteristics of the tire, the controller has a narrow stable domain and low control precision.

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